2023/7/20

CAN-LOC: spoofing detection and physical intrusion localization on an in-vehicle CAN bus based on deep features of voltage signals

Efrat Levy, Asaf Shabtai, Bogdan Groza, Pal-Stefan Murvay, Yuval Elovici

IEEE Transactions on Information Forensics and Security, 2023

2023/7/20

CAN-LOC: spoofing detection and physical intrusion localization on an in-vehicle CAN bus based on deep features of voltage signals

Efrat Levy, Asaf Shabtai, Bogdan Groza, Pal-Stefan Murvay, Yuval Elovici

IEEE Transactions on Information Forensics and Security, 2023

The Controller Area Network (CAN), which is used for communication between in-vehicle devices, has been shown to be vulnerable to spoofing attacks. Voltage-based spoofing detection (VBS-D) mechanisms are considered state-of-the-art solutions, complementing cryptography-based authentication whose security is limited due to the CAN protocol’s limited message size. Unfortunately, VBS-D mechanisms are vulnerable to poisoning performed by a malicious device connected to the CAN bus, specifically designed to poison the deployed VBS-D mechanism as it adapts to environmental changes that take place when the vehicle is moving. In this paper, we harden VBS-D mechanisms using a deep learning-based mechanism which runs immediately, when the vehicle starts; this mechanism utilizes physical side-channels to detect and locate physical intrusions, even when the malicious devices connected to the …

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2023/6/14

X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail

Omer Hofman, Amit Giloni, Yarin Hayun, Ikuya Morikawa, Toshiya Shimizu, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2306.08422, 2023

2023/6/14

X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail

Omer Hofman, Amit Giloni, Yarin Hayun, Ikuya Morikawa, Toshiya Shimizu, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2306.08422, 2023

Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversarial attacks on object detectors have had difficulty detecting new real-life attacks. We present X-Detect, a novel adversarial patch detector that can: i) detect adversarial samples in real time, allowing the defender to take preventive action; ii) provide explanations for the alerts raised to support the defender’s decision-making process, and iii) handle unfamiliar threats in the form of new attacks. Given a new scene, X-Detect uses an ensemble of explainable-by-design detectors that utilize object extraction, scene manipulation, and feature transformation techniques to determine whether an alert needs to be raised. X-Detect was evaluated in both the physical and digital space using five different attack scenarios (including adaptive attacks) and the COCO dataset and our new Superstore dataset. The physical evaluation was performed using a smart shopping cart setup in real-world settings and included 17 adversarial patch attacks recorded in 1,700 adversarial videos. The results showed that X-Detect outperforms the state-of-the-art methods in distinguishing between benign and adversarial scenes for all attack scenarios while maintaining a 0% FPR (no false alarms) and providing actionable explanations for the alerts raised. A demo is available.

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2023/6/4

Discussion Paper: The Threat of Real Time Deepfakes

Guy Frankovits, Yisroel Mirsky

arXiv preprint arXiv:2306.02487, 2023

2023/6/4

Discussion Paper: The Threat of Real Time Deepfakes

Guy Frankovits, Yisroel Mirsky

arXiv preprint arXiv:2306.02487, 2023

Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These “deepfakes” are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and today attackers have the ability to generate deepfakes in real-time. This new capability poses a significant threat to society as attackers begin to exploit the technology in advances social engineering attacks. In this paper, we discuss the implications of this emerging threat, identify the challenges with preventing these attacks and suggest a better direction for researching stronger defences.

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2023/5/3

IPatch: a remote adversarial patch

Yisroel Mirsky

Cybersecurity 6 (1), 18, 2023

2023/5/3

IPatch: a remote adversarial patch

Yisroel Mirsky

Cybersecurity 6 (1), 18, 2023

Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single frame. In the past, it has been shown how an attacker can fool these models by placing an adversarial patch within a scene. However, these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the image. In this paper, we introduce a new type of adversarial patch which alters a model’s perception of an image’s semantics. These patches can be placed anywhere within an image to change the classification or semantics of locations far from the patch. We call this new class of adversarial examples ‘remote adversarial patches’ (RAP). We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight …

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2023/4/1

Enabling object detectors to better distinguish between real and fake objects in semi-autonomous and fully autonomous vehicles. Protecting Autonomous Cars from Phantom Attacks

Ben Nassi, Yisroel Mirsky, Jacob Shams, Raz Ben-Netanel, Dudi Nassi, Yuval Elovici

COMMUNICATIONS OF THE ACM 66 (4), 56-67, 2023

2023/4/1

Enabling object detectors to better distinguish between real and fake objects in semi-autonomous and fully autonomous vehicles. Protecting Autonomous Cars from Phantom Attacks

Ben Nassi, Yisroel Mirsky, Jacob Shams, Raz Ben-Netanel, Dudi Nassi, Yuval Elovici

COMMUNICATIONS OF THE ACM 66 (4), 56-67, 2023

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2023/3/23

Protecting Autonomous Cars from Phantom Attacks

Ben Nassi, Yisroel Mirsky, Jacob Shams, Raz Ben-Netanel, Dudi Nassi, Yuval Elovici

Communications of the ACM 66 (4), 56-69, 2023

2023/3/23

Protecting Autonomous Cars from Phantom Attacks

Ben Nassi, Yisroel Mirsky, Jacob Shams, Raz Ben-Netanel, Dudi Nassi, Yuval Elovici

Communications of the ACM 66 (4), 56-69, 2023

Enabling object detectors to better distinguish between real and fake objects in semi-autonomous and fully autonomous vehicles.

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2023/3/15

A survey of MulVAL extensions and their attack scenarios coverage

David Tayouri, Nick Baum, Asaf Shabtai, Rami Puzis

IEEE Access, 2023

2023/3/15

A survey of MulVAL extensions and their attack scenarios coverage

David Tayouri, Nick Baum, Asaf Shabtai, Rami Puzis

IEEE Access, 2023

Organizations employ various adversary models to assess the risk and potential impact of attacks on their networks. A popular method of visually representing cyber risks is the attack graph. Attack graphs represent vulnerabilities and actions an attacker can take to identify and compromise an organization’s assets. Attack graphs facilitate the visual presentation and algorithmic analysis of attack scenarios in the form of attack paths. MulVAL is a generic open-source framework for constructing logical attack graphs, which has been widely used by researchers and practitioners and extended by them with additional attack scenarios. This paper surveys all of the existing MulVAL extensions and maps all MulVAL interaction rules to MITRE ATT&CK Techniques to estimate their attack scenarios coverage. This survey aligns current MulVAL extensions along unified ontological concepts and highlights the existing gaps. It …

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2023/3/1

D-Score: An expert-based method for assessing the detectability of IoT-related cyber-attacks

Yair Meidan, Daniel Benatar, Ron Bitton, Dan Avraham, Asaf Shabtai

Computers & Security 126, 103073, 2023

2023/3/1

D-Score: An expert-based method for assessing the detectability of IoT-related cyber-attacks

Yair Meidan, Daniel Benatar, Ron Bitton, Dan Avraham, Asaf Shabtai

Computers & Security 126, 103073, 2023

IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detectability and devise a risk assessment method capable of addressing a key question: how easy is it for an anomaly-based network intrusion detection system to detect a given cyber-attack involving a specific IoT model? In the process of addressing this question we (a) investigate the predictability of IoT network traffic, (b) present a novel …

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2023/1/13

Evaluating the Cybersecurity Risk of Real-world, Machine Learning Production Systems

Ron Bitton, Nadav Maman, Inderjeet Singh, Satoru Momiyama, Yuval Elovici, Asaf Shabtai

ACM Computing Surveys 55 (9), 1-36, 2023

2023/1/13

Evaluating the Cybersecurity Risk of Real-world, Machine Learning Production Systems

Ron Bitton, Nadav Maman, Inderjeet Singh, Satoru Momiyama, Yuval Elovici, Asaf Shabtai

ACM Computing Surveys 55 (9), 1-36, 2023

Although cyberattacks on machine learning (ML) production systems can be harmful, today, security practitioners are ill-equipped, lacking methodologies and tactical tools that would allow them to analyze the security risks of their ML-based systems. In this article, we perform a comprehensive threat analysis of ML production systems. In this analysis, we follow the ontology presented by NIST for evaluating enterprise network security risk and apply it to ML-based production systems. Specifically, we (1) enumerate the assets of a typical ML production system, (2) describe the threat model (i.e., potential adversaries, their capabilities, and their main goal), (3) identify the various threats to ML systems, and (4) review a large number of attacks, demonstrated in previous studies, which can realize these threats. To quantify the risk posed by adversarial machine learning (AML) threat, we introduce a novel scoring system that …

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2023/1/8

Deepfake CAPTCHA: A Method for Preventing Fake Calls

Lior Yasur, Guy Frankovits, Fred M Grabovski, Yisroel Mirsky

arXiv preprint arXiv:2301.03064, 2023

2023/1/8

Deepfake CAPTCHA: A Method for Preventing Fake Calls

Lior Yasur, Guy Frankovits, Fred M Grabovski, Yisroel Mirsky

arXiv preprint arXiv:2301.03064, 2023

Deep learning technology has made it possible to generate realistic content of specific individuals. These `deepfakes’ can now be generated in real-time which enables attackers to impersonate people over audio and video calls. Moreover, some methods only need a few images or seconds of audio to steal an identity. Existing defenses perform passive analysis to detect fake content. However, with the rapid progress of deepfake quality, this may be a losing game. In this paper, we propose D-CAPTCHA: an active defense against real-time deepfakes. The approach is to force the adversary into the spotlight by challenging the deepfake model to generate content which exceeds its capabilities. By doing so, passive detection becomes easier since the content will be distorted. In contrast to existing CAPTCHAs, we challenge the AI’s ability to create content as opposed to its ability to classify content. In this work we focus on real-time audio deepfakes and present preliminary results on video. In our evaluation we found that D-CAPTCHA outperforms state-of-the-art audio deepfake detectors with an accuracy of 91-100% depending on the challenge (compared to 71% without challenges). We also performed a study on 41 volunteers to understand how threatening current real-time deepfake attacks are. We found that the majority of the volunteers could not tell the difference between real and fake audio.

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2023

Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors

Avishag Shapira, Alon Zolfi, Luca Demetrio, Battista Biggio, Asaf Shabtai

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023

2023

Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors

Avishag Shapira, Alon Zolfi, Luca Demetrio, Battista Biggio, Asaf Shabtai

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023

Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years. Most of the attacks proposed have targeted the model’s integrity (ie, caused the model to make incorrect predictions), while adversarial attacks targeting the model’s availability, a critical aspect in safety-critical domains such as autonomous driving, have not yet been explored by the machine learning research community. In this paper, we propose a novel attack that negatively affects the decision latency of an end-to-end object detection pipeline. We craft a universal adversarial perturbation (UAP) that targets a widely used technique integrated in many object detector pipelines-non-maximum suppression (NMS). Our experiments demonstrate the proposed UAP’s ability to increase the processing time of individual frames by adding” phantom” objects that overload the NMS algorithm while preserving the detection of the original objects which allows the attack to go undetected for a longer period of time.

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2023

VulChecker: Graph-based Vulnerability Localization in Source Code

Yisroel Mirsky, George Macon, Michael Brown, Carter Yagemann, Matthew Pruett, Evan Downing, Sukarno Mertoguno, Wenke Lee

USENIX Security '23, 2023

2023

VulChecker: Graph-based Vulnerability Localization in Source Code

Yisroel Mirsky, George Macon, Michael Brown, Carter Yagemann, Matthew Pruett, Evan Downing, Sukarno Mertoguno, Wenke Lee

USENIX Security '23, 2023

In software development, it is critical to detect vulnerabilities in a project as early as possible. Although, deep learning has shown promise in this task, current state-of-the-art methods cannot classify and identify the line on which the vulnerability occurs. Instead, the developer is tasked with searching for an arbitrary bug in an entire function or even larger region of code. In this paper, we propose VulChecker: a tool that can precisely locate vulnerabilities in source code (down to the exact instruction) as well as classify their type (CWE). To accomplish this, we propose a new program representation, program slicing strategy, and the use of a message-passing graph neural network to utilize all of code’s semantics and improve the reach between a vulnerability’s root cause and manifestation points. We also propose a novel data augmentation strategy for cheaply creating strong datasets for vulnerability detection in the wild, using free synthetic samples available online. With this training strategy, VulChecker was able to identify 24 CVEs (10 from 2019 & 2020) in 19 projects taken from the wild, with nearly zero false positives compared to a commercial tool that could only detect 4. VulChecker also discovered an exploitable zero-day vulnerability, which has been reported to developers for responsible disclosure.

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2022/12/5

YolOOD: Utilizing Object Detection Concepts for Out-of-Distribution Detection

Alon Zolfi, Guy Amit, Amit Baras, Satoru Koda, Ikuya Morikawa, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2212.02081, 2022

2022/12/5

YolOOD: Utilizing Object Detection Concepts for Out-of-Distribution Detection

Alon Zolfi, Guy Amit, Amit Baras, Satoru Koda, Ikuya Morikawa, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2212.02081, 2022

Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However, OOD detection in the multi-label classification task remains an underexplored domain. In this research, we propose YolOOD – a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution) and irrelevant objects (e.g., OOD objects) on images that contain multiple objects from different categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD’s ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets.

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2022/11/27

Latent SHAP: Toward Practical Human-Interpretable Explanations

Ron Bitton, Alon Malach, Amiel Meiseles, Satoru Momiyama, Toshinori Araki, Jun Furukawa, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2211.14797, 2022

2022/11/27

Latent SHAP: Toward Practical Human-Interpretable Explanations

Ron Bitton, Alon Malach, Amiel Meiseles, Satoru Momiyama, Toshinori Araki, Jun Furukawa, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2211.14797, 2022

Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks. However, since complex classification models produce superior performance when trained on low-level (or encoded) features, in many cases, the explanations generated by these algorithms are neither interpretable nor usable by humans. Methods proposed in recent studies that support the generation of human-interpretable explanations are impractical, because they require a fully invertible transformation function that maps the model’s input features to the human-interpretable features. In this work, we introduce Latent SHAP, a black-box feature attribution framework that provides human-interpretable explanations, without the requirement for a fully invertible transformation function. We demonstrate Latent SHAP’s effectiveness using (1) a controlled experiment where invertible transformation functions are available, which enables robust quantitative evaluation of our method, and (2) celebrity attractiveness classification (using the CelebA dataset) where invertible transformation functions are not available, which enables thorough qualitative evaluation of our method.

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2022/11/18

Fingerprinting smartphones based on microphone characteristics from environment affected recordings

Adriana Berdich, Bogdan Groza, Efrat Levy, Asaf Shabtai, Yuval Elovici, Rene Mayrhofer

IEEE Access 10, 122399-122413, 2022

2022/11/18

Fingerprinting smartphones based on microphone characteristics from environment affected recordings

Adriana Berdich, Bogdan Groza, Efrat Levy, Asaf Shabtai, Yuval Elovici, Rene Mayrhofer

IEEE Access 10, 122399-122413, 2022

Fingerprinting devices based on unique characteristics of their sensors is an important research direction nowadays due to its immediate impact on non-interactive authentications and no less due to privacy implications. In this work, we investigate smartphone fingerprints obtained from microphone data based on recordings containing human speech, environmental sounds and several live recordings performed outdoors. We record a total of 19,200 samples using distinct devices as well as identical microphones placed on the same device in order to check the limits of the approach. To comply with real-world circumstances, we also consider the presence of several types of noise that is specific to the scenarios which we address, e.g., traffic and market noise at distinct volumes, and may reduce the reliability of the data. We analyze several classification techniques based on traditional machine learning algorithms …

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2022/11/16

Improving Interpretability via Regularization of Neural Activation Sensitivity

Ofir Moshe, Gil Fidel, Ron Bitton, Asaf Shabtai

arXiv preprint arXiv:2211.08686, 2022

2022/11/16

Improving Interpretability via Regularization of Neural Activation Sensitivity

Ofir Moshe, Gil Fidel, Ron Bitton, Asaf Shabtai

arXiv preprint arXiv:2211.08686, 2022

State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses – their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about the security and generalization of DNNs in real-world conditions, whereas the latter impedes users’ trust in their output. In this research, we (1) examine the effect of adversarial robustness on interpretability and (2) present a novel approach for improving the interpretability of DNNs that is based on regularization of neural activation sensitivity. We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques. Our results show that adversarially robust models are superior to standard models and that models trained using our proposed method are even better than adversarially robust models in terms of interpretability.

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2022/11/16

Attacking object detector using a universal targeted label-switch patch

Avishag Shapira, Ron Bitton, Dan Avraham, Alon Zolfi, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2211.08859, 2022

2022/11/16

Attacking object detector using a universal targeted label-switch patch

Avishag Shapira, Ron Bitton, Dan Avraham, Alon Zolfi, Yuval Elovici, Asaf Shabtai

arXiv preprint arXiv:2211.08859, 2022

Adversarial attacks against deep learning-based object detectors (ODs) have been studied extensively in the past few years. These attacks cause the model to make incorrect predictions by placing a patch containing an adversarial pattern on the target object or anywhere within the frame. However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object. In this study, we propose a novel, universal, targeted, label-switch attack against the state-of-the-art object detector, YOLO. In our attack, we use (i) a tailored projection function to enable the placement of the adversarial patch on multiple target objects in the image (e.g., cars), each of which may be located a different distance away from the camera or have a different view angle relative to the camera, and (ii) a unique loss function capable of changing the label of the attacked objects. The proposed universal patch, which is trained in the digital domain, is transferable to the physical domain. We performed an extensive evaluation using different types of object detectors, different video streams captured by different cameras, and various target classes, and evaluated different configurations of the adversarial patch in the physical domain.

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2022/11/6

The threat of offensive ai to organizations

Yisroel Mirsky, Ambra Demontis, Jaidip Kotak, Ram Shankar, Deng Gelei, Liu Yang, Xiangyu Zhang, Maura Pintor, Wenke Lee, Yuval Elovici, Battista Biggio

Computers & Security, 103006, 2022

2022/11/6

The threat of offensive ai to organizations

Yisroel Mirsky, Ambra Demontis, Jaidip Kotak, Ram Shankar, Deng Gelei, Liu Yang, Xiangyu Zhang, Maura Pintor, Wenke Lee, Yuval Elovici, Battista Biggio

Computers & Security, 103006, 2022

AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns.Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future?In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then …

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2022/11/1

Practical evaluation of poisoning attacks on online anomaly detectors in industrial control systems

Moshe Kravchik, Luca Demetrio, Battista Biggio, Asaf Shabtai

Computers & Security 122, 102901, 2022

2022/11/1

Practical evaluation of poisoning attacks on online anomaly detectors in industrial control systems

Moshe Kravchik, Luca Demetrio, Battista Biggio, Asaf Shabtai

Computers & Security 122, 102901, 2022

Recently, neural networks (NNs) have been proposed for the detection of cyber attacks targeting industrial control systems (ICSs). Such detectors are often retrained, using data collected during system operation, to cope with the evolution of the monitored signals over time. However, by exploiting this mechanism, an attacker can fake the signals provided by corrupted sensors at training time and poison the learning process of the detector to allow cyber attacks to stay undetected at test time. Previous work explored the ability to generate adversarial samples that fool anomaly detection models in ICSs but without compromising their training process. With this research, we are the first to demonstrate such poisoning attacks on ICS cyber attack online detectors based on neural networks. We propose two distinct attack algorithms, namely, interpolation- and back-gradient-based poisoning, and demonstrate their …

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2022/11/1

Security of open radio access networks

Dudu Mimran, Ron Bitton, Yehonatan Kfir, Eitan Klevansky, Oleg Brodt, Heiko Lehmann, Yuval Elovici, Asaf Shabtai

Computers & Security 122, 102890, 2022

2022/11/1

Security of open radio access networks

Dudu Mimran, Ron Bitton, Yehonatan Kfir, Eitan Klevansky, Oleg Brodt, Heiko Lehmann, Yuval Elovici, Asaf Shabtai

Computers & Security 122, 102890, 2022

The Open Radio Access Network (O-RAN) is a promising radio access network (RAN) architecture aimed at reshaping the RAN industry toward an open, adaptive, and intelligent RAN. In this paper, we perform a comprehensive security analysis of O-RANs. Specifically, we review the architectural blueprint designed by the O-RAN Alliance, leader in the cellular ecosystem. As part of the security analysis, we provide a detailed overview of the O-RAN architecture; present an ontology for evaluating the security of a system that is currently at an early development stage; identify O-RAN’s high-risk areas; enumerate O-RAN’s threat actors; and model potential threats to O-RAN. The significance of this work is in providing an updated attack surface to cellular network operators. Based on the attack surface, cellular network operators can carefully deploy the appropriate countermeasures to improve O-RAN’s security.

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2022/9/28

Workshop on Security and Privacy in Social Networks (SPSN 2012)

Yaniv Altshuler, Armin Cremers, Yehudith Naftalovich, VS Subrahmanian, Rami Puzis, MIT Yves-Alex, re de Montjoye, Arie Matsliah, Manuel Cebrian, UCSD Wei Pan, MIT Jean-Pierre Seifert, Bernhard Loehlein, Deutsche Telekom

2022/9/28

Workshop on Security and Privacy in Social Networks (SPSN 2012)

Yaniv Altshuler, Armin Cremers, Yehudith Naftalovich, VS Subrahmanian, Rami Puzis, MIT Yves-Alex, re de Montjoye, Arie Matsliah, Manuel Cebrian, UCSD Wei Pan, MIT Jean-Pierre Seifert, Bernhard Loehlein, Deutsche Telekom

Workshop on Security and Privacy in Social Networks (SPSN 2012) Committees Toggle
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Social Networks (SPSN 2012) Committees 2012, pp. xxv-xxv, DOI Bookmark: 10.1109/SocialCom-PASSAT.2012.142
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Security and Privacy in Social Networks(SPSN 2012) Organizing Committee Yuval Elovici, …

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2022/9/28

PASSAT/SocialCom 2011 Workshop Committees

Armin Cremers, Alfred Bruckstein, VS Subrahmanian, Rami Puzis, Sagi Ben Moshe, Ronen Vaisenberg, Arie Matsliah, Orna Agmon Ben-Yehuda, Shlomi Dolev, Alvin Chin, Martin Atzmueller, Denis Helic, Ed Chi, Markus Strohmaier, Daniel Gayo-Avello, France Wai-Tat Fu

2022/9/28

PASSAT/SocialCom 2011 Workshop Committees

Armin Cremers, Alfred Bruckstein, VS Subrahmanian, Rami Puzis, Sagi Ben Moshe, Ronen Vaisenberg, Arie Matsliah, Orna Agmon Ben-Yehuda, Shlomi Dolev, Alvin Chin, Martin Atzmueller, Denis Helic, Ed Chi, Markus Strohmaier, Daniel Gayo-Avello, France Wai-Tat Fu

Provides a listing of current committee members.

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2022/9/28

ASONAM 2011 External Reviewers

Hijbul Alam, Sofia Angeletou, Sam Blasiak, Haiquan Chen, Shumo Chu, Kamalika Das, Pasquale De Meo, Bolin Ding, Huiji Gao, Toader David Gherasim, Robert Görke, Zhouzhou He, Xia Hu, Ioana Hulpus, Andrey Kan, Daisuke Kitayama, Shamanth Kumar, Yi-Horng Lai, Ofrit Lesser, Alex Leung, Yingming Li, Shoude Lin, Lin Liu, Claudia Marinica, Pierre-Nicolas Mougel, Kenta Oku, Yu Peng, Giovanni Ponti, Harald Psaier, Rami Puzis, Kun Qian, Pir Abdul Rasool Queshi, Pir Abdul Rasool Qureshi, Zeehasham Rasheed, Jia Rong Rouff, Ning Ruan, Tanwistha Saha, Marian Scuturici, Anna Stavrianou, Xiaoyuan Su, Jiliang Tang, Mohammad A Tayebi, Ze Tian, Martin Treiber, Shiro Uesugi, Jinlong Wang, Chao-Lin Wu, Yang Xiang, Zhiqiang Xu, Ming Yang, Liangliang Ye, Xiao Yu, Yang Yu, Jianwei Zhang

2022/9/28

ASONAM 2011 External Reviewers

Hijbul Alam, Sofia Angeletou, Sam Blasiak, Haiquan Chen, Shumo Chu, Kamalika Das, Pasquale De Meo, Bolin Ding, Huiji Gao, Toader David Gherasim, Robert Görke, Zhouzhou He, Xia Hu, Ioana Hulpus, Andrey Kan, Daisuke Kitayama, Shamanth Kumar, Yi-Horng Lai, Ofrit Lesser, Alex Leung, Yingming Li, Shoude Lin, Lin Liu, Claudia Marinica, Pierre-Nicolas Mougel, Kenta Oku, Yu Peng, Giovanni Ponti, Harald Psaier, Rami Puzis, Kun Qian, Pir Abdul Rasool Queshi, Pir Abdul Rasool Qureshi, Zeehasham Rasheed, Jia Rong Rouff, Ning Ruan, Tanwistha Saha, Marian Scuturici, Anna Stavrianou, Xiaoyuan Su, Jiliang Tang, Mohammad A Tayebi, Ze Tian, Martin Treiber, Shiro Uesugi, Jinlong Wang, Chao-Lin Wu, Yang Xiang, Zhiqiang Xu, Ming Yang, Liangliang Ye, Xiao Yu, Yang Yu, Jianwei Zhang

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Bookmark: 10.1109/ASONAM.2011.9 Keywords Authors Abstract The conference offers a note
of thanks and lists its reviewers. ASONAM 2011 External Reviewers Hijbul Alam Sofia
Angeletou Sam Blasiak Haiquan Chen Shumo Chu Kamalika Das Pasquale De Meo Bolin Ding
Huiji Gao Toader David Gherasim Robert Görke Zhouzhou He Xia Hu Ioana Hulpus Andrey …

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2022/9/28

SwSTE 2016 Reviewers

Yehuda Afek, Paolo Alencar, Amir Averbuch, Mira Balaban, IBM Avishay Bartik, Israel Dan Berry, Anat Bremler-Barr, Ronit Bustin, Itai Dinur, Danny Dolev, YDC Yael Dubinsky, Israel Amit Dvir, Michael Elhadad, Yuval Elovici, Dror Feitelson, Yishai A Feldman, Ariel Frank, Carlo Ghezzi, Yossi Gil, Ehud Gudes, Orit Hazzan, Danny Hendler, Shmuel Katz, Daniel Khankin, Vladimir Kolesnikov, Jeff Kramer, Sarit Kraus, Tsvi Kuflik, Tami Lapidot, Julio Cesar Leite, Luisa Mich, Leon Osterweil, Rami Puzis, Iris Reinhartz-Berger

2022/9/28

SwSTE 2016 Reviewers

Yehuda Afek, Paolo Alencar, Amir Averbuch, Mira Balaban, IBM Avishay Bartik, Israel Dan Berry, Anat Bremler-Barr, Ronit Bustin, Itai Dinur, Danny Dolev, YDC Yael Dubinsky, Israel Amit Dvir, Michael Elhadad, Yuval Elovici, Dror Feitelson, Yishai A Feldman, Ariel Frank, Carlo Ghezzi, Yossi Gil, Ehud Gudes, Orit Hazzan, Danny Hendler, Shmuel Katz, Daniel Khankin, Vladimir Kolesnikov, Jeff Kramer, Sarit Kraus, Tsvi Kuflik, Tami Lapidot, Julio Cesar Leite, Luisa Mich, Leon Osterweil, Rami Puzis, Iris Reinhartz-Berger

The conference offers a note of thanks and lists its reviewers.

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2022/9/28

Is the News Deceptive? Fake News Detection using Topic Authenticity

Aviad Elyashar, Jorge Bendahan, Rami Puzis

2022/9/28

Is the News Deceptive? Fake News Detection using Topic Authenticity

Aviad Elyashar, Jorge Bendahan, Rami Puzis

In this paper, we propose an approach for the detection of fake news in online social media (OSM). The approach is based on the authenticity of online discussions published by fake news promoters and legitimate accounts. Authenticity is quantified using a machine learning (ML) classifier that distinguishes between fake news promoters and legitimate accounts. In addition, we introduce novel link prediction features that were shown to be useful for classification. A description of the processes used to divide the dataset into categories representing topics or online discussions and measuring the authenticity of online discussions is provided. We also discuss new data collection methods for OSM, describe the process used to retrieve accounts and their posts in order to train traditional ML classifiers, and present guidelines for manually labeling accounts. The proposed approach is demonstrated using a Twitter pro-ISIS fanboy dataset provided by Kaggle. Our results show that the method can determine a topic’s authenticity from fake news promoters, and legitimate accounts. Thus, the suggested approach is effective for discriminating between topics that were strongly promoted by fake news promoters and those that attracted authentic public interest.

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2022/9/28

Potential Search: a greedy anytime heuristic search

Roni Stern, Rami Puzis, Ariel Felner

2022/9/28

Potential Search: a greedy anytime heuristic search

Roni Stern, Rami Puzis, Ariel Felner

In this paper we explore a novel approach for anytime heuristic search, in which the node that is most probable to improve the incumbent solution is expanded first. This is especially suited for the anytime aspect of anytime algorithms-the possibility that the algorithm will be be halted anytime throughout the search. The potential of a node to improve the incumbent solution is estimated by a custom cost function, resulting in Potential Search, an anytime best-first search. Experimental results on the 15-puzzle and the key player problem in communication networks (KPP-COM) show that this approach is competitive with state-of-the-art anytime heuristic search algorithms, and is more robust.

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2022/9/28

Iterative Keyword Optimization

Aviad Elyashar, Maor Reuben, Rami Puzis

2022/9/28

Iterative Keyword Optimization

Aviad Elyashar, Maor Reuben, Rami Puzis

Short keyword queries are one of the main tool of any user or bot seeking information through the ubiquitous search engines. Automated keyword optimization relies primarily on the analysis of data repositories in order to find a small set of keywords that identify the topic discussed and relevant documents. However, most search engines, available today on the Web are opaque, providing little to no information about their methods and the searched repository.In this paper, we propose an automated iterative optimization of short keyword queries in order to improve information retrieval from opaque (black box) search engines. The use case considered involves the retrieval of relevant posts from online social media for a given a news article (claim) discussed online. The proposed algorithm iteratively selects keywords while querying the search engine and comparing a small set of retrieved posts to the given news article using a mean relevance error based on word embedding. We demonstrate the proposed algorithm while building a Fake News dataset from claims (collected from fact-checking websites) and their associated tweets. The proposed mean relevance error was found to be accurate for differentiating between relevant and irrelevant posts (0.9 AUC). The optimized queries produce similar results to manually extracted keywords and outperform TF-IDF based methods and POS tagging.

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2022/9/28

PriSecCSN2012 Organizing and Program Committees

S Yu Philip, Bhavani Thuraisingham, Vijay Varadharajan, Chang Liu, Jinjun Chen, Rose-Mharie Åhlfeldt, Cristina Alcaraz, Basel Alomair, Aless, ro Arm, o, Reza Azarderakhsh, Shlomi Dolev, Rino Falcone, Debasis Giri, Dieter Gollmann, Kartik Gopalan, Victor Govindaswamy, Shuguo Han, Ching-Hsien Hsu, Meiko Jensen, Henrik Johnsson, Changhoon Lee, Sjouke Mauw, Charles Morisset, Stefano Paraboschi, Gerard Parr, Siani Pearson, Guenther Pernul, Radha Poovendran, Rami Puzis, Peter Ryan, Jean-Marc Seigneur, Abhinav Srivastava, Guilin Wang, Ke Wang, Yang Xiang

2022/9/28

PriSecCSN2012 Organizing and Program Committees

S Yu Philip, Bhavani Thuraisingham, Vijay Varadharajan, Chang Liu, Jinjun Chen, Rose-Mharie Åhlfeldt, Cristina Alcaraz, Basel Alomair, Aless, ro Arm, o, Reza Azarderakhsh, Shlomi Dolev, Rino Falcone, Debasis Giri, Dieter Gollmann, Kartik Gopalan, Victor Govindaswamy, Shuguo Han, Ching-Hsien Hsu, Meiko Jensen, Henrik Johnsson, Changhoon Lee, Sjouke Mauw, Charles Morisset, Stefano Paraboschi, Gerard Parr, Siani Pearson, Guenther Pernul, Radha Poovendran, Rami Puzis, Peter Ryan, Jean-Marc Seigneur, Abhinav Srivastava, Guilin Wang, Ke Wang, Yang Xiang

PriSecCSN2012 Organizing and Program Committees Toggle navigation IEEE Computer Society
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Cover Image Download 1.Home 2.Proceedings 3.cgc 2012 PriSecCSN2012 Organizing and
Program Committees 2012, pp. xxviii-xxviii, DOI Bookmark: 10.1109/CGC.2012.135
Keywords Authors Abstract Provides a listing of current committee members. PriSecCSN2012
Organizing and Program Committees General Chairs Philip S. Yu, University of Illinois
at Chicago, USA Bhavani Thuraisingham, The University of Texas at Dallas, USA …

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2022/9/28

AGrO-Fake: optimal Attack Graph Obfuscation using Fake vulnerabilities

Hadar Polad, Rami Puzis, Bracha Shapira

2022/9/28

AGrO-Fake: optimal Attack Graph Obfuscation using Fake vulnerabilities

Hadar Polad, Rami Puzis, Bracha Shapira

Following initial penetration into the victim’s network, adversaries usually explore environment in an attempt to discover the network topology and vulnerabilities in the victim’s hosts. Attack graphs are used to model the paths of an attacker within the victim’s network making his/her way towards the goal. Falsifying the information collected by the adversary may significantly slow down lateral movement and increase the amount of noise generated by the attacker within the victim’s network. This in turn will ease on his/her detection. Fake vulnerabilities deployed within the enterprise network can obfuscate the attack graph observed by the adversary. Heuristic search algorithms are used to optimize the assignments of fake vulnerabilities in terms of the maximal negative impact of the attack cost. According to computational experiments conducted on a real large-scale network the proposed deception-based defense can increase the number of attack actions required to reach a goal by more that 100% with only 2-5 fake vulnerabilities.

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2022/9/28

Group Betweenness Centrality: Efficient Computations and Applications

Rami Puzis

2022/9/28

Group Betweenness Centrality: Efficient Computations and Applications

Rami Puzis

Complex networks are used to study the structure and dynamics of complex systems in various disciplines. For example in social networks, vertices are usually individuals and edges characterize the relations between them; in computer networks, vertices might be routers connected to each other through communication lines.In many applications we are required to locate the most prominent group of vertices in a complex network. For example, Ballester et al. state in [1] the importance of finding the key group in a criminal network. Borgatti elaborates in [2] on a Key Player Problem (KPP) that is strongly related to the cohesion of a network. Groups or routers or links that has maximal potential to control over traffic in communication networks can be used to increase the effectiveness of network measurements or intrusion detection in computer communication networks.

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2022/9/28

Optimizing the Deployment Strategy of Distributed Network Intrusion Detection Systems (DNIDS) in Large-scale Communication Networks

Rami Puzis

Ben Gurion University, 2009

2022/9/28

Optimizing the Deployment Strategy of Distributed Network Intrusion Detection Systems (DNIDS) in Large-scale Communication Networks

Rami Puzis

Ben Gurion University, 2009

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2022/9/28

On-Line detection and prediction of temporal patterns

Shlomi Dolev, Jonathan Goldfeld, Rami Puzis

Haifa Verification Conference, 254-256, 2011

2022/9/28

On-Line detection and prediction of temporal patterns

Shlomi Dolev, Jonathan Goldfeld, Rami Puzis

Haifa Verification Conference, 254-256, 2011

Identifying a temporal pattern of events is a fundamental task of on-line (real-time) verification. In this work we present efficient schemes for on-line monitoring of events for identifying predefined patterns of events. The schemes use preprocessing to ensure that the number of comparisons during run-time is minimized. In particular, obsoloete sub-sequences are discarded to avoid unnecessary comparisons.We use our monitoring scheme for estimating the probability that a random suffix of a given execution will contain the pattern.

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2022/9/28

Lookie-A Case Study of a Location Based Collaborative Application

Elina Yaakobovich, Rami Puzis

COLLA 2014, 42, 2014

2022/9/28

Lookie-A Case Study of a Location Based Collaborative Application

Elina Yaakobovich, Rami Puzis

COLLA 2014, 42, 2014

In the age of smartphones, increased online social connectivity, and advanced technological capabilities, collaborative applications often take advantage of crowd resources in an effort to enhance the welfare of the community. Lookie is a collaborative application where users can ask other users to share up to date footage regarding their whereabouts. This paper presents the results of a field trial performed with Lookie, focusing on aspects of user experience, privacy, and participation. Analysis of system logs and questionnaires answered by the field trial participants produced the following key results:(1) users’ perceived participation is biased toward their own active deeds,(2) appropriate timing of requests and personalized meaningful request messages improve user experience,(3) most users do not mind helping strangers by taking pictures or answering requests but many refrain from disclosing their location, and finally,(4) users that indicate privacy concerns and feel reluctant to reply to requests, have the same average response ratio as the rest of the community, although, they initiate less interactions.

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2022/9/28

Brain inspired automatic directory

Itay Azaria, Shlomi Dolev, Ariel Hanemann, Rami Puzis

2016 Second International Symposium on Stochastic Models in Reliability …, 2016

2022/9/28

Brain inspired automatic directory

Itay Azaria, Shlomi Dolev, Ariel Hanemann, Rami Puzis

2016 Second International Symposium on Stochastic Models in Reliability …, 2016

The fascinating question of the relation of information and coding theory to the memories stored in the brain is our research scope. We speculate there is a similar code used to represent different memories, rather than unique code for different memories. The uniform cortex structure supports our speculation. Recently we suggested holographic coding that can fit Pribram’s holographic memory theory. Using the holographic coding metaphor, the memory should be retrieved by a reference beam as in a hologram. We explore the possibility that the brain learns its directory (possibly in the temporal lobe), during memory consolidation. This directory is a neural network that is used for sending signals to the cortex to recall memories. The network learns to distinguish between objects during saving, in order to signal the correct recall. Haar features (HF) are 0/1 matrices used for face recognition. We use HF to learn to …

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2022/9/28

Efficient online detection of temporal patterns

Shlomi Dolev, Jonathan Goldfeld, Rami Puzis

PeerJ Computer Science 2, e53, 2016

2022/9/28

Efficient online detection of temporal patterns

Shlomi Dolev, Jonathan Goldfeld, Rami Puzis

PeerJ Computer Science 2, e53, 2016

Identifying a temporal pattern of events is a fundamental task of online (real-time) verification. We present efficient schemes for online monitoring of events for identifying desired/undesired patterns of events. The schemes use preprocessing to ensure that the number of comparisons during run-time is minimized. In particular, the first comparison following the time point when an execution sub-sequence cannot be further extended to satisfy the temporal requirements halts the process that monitors the sub-sequence.

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2022/9/28

Pinpoint Influential Posts and Authors

Luiza Nacshon, Rami Puzis, Amparo Sanmateho

arXiv preprint arXiv:1609.02945, 2016

2022/9/28

Pinpoint Influential Posts and Authors

Luiza Nacshon, Rami Puzis, Amparo Sanmateho

arXiv preprint arXiv:1609.02945, 2016

This research presents an analytical model that aims to pin-point influential posts across a social web comprised of a corpus of posts. The model employs the Latent Dirichlet Al-location algorithm to associate posts with topics, and the TF-IDF metric to identify the key posts associated with each top-ic. The model was demonstrated in the domain of customer relationship by enabling careful monitoring of evolving “storms” created by individuals which tend to impact large audiences (either positively or negatively). Future research should be engaged in order to extend the scope of the corpus by including additional relevant publicly available sources.

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2022/9/28

DiscOF: Balanced flow discovery in OpenFlow

Luiza Nacshon, Rami Puzis, Polina Zilberman

2017 IEEE Conference on Network Function Virtualization and Software Defined …, 2017

2022/9/28

DiscOF: Balanced flow discovery in OpenFlow

Luiza Nacshon, Rami Puzis, Polina Zilberman

2017 IEEE Conference on Network Function Virtualization and Software Defined …, 2017

Flexibility and extendibility of Software Defined Networks allows development of diverse network management and flow monitoring techniques. Yet, there are inherent tradeoffs between the quality of flow monitoring and the required network resources. In particular, collecting flow statistics, at the level of specific source-destination addresses (and, moreover, specific protocols and ports), requires too many flow table entries. This problem is emphasized by the difficulty of anticipating the individual flows that need to be monitored. In this paper we propose a method for dynamic flow discovery at any required spatial resolution. In addition, we propose a method for balancing the monitoring effort among the switches. These methods allow increasing the spatial resolution of traffic monitoring with minimal effects of the network performance.

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2022/9/28

Target oriented network intelligence collection: effective exploration of social networks

Rami Puzis, Liron Kachko, Barak Hagbi, Roni Stern, Ariel Felner

World Wide Web 22, 1447-1480, 2019

2022/9/28

Target oriented network intelligence collection: effective exploration of social networks

Rami Puzis, Liron Kachko, Barak Hagbi, Roni Stern, Ariel Felner

World Wide Web 22, 1447-1480, 2019

Target Oriented Network Intelligence Collection (TONIC) is a crawling process whose goal is to find social network profiles that contain information about a given target. Such profiles are called leads and the TONIC problem is how to minimize crawling costs incurred while finding them. We model this problem as a search problem in an unknown graph and present a best-first search approach for solving it. Three key challenges are (1) which profiles to consider crawling to, (2) how to prioritize the crawling order, and (3) when additional crawling is not worthwhile. For the first challenge, we propose two frameworks: the Restricted TONIC Framework (RTF), that restricts the search to immediate neighbors of previously found leads, and the Extended TONIC Framework (ETF), that extends the scope of the search to a wider neighborhood. Guidelines for when to choose which framework are provided. For the …

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2022/9/28

Attack Hypothesis Generation

Polina Zilberman Aviad Elitzur, Rami Puzis

Conference: European Intelligence and Security Informatics Conference (EISIC …, 2019

2022/9/28

Attack Hypothesis Generation

Polina Zilberman Aviad Elitzur, Rami Puzis

Conference: European Intelligence and Security Informatics Conference (EISIC …, 2019

2022/9/28

It Runs in the Family: Searching for Synonyms Using Digitized Family Trees

Aviad Elyashar, Rami Puzis, Michael Fire

arXiv preprint arXiv:1912.04003, 2019

2022/9/28

It Runs in the Family: Searching for Synonyms Using Digitized Family Trees

Aviad Elyashar, Rami Puzis, Michael Fire

arXiv preprint arXiv:1912.04003, 2019

Searching for a person’s name is a common online activity. However, Web search engines provide few accurate results to queries containing names. In contrast to a general word which has only one correct spelling, there are several legitimate spellings of a given name. Today, most techniques used to suggest synonyms in online search are based on pattern matching and phonetic encoding, however they often perform poorly. As a result, there is a need for an effective tool for improved synonym suggestion. In this paper, we propose a revolutionary approach for tackling the problem of synonym suggestion. Our novel algorithm, GRAFT, utilizes historical data collected from genealogy websites, along with network algorithms. GRAFT is a general algorithm that suggests synonyms using a graph based on names derived from digitized ancestral family trees. Synonyms are extracted from this graph, which is constructed using generic ordering functions that outperform other algorithms that suggest synonyms based on a single dimension, a factor that limits their performance. We evaluated GRAFT’s performance on three ground truth datasets of forenames and surnames, including a large-scale online genealogy dataset with over 16 million profiles and more than 700,000 unique forenames and 500,000 surnames. We compared GRAFT’s performance at suggesting synonyms to 10 other algorithms, including phonetic encoding, string similarity algorithms, and machine and deep learning algorithms. The results show GRAFT’s superiority with respect to both forenames and surnames and demonstrate its use as a tool to improve synonym suggestion.

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2022/9/28

How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning

Aviad Elyashar, Rami Puzis, Michael Fire

arXiv preprint arXiv:2005.11838, 2020

2022/9/28

How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning

Aviad Elyashar, Rami Puzis, Michael Fire

arXiv preprint arXiv:2005.11838, 2020

Searching for information about a specific person is an online activity frequently performed by many users. In most cases, users are aided by queries containing a name and sending back to the web search engines for finding their will. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Currently, most solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic approach which addresses the similar name suggestion problem by utilizing automated speech generation, and deep learning to produce spoken name embeddings. This sophisticated and innovative embeddings captures the way people pronounce names in any language and accent. Utilizing the name pronunciation can be helpful for both differentiating and detecting names that sound alike, but are written differently. The proposed approach was demonstrated on a large-scale dataset consisting of 250,000 forenames and evaluated using a machine learning classifier and 7,399 names with their verified synonyms. The performance of the proposed approach was found to be superior to 10 other algorithms evaluated in this study, including well used phonetic and string similarity algorithms, and two recently proposed algorithms. The results obtained suggest that the proposed approach could serve as a useful and valuable tool for solving the similar name suggestion problem.

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2022/9/28

Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback

Aviad Elyashar, Maor Reuben, Rami Puzis

arXiv preprint arXiv:2012.12498, 2020

2022/9/28

Fake News Data Collection and Classification: Iterative Query Selection for Opaque Search Engines with Pseudo Relevance Feedback

Aviad Elyashar, Maor Reuben, Rami Puzis

arXiv preprint arXiv:2012.12498, 2020

Retrieving information from an online search engine, is the first and most important step in many data mining tasks. Most of the search engines currently available on the web, including all social media platforms, are black-boxes (a.k.a opaque) supporting short keyword queries. In these settings, retrieving all posts and comments discussing a particular news item automatically and at large scales is a challenging task. In this paper, we propose a method for generating short keyword queries given a prototype document. The proposed iterative query selection algorithm (IQS) interacts with the opaque search engine to iteratively improve the query. It is evaluated on the Twitter TREC Microblog 2012 and TREC-COVID 2019 datasets showing superior performance compared to state-of-the-art. IQS is applied to automatically collect a large-scale fake news dataset of about 70K true and fake news items. The dataset, publicly available for research, includes more than 22M accounts and 61M tweets in Twitter approved format. We demonstrate the usefulness of the dataset for fake news detection task achieving state-of-the-art performance.

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2022/9/28

It Runs in the Family: Unsupervised Algorithm for Alternative Name Suggestion Using Digitized Family Trees

Aviad Elyashar, Rami Puzis, Michael Fire

IEEE Transactions on Knowledge & Data Engineering, 1-1, 2021

2022/9/28

It Runs in the Family: Unsupervised Algorithm for Alternative Name Suggestion Using Digitized Family Trees

Aviad Elyashar, Rami Puzis, Michael Fire

IEEE Transactions on Knowledge & Data Engineering, 1-1, 2021

Searching for a person’s name is a common online activity. However, Web search engines provide few accurate results to queries containing names. In contrast to a general word that has only one correct spelling, there are several possible legitimate spellings when a name provided as a query. Today, most techniques used to suggest diminutives and alternative spellings in online search are based on pattern matching and phonetic encoding; however, they often perform poorly. As a result, there is a need for an effective tool for improved alternative name suggestion for a name provided as a query. In this paper, we propose a revolutionary approach for tackling the problem of alternative name suggestion. Our novel algorithm, GRAFT, utilizes historical data collected from genealogy websites, along with network algorithms. GRAFT is a general algorithm that suggests alternatives for input names using a graph based …

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2022/9/28

How does that name sound? Name representation learning using accent-specific speech generation

Aviad Elyashar, Rami Puzis, Michael Fire

Knowledge-Based Systems 227, 107229, 2021

2022/9/28

How does that name sound? Name representation learning using accent-specific speech generation

Aviad Elyashar, Rami Puzis, Michael Fire

Knowledge-Based Systems 227, 107229, 2021

Searching for information about a specific person is a frequent online activity. In most cases, users are aided in the search process by queries containing a name in Web search engines. Typically, Web search engines provide just a few accurate results associated with a name-containing query. Most existing solutions for suggesting synonyms in online search are based on pattern matching and phonetic encoding, however very often, the performance of such solutions is less than optimal. In this paper, we propose SpokenName2Vec, a novel and generic algorithm which addresses the synonym suggestion problem by utilizing automated speech generation, and deep learning to produce novel spoken name embeddings. These embeddings capture the way people pronounce names in a particular language and accent. Utilizing a name’s pronunciation can help detect names that sound alike, but are written differently …

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2022/9/28

MCTransformer: Combining Transformers And Monte-Carlo Tree Search For Offline Reinforcement Learning

Gur Yaari, Lior Rokach, Rami Puzis, Gilad Katz

2022/9/28

MCTransformer: Combining Transformers And Monte-Carlo Tree Search For Offline Reinforcement Learning

Gur Yaari, Lior Rokach, Rami Puzis, Gilad Katz

Recent studies explored the framing of reinforcement learning as a sequence modeling problem, and then using Transformers to generate effective solutions. In this study, we introduce MCTransformer, a framework that combines Monte-Carlo Tree Search (MCTS) with Transformers. Our approach uses an actor-critic setup, where the MCTS component is responsible for navigating previously-explored states, aided by input from the Transformer. The Transformer controls the exploration and evaluation of new states, enabling an effective and efficient evaluation of various strategies. In addition to the development of highly effective strategies, our setup enables the use of more efficient sampling compared to existing MCTS-based solutions. MCTransformer is therefore able to perform a small number of evaluations for each newly-explored node, and to do so without degrading its performance. Our evaluation, conducted on the challenging and well-known problem of SameGame, shows that our approach outperforms both Transformer-based and MCTS-based solutions.

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2022/9/28

MABAT: A Multi-Armed Bandit Approach for Threat-Hunting

Liad Dekel, Ilia Leybovich, Polina Zilberman, Rami Puzis

IEEE Transactions on Information Forensics and Security 18, 477-490, 2022

2022/9/28

MABAT: A Multi-Armed Bandit Approach for Threat-Hunting

Liad Dekel, Ilia Leybovich, Polina Zilberman, Rami Puzis

IEEE Transactions on Information Forensics and Security 18, 477-490, 2022

Threat hunting relies on cyber threat intelligence to perform active hunting of prospective attacks instead of waiting for an attack to trigger some pre-configured alerts. One of the most important aspects of threat hunting is automation, especially when it concerns targeted data collection. Multi-armed bandits (MAB) is a family of problems that can be used to optimize the targeted data collection and balance between exploration and exploitation of the collected data. Unfortunately, state-of-the-art policies for solving MAB with dependent arms do not utilize the detailed interrelationships between attacks such as telemetry or artifacts shared by multiple attacks. We propose new policies, one of which is theoretically proven, to prioritize the investigated attacks during targeted data collection. Experiments with real data extracted from VirusTotal behavior reports show the superiority of the proposed techniques and their …

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2022/9/28

Cross Version Defect Prediction with Class Dependency Embeddings

Moti Cohen, Lior Rokach, Rami Puzis

arXiv preprint arXiv:2212.14404, 2022

2022/9/28

Cross Version Defect Prediction with Class Dependency Embeddings

Moti Cohen, Lior Rokach, Rami Puzis

arXiv preprint arXiv:2212.14404, 2022

Software Defect Prediction aims at predicting which software modules are the most probable to contain defects. The idea behind this approach is to save time during the development process by helping find bugs early. Defect Prediction models are based on historical data. Specifically, one can use data collected from past software distributions, or Versions, of the same target application under analysis. Defect Prediction based on past versions is called Cross Version Defect Prediction (CVDP). Traditionally, Static Code Metrics are used to predict defects. In this work, we use the Class Dependency Network (CDN) as another predictor for defects, combined with static code metrics. CDN data contains structural information about the target application being analyzed. Usually, CDN data is analyzed using different handcrafted network measures, like Social Network metrics. Our approach uses network embedding techniques to leverage CDN information without having to build the metrics manually. In order to use the embeddings between versions, we incorporate different embedding alignment techniques. To evaluate our approach, we performed experiments on 24 software release pairs and compared it against several benchmark methods. In these experiments, we analyzed the performance of two different graph embedding techniques, three anchor selection approaches, and two alignment techniques. We also built a meta-model based on two different embeddings and achieved a statistically significant improvement in AUC of 4.7% (p < 0.002) over the baseline method.

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2022/9/28

Applying CVSS to Vulnerability Scoring in Cyber-Biological Systems

Rami Puzis, Isana Veksler-Lublinsky

Cyberbiosecurity, 115-134, 2023

2022/9/28

Applying CVSS to Vulnerability Scoring in Cyber-Biological Systems

Rami Puzis, Isana Veksler-Lublinsky

Cyberbiosecurity, 115-134, 2023

With the advent of synthetic biology, security concerns are rapidly emerging spanning both the biological and the digital realms. These concerns materialize into concrete weaknesses and vulnerabilities in biological and biomedical systems and in their supply chains. Cybersecurity risks and their biological impact on biosafety and health must be considered when developing new protocols, biological systems, and supporting machinery. It is very important to assess the risk and impact of exploiting cyberbiosecurity vulnerabilities in a systematic and methodological way. The common vulnerability scoring system (CVSS) quantifies the risk and impact of vulnerabilities in digital (software and hardware) systems. Although vulnerabilities in the machinery supporting synthetic biology can be reported in a standard way, their severity scoring does not encompass the biosafety and health impacts. Furthermore, no current …

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2022/9/28

DISCONA: distributed sample compression for nearest neighbor algorithm

Jedrzej Rybicki, Tatiana Frenklach, Rami Puzis

Applied Intelligence, 1-14, 2023

2022/9/28

DISCONA: distributed sample compression for nearest neighbor algorithm

Jedrzej Rybicki, Tatiana Frenklach, Rami Puzis

Applied Intelligence, 1-14, 2023

Sample compression using 𝜖-net effectively reduces the number of labeled instances required for accurate classification with nearest neighbor algorithms. However, one-shot construction of an 𝜖-net can be extremely challenging in large-scale distributed data sets. We explore two approaches for distributed sample compression: one where local 𝜖-net is constructed for each data partition and then merged during an aggregation phase, and one where a single backbone of an 𝜖-net is constructed from one partition and aggregates target label distributions from other partitions. Both approaches are applied to the problem of malware detection in a complex, real-world data set of Android apps using the nearest neighbor algorithm. Examination of the compression rate, computational efficiency, and predictive power shows that a single backbone of an 𝜖-net attains favorable performance while achieving a compression …

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2022/9/28

Link2speed: VANET speed assessment via link-state analysis

Alon Freund, Rami Puzis, Michael Segal

2023 IEEE Wireless Communications and Networking Conference (WCNC), 1-6, 2023

2022/9/28

Link2speed: VANET speed assessment via link-state analysis

Alon Freund, Rami Puzis, Michael Segal

2023 IEEE Wireless Communications and Networking Conference (WCNC), 1-6, 2023

Vehicular ad hoc network (VANET) is an emerging technology with a promising future and great challenges. It aims to promote safe driving, improve traffic flow and also enables a variety of entertainment applications. A fundamental need in such a network is the ability to assess vehicular speed. This enables the collection of statistics for the purpose of traffic engineering and long-term planning, and is also critical information for law enforcement groups. Many existing speed assessment technologies suffer from high physical visibility, and relatively expensive hardware. Even those that avoid detection, are inflexible due to being location specific. Therefore, reducing the ability to track and enforce traffic speed and limiting the collection of statistics for traffic engineering. In this paper, we propose a method for vehicle speed assessment, by extracting an induced Communication Connectivity Graph (CCG) from VANET …

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