ETHERLED: Air-gapped systems leak data via network card LEDs

Israeli researcher Mordechai Guri has discovered a new method to exfiltrate data from air-gapped systems using the LED indicators on network cards. Dubbed ‘ETHERLED’, the method turns the blinking lights into Morse code signals that can be decoded by an attacker. Capturing the signals requires a camera with a direct line of sight to LED lights on the air-gapped computer’s card. These can be translated into binary data to steal information. Air-gapped systems are computers typically found in highly-sensitive environments (e.g. critical infrastructure, weapon control units) that are isolated from the public int...

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Israeli researcher Mordechai Guri has discovered a new method to exfiltrate data from air-gapped systems using the LED indicators on network cards. Dubbed ‘ETHERLED’, the method turns the blinking lights into Morse code signals that can be decoded by an attacker.

Capturing the signals requires a camera with a direct line of sight to LED lights on the air-gapped computer’s card. These can be translated into binary data to steal information.

ETHERLED attack diagram (arxiv.org)

Air-gapped systems are computers typically found in highly-sensitive environments (e.g. critical infrastructure, weapon control units) that are isolated from the public internet for security reasons.

However, these systems work in air-gapped networks and still use a network card. If an intruder infects them with specially crafted malware, they could replace the card driver with a version that modifies the LED color and blinking frequency to send waves of encoded data, Mordechai Guri has found.

The ETHERLED method can work with other peripherals or hardware that use LEDs as status or operational indicators like routers, network-attached storage (NAS) devices, printers, scanners, and various other connected devices.

Compared to previously disclosed data exfiltration methods based on optical emanation that take control of LEDs in keyboards and modemsETHERLED is a more covert approach and less likely to raise suspicion.

ETHERLED details

The attack begins with planting on the target computer malware that contains a modified version of the firmware for the network card. This allows taking control of the LED blinking frequency, duration, and color.

Code to control LED indicators (arxiv.org)

Alternatively, the malware can directly attack the drive for the network interface controller (NIC) to change connectivity status or to modulate the LEDs required for generating the signals.

The three potential attack methods (arxiv.org)

The researcher found that the malicious driver can exploit documented or undocumented hardware functionality to fiddle with network connection speeds and to enable or disable the Ethernet interface, resulting in light blinks and color changes.

Network card indicators lighting up to convey signals (arxiv.org)

Guri’s tests show that each data frame begins with a sequence of ‘1010’, to mark the start of the package, followed by a payload of 64 bits.

Signal contents (arxiv.org)

For data exfiltration through single status LEDs, Morse code dots and dashes lasting between 100 ms and 300 ms were generated, separated by indicator deactivation spaces between 100 ms and 700 ms.

The bitrate of the Morse code can be increased by up to ten times (10m dots, 30m dashes, and 10-70ms spaces) when using the driver/firmware attack method.

To capture the signals remotely, threat actors can use anything from smartphone cameras (up to 30 meters), drones (up to 50m), hacked webcams (10m), hacked surveillance cameras (30m), and telescopes or cameras with  telephoto or superzoom lenses (over 100 meters).

The time needed to leak secrets such as passwords through ETHERLED ranges between 1 second and 1.5 minutes, depending on the attack method used, 2.5 sec to 4.2 minutes for private Bitcoin keys, and 42 seconds to an hour for 4096-bit RSA keys.

Times required to transmit secrets (arxiv.org)

Other exfiltration channels

Mordechai also published a paper on ‘GAIROSCOPE‘, an attack on air-gapped systems relying on the generation of resonance frequencies on the target system, captured by a nearby (up to 6 meters) smartphone’s gyroscope sensor.

In July, the same researcher presented the ‘SATAn’ attack, which uses SATA cables inside computers as antennas, generating data-carrying electromagnetic waves that can be captured by nearby (up to 1.2 meters) laptops.

The complete collection of Dr. Mordechai Guri’s air-gap covert channel methods can be found in a dedicated section on the Ben-Gurion University of the Negev website.

Source: bleepingcomputer.com

A new method devised to leak information and jump over air-gaps takes advantage of Serial Advanced Technology Attachment (SATA) or Serial ATA cables as a communication medium, adding to a long list of electromagnetic, magnetic, electric, optical, and acoustic methods already demonstrated to plunder data.

“Although air-gap computers have no wireless connectivity, we show that attackers can use the SATA cable as a wireless antenna to transfer radio signals at the 6GHz frequency band,” Dr. Mordechai Guri, the head of R&D in the Cyber Security Research Center in the Ben Gurion University of the Negev in Israel, wrote in a paper published last week.

The technique, dubbed SATAn, takes advantage of the prevalence of the computer bus interface, making it “highly available to attackers in a wide range of computer systems and IT environments.”

Put simply, the goal is to use the SATA cable as a covert channel to emanate electromagnetic signals and transfer a brief amount of sensitive information from highly secured, air-gapped computers wirelessly to a nearby receiver more than 1m away.

An air-gapped network is one that’s physically isolated from any other networks in order to increase its security. Air-gapping is seen as an essential mechanism to safeguard high-value systems that are of huge interest to espionage-motivated threat actors.

That said, attacks targeting critical mission-control systems have grown in number and sophistication in recent years, as observed recently in the case of Industroyer 2 and PIPEDREAM (aka INCONTROLLER).

Dr. Guri is no stranger to coming up with novel techniques to extract sensitive data from offline networks, with the researcher concocting four different approaches since the start of 2020 that leverage various side-channels to surreptitiously siphon information.

These include BRIGHTNESS (LCD screen brightness), POWER-SUPPLaY (power supply unit), AIR-FI (Wi-Fi signals), and LANtenna (Ethernet cables). The latest approach is no different, wherein it takes advantage of the Serial ATA cable to achieve the same goals.

Serial ATA is a bus interface and an Integrated Drive Electronics (IDE) standard that’s used to transfer data at higher rates to mass storage devices. One of its chief uses is to connect hard disk drives (HDD), solid-state drives (SSD), and optical drives (CD/DVD) to the computer’s motherboard.

Unlike breaching a traditional network by means of spear-phishing or watering holes, compromising an air-gapped network requires more complex strategies such as a supply chain attack, using removable media (e.g., USBStealer and USBFerry), or rogue insiders to plant malware.

For an adversary whose aim is to steal confidential information, financial data, and intellectual property, the initial penetration is only the start of the attack chain that’s followed by reconnaissance, data gathering, and data exfiltration through workstations that contain active SATA interfaces.

In the final data reception phase, the transmitted data is captured through a hidden receiver or relies on a malicious insider in an organization to carry a radio receiver near the air-gapped system. “The receiver monitors the 6GHz spectrum for a potential transmission, demodulates the data, decodes it, and sends it to the attacker,” Dr. Guri explained.

As countermeasures, it’s recommended to take steps to prevent the threat actor from gaining an initial foothold, use an external Radio frequency (RF) monitoring system to detect anomalies in the 6GHz frequency band from the air-gapped system, or alternatively polluting the transmission with random read and write operations when a suspicious covert channel activity is detected.

Source: The Hacker News

Can attackers create a face mask that would defeat modern facial recognition (FR) systems? A group of researchers from from Ben-Gurion University of the Negev and Tel Aviv University have proven that it can be done.

“We validated our adversarial mask’s effectiveness in real-world experiments (CCTV use case) by printing the adversarial pattern on a fabric face mask. In these experiments, the FR system was only able to identify 3.34% of the participants wearing the mask (compared to a minimum of 83.34% with other evaluated masks),” they noted.

A mask that works against many facial recognition models

The COVID-19 pandemic has made wearing face masks a habitual practice, and it initially hampered many facial recognition systems in use around the world. With time, though, the technology evolved and adapted to accurately identify individuals wearing medical and other masks.

But as we learned time and time again, if there is a good enough incentive, adversaries will always find new ways to achieve their intended goal.

In this particular case, the researchers took over the adversarial role and decided to find out whether they could create a specific pattern/mask that would work against modern deep learning-based FR models.

Their attempt was successful: they used a gradient-based optimization process to create a universal perturbation (and mask) that would falsely classify each wearer – no matter whether male or female – as an unknown identity, and would do so even when faced with different FR models.

This mask works as intended both when printed on paper and or fabric. But, even more importantly, the mask will not raise suspicion in our post-Covid world and can easily be removed when the adversary needs to blend in real-world scenarios.

Possible countermeasures

While their mask works well, theirs is not the only “version” possible.

“The main goal of a universal perturbation is to fit any person wearing it, i.e., there is a single pattern. Having said that, the perturbation depends on the FR model it was used to attack, which means different patterns will be crafted depending on the different victim models,” Alon Zolfi, the PhD student who led the research, told Help Net Security.

If randomization is added to the process, the resulting patterns can also be slightly different.

“Tailor made masks (fitting a single person) could also be crafted and result in different adversarial patterns to widen the diversity,” he noted.

Facial recognition models can be trained to recognize people wearing their and similar adversarial masks, the researchers pointed out. Alternatively, during the inference phase, every masked face image could be preprocessed so that it looks like the person is wearing a standard mask (e.g., blue surgical mask), because current FR models work well with those.

At the moment, FR systems rely on the entire facial area to answer a query whether two faces are of the same person, and Zolfi believes there are three solutions to make them “see through” a masked face image.

The first is adversarial learning, i.e., training FR models with facial images that contain adversarial patterns (whether universal or tailor-made).

The second is training FR models to make a prediction based only on the upper area of the face – but this approach has been shown to degrade the performance of the models even on unmasked facial images and is currently unsatisfactory, he noted.

Thirdly, FR models could be trained to generate lower facial area based on the upper facial area.

“There is a popular line of work called generative adversarial network (GAN) that is used to generate what we think of as ‘inputs’ (in this case, given some input we want it to output an image of the lower facial area). This is a ‘heavy’ approach because it requires completely different model architectures, training procedures and larger physical resources during inference,” he concluded.

Source: Help Net Security

SIMON MCGILL/GETTY IMAGES

Anything from a metallic Rubik’s cube to an aluminum trash can inside a room could give away your private conversations.

THE MOST PARANOID among us already know the checklist to avoid modern audio eavesdropping: Sweep your home or office for bugs. Put your phone in a Faraday bag—or a fridge. Consider even stripping internal microphones from your devices. Now one group of researchers offers a surprising addition to that list: Remove every lightweight, metallic object from the room that’s visible from a window.

At the Black Hat Asia hacker conference in Singapore this May, researchers from Israel’s Ben Gurion University of the Negev plan to present a new surveillance technique designed to allow anyone with off-the-shelf equipment to eavesdrop on conversations if they can merely find a line of sight through a window to any of a wide variety of reflective objects in a given room. By pointing an optical sensor attached to a telescope at one of those shiny objects—the researchers tested their technique with everything from an aluminum trash can to a metallic Rubik’s cube—they could detect visible vibrations on an object’s surface that allowed them to derive sounds and thus listen to speech inside the room. Unlike older experiments that similarly watched for minute vibrations to remotely listen in on a target, this new technique let researchers pick up lower-volume conversations, works with a far greater range of objects, and enables real-time snooping rather than after-the-fact reconstruction of a room’s audio.

“We can recover speech from lightweight, shiny objects placed in proximity to an individual who is speaking by analyzing the light reflected from them,” says Ben Nassi, the Ben Gurion professor who carried out the research along with Ras Swissa, Boris Zadov, and Yuval Elovici. “And the beauty of it is that we can do it in real time, which for espionage allows you to act on the information revealed in the content of the conversation.”

The researchers’ trick takes advantage of the fact that sound waves from speech create changes in air pressure that can imperceptibly vibrate objects in a room. In their experimental setup, they attached a photodiode, a sensor that converts light into voltage, to a telescope; the longer-range its lenses and the more light they allow to hit the sensor, the better. That photodiode was then connected to an analog-to-digital converter and a standard PC, which translated the sensor’s voltage output to data that represents the real-time fluctuations of the light reflecting from whatever object the telescope points at. The researchers could then correlate those tiny light changes to the object’s vibration in a room where someone is speaking, allowing them to reconstruct the nearby person’s speech.

The researchers showed that in some cases, using a high-end analog-to-digital converter, they could recover audible speech with their technique when a speaker is about 10 inches from a shiny metallic Rubik’s cube and speaking at 75 decibels, the volume of a loud conversation. With a powerful enough telescope, their method worked from a range of as much as 115 feet. Aside from the Rubik’s cube, they tested the trick with half a dozen objects: a silvery bird figurine, a small polished metal trash can, a less-shiny aluminum ice-coffee can, an aluminum smartphone standard, and even thin metal venetian blinds.

The recovered sound was clearest when using objects like the smartphone stand or trash can, and least clear with the venetian blinds—but still audible to make out every word in some cases. Nassi points out that the ability to capture sounds from window coverings is particularly ironic. “This is an object designed to increase privacy in a room,” Nassi says. “However, if you’re close enough to the venetian blinds, they can be exploited as diaphragms, and we can recover sound from them.”

The Ben Gurion researchers named their technique the Little Seal Bug in an homage to a notorious Cold War espionage incident known as the Great Seal Bug: In 1945, the USSR gave a gift of a wooden seal placard displaying the US coat of arms to the embassy in Moscow, which was discovered years later to contain an RFID spy bug that was undetectable to bug sweepers of that time. Nassi suggests that the Little Seal Bug technique could similarly work when a spy sends a seemingly innocuous gift of a metallic trophy or figurine to someone, which the eavesdropper can then exploit as an ultra-stealthy listening device. But Nassi argues it’s just as likely that a target has a suitable lightweight shiny object on their desk already, in view of a window and any optical snooper.

Nassi’s team isn’t the first to suggest that long-range, optical spying can pick up vocal conversations. In 2014, a team of MIT, Adobe, and Microsoft researchers created what they called the Visual Microphone, showing it was possible to analyze a video of a houseplant’s leaves or an empty potato chip bag inside a room to similarly detect vibrations and reconstruct sound. But Nassi says his researchers’ work can pick up lower-volume sounds and requires far less processing than the video analysis used by the Visual Microphone team. The Ben Gurion team found that using a photodiode was more effective and more efficient than using a camera, allowing easier long-range listening with a new range of objects and offering real-time results.

“This definitely takes a step toward something that’s more useful for espionage,” says Abe Davis, one of the former MIT researchers who worked on the Visual Microphone and is now at Cornell. He says he has always suspected that using a different sort of camera, purpose-built for this sort of optical eavesdropping, would advance the technique. “It’s like we invented the shotgun, and this work is like, ‘We improve on the shotgun, we give you a rifle,'” Davis says.

It’s still far from clear how practical the method will be in a real-world setting, says Thomas Ristenpart, another Cornell computer scientist who has long studied side-channel attacks—techniques like the Little Seal Bug that can extract secrets from unexpected side effects of communications. He points out that even the 75-decibel words the Israeli researchers detected in their tests would be relatively loud, and background noise from an air conditioner, music, or other speakers in the room might interfere with the technique. “But as a proof of concept, it’s still interesting,” Ristenpart says.

Ben Gurion’s Ben Nassi argues, though, that the technique has proven to work well enough that an intelligence agency with a budget in the millions of dollars rather than mere thousands his team spent could likely hone their spy method into a practical and powerful tool. In fact, he says, they may have already. “This is something that could have been exploited many years ago—and probably was exploited for many years,” says Nassi. “The things we’re revealing to the public probably have already been used by clandestine agencies for a long time.”

All of which means that anyone with secrets to keep would be wise to sweep their desk for shiny objects that might serve as inadvertent spy bugs. Or lower the window shades—just not the venetian blinds.

Source: WIRED

Good news, everyone! Security researcher [Mordechai Guri] has given us yet another reason to look askance at our computers and wonder who might be sniffing in our private doings.

This time, your suspicious gaze will settle on the lowly Ethernet cable, which he has used to exfiltrate data across an air gap. The exploit requires almost nothing in the way of fancy hardware — he used both an RTL-SDR dongle and a HackRF to receive the exfiltrated data, and didn’t exactly splurge on the receiving antenna, which was just a random chunk of wire. The attack, dubbed “LANtenna”, does require some software running on the target machine, which modulates the desired data and transmits it over the Ethernet cable using one of two methods: by toggling the speed of the network connection, or by sending raw UDP packets. Either way, an RF signal is radiated by the Ethernet cable, which was easily received and decoded over a distance of at least two meters. The bit rate is low — only a few bits per second — but that may be all a malicious actor needs to achieve their goal.

To be sure, this exploit is quite contrived, and fairly optimized for demonstration purposes. But it’s a pretty effective demonstration, but along with the previously demonstrated hard drive activity lightspower supply fans, and even networked security cameras, it adds another seemingly innocuous element to the list of potential vectors for side-channel attacks.

[via The Register]

Source: hackaday.com

A new study used digitally and physically applied makeup to test the limits of state-of-the-art facial recognition software.

YURI KADOBNOV / GETTY IMAGES

Researchers have found a new and surprisingly simple method for bypassing facial recognition software using makeup patterns. 

new study from Ben-Gurion University of the Negev found that software-generated makeup patterns can be used to consistently bypass state-of-the-art facial recognition software, with digitally and physically-applied makeup fooling some systems with a success rate as high as 98 percent.

In their experiment, the researchers defined their 20 participants as blacklisted individuals so their identification would be flagged by the system. They then used a selfie app called YouCam Makeup to digitally apply makeup to the facial images according to the heatmap which targets the most identifiable regions of the face.. A makeup artist then emulated the digital makeup onto the participants using natural-looking makeup in order to test the target model’s ability to identify them in a realistic situation.

“​​I was surprised by the results of this study,” Nitzan Guettan, a doctoral student and lead author of the study, told Motherboard. “[The makeup artist] didn’t do too much tricks, just see the makeup in the image and then she tried to copy it into the physical world. It’s not a perfect copy there. There are differences but it still worked.”

The researchers tested the attack method in a simulated real-world scenario in which participants wearing the makeup walked through a hallway to see whether they would be detected by a facial recognition system. The hallway was equipped with two live cameras that streamed to the MTCNN face detector while evaluating the system’s ability to identify the participant.

“Our attacker assumes a black-box scenario, meaning that the attacker cannot access the target FR model, its architecture, or any of its parameters,” the paper explains. “Therefore, [the] attacker’s only option is to alter his/her face before being captured by the cameras that feeds the input to the target FR model.” 

The experiment saw 100 percent success in the digital experiments on both the FaceNet model and the LResNet model, according to the paper. In the physical experiments, the participants were detected in 47.6 percent of the frames if they weren’t wearing any makeup and 33.7 percent of the frames if they wore randomly applied makeup. Using the researchers’ method of applying makeup to the highly identifiable parts of the attacker’s face, they were only recognized in 1.2 percent of the frames. 

The researchers are not the first to demonstrate how makeup can be used to fool facial recognition systems. In 2010, artist Adam Harvey’s CV Dazzle project presented a host of makeup looks designed to thwart algorithms, inspired by “dazzle” camouflage used by naval vessels in World War I.

Various studies have shown how facial recognition systems can be bypassed digitally, such as by creating “master faces” that could impersonate others. The paper references a study where a printable sticker was attached to a hat to bypass the facial recognition system, and another where eyeglass frames were printed

While all of these methods might hide someone from facial recognition algorithms, they have the side effect of making you very visible to other humans—especially if attempted somewhere with high security, like an airport.

In the researchers’ experiment, they addressed this by having the makeup artist only use conventional makeup techniques and neutral color palettes to achieve a natural look. Considering its success in the study, the researchers say this method could technically be replicated by anyone using store bought makeup. 

Perhaps unsurprisingly, Guettan says she generally does not trust facial recognition technology in its current state. “I don’t even use it on my iPhone,” she told Motherboard. “There are a lot of problems with this domain of facial recognition. But I think the technology is becoming better and better.”

Source: vice.com

A new class of passive TEMPEST attack converts LED output into intelligible audio.

Researchers at Ben-Gurion University of the Negev have demonstrated a novel way to spy on electronic conversations. A new paper released today outlines a novel passive form of the TEMPEST attack called Glowworm, which converts minute fluctuations in the intensity of power LEDs on speakers and USB hubs back into the audio signals that caused those fluctuations.

The Cyber@BGU team—consisting of Ben Nassi, Yaron Pirutin, Tomer Gator, Boris Zadov, and Professor Yuval Elovici—analyzed a broad array of widely used consumer devices including smart speakers, simple PC speakers, and USB hubs. The team found that the devices’ power indicator LEDs were generally influenced perceptibly by audio signals fed through the attached speakers.

Although the fluctuations in LED signal strength generally aren’t perceptible to the naked eye, they’re strong enough to be read with a photodiode coupled to a simple optical telescope. The slight flickering of power LED output due to changes in voltage as the speakers consume electrical current are converted into an electrical signal by the photodiode; the electrical signal can then be run through a simple Analog/Digital Converter (ADC) and played back directly.

A novel passive approach

With sufficient knowledge of electronics, the idea that a device’s supposedly solidly lit LEDs will “leak” information about what it’s doing is straightforward. But to the best of our knowledge, the Cyber@BGU team is the first to both publish the idea and prove that it works empirically.

The strongest features of the Glowworm attack are its novelty and its passivity. Since the approach requires absolutely no active signaling, it would be immune to any sort of electronic countermeasure sweep. And for the moment, a potential target seems unlikely to either expect or deliberately defend against Glowworm—although that might change once the team’s paper is presented later this year at the CCS 21 security conference.

The attack’s complete passivity distinguishes it from similar approaches—a laser microphone can pick up audio from the vibrations on a window pane. But defenders can potentially spot the attack using smoke or vapor—particularly if they know the likely frequency ranges an attacker might use.

Glowworm requires no unexpected signal leakage or intrusion even while actively in use, unlike “The Thing.” The Thing was a Soviet gift to the US Ambassador in Moscow, which both required “illumination” and broadcast a clear signal while illuminated. It was a carved wooden copy of the US Great Seal, and it contained a resonator that, if lit up with a radio signal at a certain frequency (“illuminating” it), would then broadcast a clear audio signal via radio. The actual device was completely passive; it worked a lot like modern RFID chips (the things that squawk when you leave the electronics store with purchases the clerk forgot to mark as purchased).

Accidental defense

Despite Glowworm’s ability to spy on targets without revealing itself, it’s not something most people will need to worry much about. Unlike the listening devices we mentioned in the section above, Glowworm doesn’t interact with actual audio at all—only with a side effect of electronic devices that produce audio.

This means that, for example, a Glowworm attack used successfully to spy on a conference call would not capture the audio of those actually in the room—only of the remote participants whose voices are played over the conference room audio system.

The need for a clean line of sight is another issue that means that most targets will be defended from Glowworm entirely by accident. Getting a clean line of sight to a windowpane for a laser microphone is one thing—but getting a clean line of sight to the power LEDs on a computer speaker is another entirely.

Humans generally prefer to face windows themselves for the view and have the LEDs on devices face them. This leaves the LEDs obscured from a potential Glowworm attack. Defenses against simple lip-reading—like curtains or drapes—are also effective hedges against Glowworm, even if the targets don’t actually know Glowworm might be a problem.

Finally, there’s currently no real risk of a Glowworm “replay” attack using video that includes shots of vulnerable LEDs. A close-range, 4k at 60 fps video might just barely capture the drop in a dubstep banger—but it won’t usefully recover human speech, which centers between 85Hz-255Hz for vowel sounds and 2KHz-4KHz for consonants.

Turning out the lights

Although Glowworm is practically limited by its need for clear line of sight to the LEDs, it works at significant distance. The researchers recovered intelligible audio at 35 meters—and in the case of adjoining office buildings with mostly glass facades, it would be quite difficult to detect.

For potential targets, the simplest fix is very simple indeed—just make sure that none of your devices has a window-facing LED. Particularly paranoid defenders can also mitigate the attack by placing opaque tape over any LED indicators that might be influenced by audio playback.

On the manufacturer’s side, defeating Glowworm leakage would also be relatively uncomplicated—rather than directly coupling a device’s LEDs to the power line, the LED might be coupled via an opamp or GPIO port of an integrated microcontroller. Alternatively (and perhaps more cheaply), relatively low-powered devices could damp power supply fluctuations by connecting a capacitor in parallel to the LED, acting as a low-pass filter.

For those interested in further details of both Glowworm and its effective mitigation, we recommend visiting the researchers’ website, which includes a link to the full 16-page white paper.

Source: Ars Technica

The selected consortium, which included a number of American and Israeli companies and universities, will receive a total of $6 million, taking Center’s total investment to $67.2 million

The U.S. Department of Energy (DOE) along with its Israeli counterpart, the Ministry of Energy and the Israel Innovation Authority announced on Tuesday the winner of a government-funding award amounting to $6 million in funding on behalf of the U.S.-Israel Energy Center for ensuring cybersecurity of energy infrastructure.

The award follows the selection in March of last year for similar grants in the fields of energy storage, fossil energy, and energy-water conservation. The total value of investments could reach up to $12 million over a period of three years, and the total value of all four programs could reach up to $67.2 million.

The project will work toward ensuring critical energy assets and infrastructure

The selected consortium was led by Arizona State University and Ben-Gurion University who will perform research and development. Their project was entitled, “Comprehensive Cybersecurity Technology for Critical Power Infrastructure AI Based Centralized Defense and Edge Resilience,” and includes the following partners: the Georgia Tech Research Corporation, Nexant, Delek US Holdings Inc., Duquesne Light Company, Schweitzer Engineering Laboratories, the MITRE Corporation, Arizona Public Service, OTORIO, Rad Data Communication, SIGA OT Solutions, and Arava Power.

The U.S.-Israel Energy Center of Excellence in Energy, Engineering and Water Technology was initially authorized by Congress as part of the U.S.-Israel Strategic Partnership Act of 2014 and has been funded by the Israeli government since 2016. Total government funding is expected to total $40 million for the next five years to promote energy security and economic development through research and development of innovative energy technologies, while facilitating cooperation collaborations between both countries’ companies, research institutes, and universities. The Energy Center is managed by the BIRD Foundation.“Cybersecurity for energy infrastructure is key to deploying new innovative technologies to combat the climate crisis, promote energy justice, and create new clean energy jobs. It is vital that we ensure the security and reliability of critical energy infrastructure, as well as protecting U.S. assets. I am pleased that this international consortium will develop new tools to address the cybersecurity threats we will face as we invest in our people, supply chains, and the capacity to meet our clean energy goals,” said Dr. Andrew Light, who serves as Assistant Secretary for International Affairs (Acting) at the U.S. Department of Energy.“The Ministry of Energy is strongly involved in protecting the water and energy sector from cyberattacks, and believes that investing in research and development is just as important,” said

Udi Adiri, who serves as the Director-General at the Israel Ministry of Energy.Dr. Ami Appelbaum, Chairman of the Israel Innovation Authority and Chief Scientist at the Ministry of Economy and Industry also commented: “In an age where technological innovations are multiplying exponentially, the risks of cyberattacks are also increasing significantly, especially in critical facilities such as energy infrastructure. We are pleased to see the high level of engagement in both countries, and look forward to the amazing changes they will bring about to ensure the security of the energy sector and the population worldwide.”

Source: CTECH

The research highlights the potential dangers of new ‘biohacking’ techniques.

A new form of cyberattack has been developed which highlights the potential future ramifications of digital assaults against the biological research sector.

On Monday, academics from the Ben-Gurion University of the Negev described how “unwitting” biologists and scientists could become victims of cyberattacks designed to take biological warfare to another level. 

At a time where scientists worldwide are pushing ahead with the development of potential vaccines to combat the COVID-19 pandemic, Ben-Gurion’s team says that it is no longer the case that a threat actor needs physical access to a “dangerous” substance to produce or deliver it — instead, scientists could be duped into producing toxins or synthetic viruses on their behalf through targeted cyberattacks. 

The research, “Cyberbiosecurity: Remote DNA Injection Threat in Synthetic Biology,” has been recently published in the academic journal Nature Biotechnology.

The attack documents how malware, used to infiltrate a biologist’s computer, could replace sub-strings in DNA sequencing. Specifically, weaknesses in the Screening Framework Guidance for Providers of Synthetic Double-Stranded DNA and Harmonized Screening Protocol v2.0 systems “enable protocols to be circumvented using a generic obfuscation procedure.”

When DNA orders are made to synthetic gene providers, US Department of Health and Human Services (HHS) guidance requires screening protocols to be in place to scan for potentially harmful DNA. 

However, it was possible for the team to circumvent these protocols through obfuscation, in which 16 out of 50 obfuscated DNA samples were not detected against ‘best match’ DNA screening. 

Software used to design and manage synthetic DNA projects may also be susceptible to man in-the-browser attacks that can be used to inject arbitrary DNA strings into genetic orders, facilitating what the team calls an “end-to-end cyberbiological attack.”

The synthetic gene engineering pipeline offered by these systems can be tampered with in browser-based attacks. Remote hackers could use malicious browser plugins, for example, to “inject obfuscated pathogenic DNA into an online order of synthetic genes.”

In a case demonstrating the possibilities of this attack, the team cited residue Cas9 protein, using malware to transform this sequence into active pathogens. Cas9 protein, when using CRISPR protocols, can be exploited to “deobfuscate malicious DNA within the host cells,” according to the team.

For an unwitting scientist processing the sequence, this could mean the accidental creation of dangerous substances, including synthetic viruses or toxic material. 

“To regulate both intentional and unintentional generation of dangerous substances, most synthetic gene providers screen DNA orders which is currently the most effective line of defense against such attacks,” commented Rami Puzis, head of the BGU Complex Networks Analysis Lab. “Unfortunately, the screening guidelines have not been adapted to reflect recent developments in synthetic biology and cyberwarfare.”

A potential attack chain is outlined below:

“This attack scenario underscores the need to harden the synthetic DNA supply chain with protections against cyber-biological threats,” Puzis added. “To address these threats, we propose an improved screening algorithm that takes into account in vivo gene editing.”

Source: ZDNet

Researchers found they could stop a Tesla by flashing a few frames of a stop sign for less than half a second on an internet-connected billboard.

SAFETY CONCERNS OVER automated driver-assistance systems like Tesla’s usually focus on what the car can’t see, like the white side of a truck that one Tesla confused with a bright sky in 2016, leading to the death of a driver. But one group of researchers has been focused on what autonomous driving systems might see that a human driver doesn’t—including “phantom” objects and signs that aren’t really there, which could wreak havoc on the road.

Researchers at Israel’s Ben Gurion University of the Negev have spent the last two years experimenting with those “phantom” images to trick semi-autonomous driving systems. They previously revealed that they could use split-second light projections on roads to successfully trick Tesla’s driver-assistance systems into automatically stopping without warning when its camera sees spoofed images of road signs or pedestrians. In new research, they’ve found they can pull off the same trick with just a few frames of a road sign injected on a billboard’s video. And they warn that if hackers hijacked an internet-connected billboard to carry out the trick, it could be used to cause traffic jams or even road accidents while leaving little evidence behind.

“The attacker just shines an image of something on the road or injects a few frames into a digital billboard, and the car will apply the brakes or possibly swerve, and that’s dangerous,” says Yisroel Mirsky, a researcher for Ben Gurion University and Georgia Tech who worked on the research, which will be presented next month at the ACM Computer and Communications Security conference. “The driver won’t even notice at all. So somebody’s car will just react, and they won’t understand why.”

In their first round of research, published earlier this year, the team projected images of human figures onto a road, as well as road signs onto trees and other surfaces. They found that at night, when the projections were visible, they could fool both a Tesla Model X running the HW2.5 Autopilot driver-assistance system—the most recent version available at the time, now the second-most-recent —and a Mobileye 630 device. They managed to make a Tesla stop for a phantom pedestrian that appeared for a fraction of a second, and tricked the Mobileye device into communicating the incorrect speed limit to the driver with a projected road sign.

In this latest set of experiments, the researchers injected frames of a phantom stop sign on digital billboards, simulating what they describe as a scenario in which someone hacked into a roadside billboard to alter its video. They also upgraded to Tesla’s most recent version of Autopilot known as HW3. They found that they could again trick a Tesla or cause the same Mobileye device to give the driver mistaken alerts with just a few frames of altered video.

The researchers found that an image that appeared for 0.42 seconds would reliably trick the Tesla, while one that appeared for just an eighth of a second would fool the Mobileye device. They also experimented with finding spots in a video frame that would attract the least notice from a human eye, going so far as to develop their own algorithm for identifying key blocks of pixels in an image so that a half-second phantom road sign could be slipped into the “uninteresting” portions. And while they tested their technique on a TV-sized billboard screen on a small road, they say it could easily be adapted to a digital highway billboard, where it could cause much more widespread mayhem.

The Ben Gurion researchers are far from the first to demonstrate methods of spoofing inputs to a Tesla’s sensors. As early as 2016, one team of Chinese researchers demonstrated they could spoof and even hide objects from Tesla’s sensors using radio, sonic, and light-emitting equipment. More recently, another Chinese team found they could exploit Tesla’s lane-follow technology to trick a Tesla into changing lanes just by planting cheap stickers on a road.

“Somebody’s car will just react, and they won’t understand why.”

YISROEL MIRSKY, BEN GURION UNIVERSITY

But the Ben Gurion researchers point out that unlike those earlier methods, their projections and hacked billboard tricks don’t leave behind physical evidence. Breaking into a billboard in particular can be performed remotely, as plenty of hackers have previously demonstrated. The team speculates that the phantom attacks could be carried out as an extortion technique, as an act of terrorism, or for pure mischief. “Previous methods leave forensic evidence and require complicated preparation,” says Ben Gurion researcher Ben Nassi. “Phantom attacks can be done purely remotely, and they do not require any special expertise.”

Neither Mobileye nor Tesla responded to WIRED’s request for comment. But in an email to the researchers themselves last week, Tesla made a familiar argument that its Autopilot feature isn’t meant to be a fully autonomous driving system. “Autopilot is a driver assistance feature that is intended for use only with a fully attentive driver who has their hands on the wheel and is prepared to take over at any time,” reads Tesla’s response. The Ben Gurion researchers counter that Autopilot is used very differently in practice. “As we know, people use this feature as an autopilot and do not keep 100 percent attention on the road while using it,” writes Mirsky in an email. “Therefore, we must try to mitigate this threat to keep people safe, regardless of [Tesla’s] warnings.”

Tesla does have a point, though not one that offers much consolation to its own drivers. Tesla’s Autopilot system depends largely on cameras and, to a lesser extent, radar, while more truly autonomous vehicles like those developed by Waymo, Uber, or GM-owned autonomous vehicle startup Cruise also integrate laser-based lidar, points out Charlie Miller, the lead autonomous vehicle security architect at Cruise. “Lidar would not have been susceptible to this type of attack,” says Miller. “You can change an image on a billboard and lidar doesn’t care, it’s measuring distance and velocity information. So these attacks wouldn’t have worked on most of the truly autonomous cars out there.”

The Ben Gurion researchers didn’t test their attacks against those other, more multi-sensor setups. But they did demonstrate ways to detect the phantoms they created even on a camera-based platform. They developed a system they call “Ghostbusters” that’s designed to take into account a collection of factors like depth, light, and the context around a perceived traffic sign, then weigh all those factors before deciding whether a road sign image is real. “It’s like a committee of experts getting together and deciding based on very different perspectives what this image is, whether it’s real or fake, and then making a collective decision,” says Mirsky. The result, the researchers say, could far more reliably defeat their phantom attacks, without perceptibly slowing down a camera-based autonomous driving system’s reactions.

Ben Gurion’s Nassi concedes that the Ghostbuster system isn’t perfect, and he argues that their phantom research shows the inherent difficulty in making autonomous driving decisions even with multiple sensors like a Tesla’s combined radar and camera. Tesla, he says, has taken a “better safe than sorry” approach that trusts the camera alone if it shows an obstacle or road sign ahead, leaving it vulnerable to their phantom attacks. But an alternative might disregard hazards if one or more of a vehicle’s sensors misses them. “If you implement a system that ignores phantoms if they’re not validated by other sensors, you will probably have some accidents,” says Nassi. “Mitigating phantoms comes with a price.”

Cruise’s Charlie Miller, who previously worked on autonomous vehicle security at Uber and Chinese self-driving car firm Didi Chuxing, counters that truly autonomous, lidar-enabled vehicles have in fact managed to solve that problem. “Attacks against sensor systems are interesting, but this isn’t a serious attack against the systems I’m familiar with,” such as Uber and Cruise vehicles, Miller says. But he still sees value in Ben Gurion’s work. “It’s something we need to think about and work on and plan for. These cars rely on their sensor inputs, and we need to make sure they’re trusted.”

Source: Wired.com

About Us

Cyber@BGU is an umbrella organization at Ben Gurion University, being home to various cyber security, big data analytics and AI applied research activities.Residing in newly established R&D center at the new Hi-Tech park of Beer Sheva (Israel’s Cyber Capital), Cyber@BGU serves as a platform for the most innovative and technologically challenging projects with various industrial and governmental partners.

Latest Publications

The Creation and Detection of Deepfakes: A Survey

Yisroel Mirsky, Wenke Lee

Ben-Gurion University and Georgia Institute of Technology, May 2020

The Creation and Detection of Deepfakes: A Survey

Yisroel Mirsky, Wenke Lee

Ben-Gurion University and Georgia Institute of Technology, May 2020

A deepfake is content generated by artificial intelligence which seems authentic in the eyes of a human being. The word deepfake is a combination of the words ‘deep learning’ and ‘fake’ and primarily relates to content generated by an artificial neural network, a branch of machine learning.

The most common form of deepfakes involves the generation and manipulation of human imagery. This technology has creative and productive applications. For example, realistic video dubbing of foreign films, education through the reanimation of historical figures, and virtually trying on clothes while shopping. There are also numerous online communities devoted to creating deepfake memes for entertainment, such as music videos portraying the face of actor Nicolas Cage.

However, despite the positive applications of deepfakes, the technology is infamous for its unethical and malicious capabilities. At the end of 2017, a Reddit user by the name of ‘deepfakes’ used deep learning to swap faces of celebrities into pornographic videos and posted them online.

Link

Deployment Optimization of IoT Devices through Attack Graph Analysis

Noga Agmon, Asaf Shabtai, Rami Puzis

Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, 11 Apr 2019

Deployment Optimization of IoT Devices through Attack Graph Analysis

Noga Agmon, Asaf Shabtai, Rami Puzis

Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, 11 Apr 2019

The Internet of things (IoT) has become an integral part of our life
at both work and home. However, these IoT devices are prone to vulnerability exploits due to their low cost, low resources, the diversity
of vendors, and proprietary firmware. Moreover, short range communication protocols (e.g., Bluetooth or ZigBee) open additional
opportunities for the lateral movement of an attacker within an organization. Thus, the type and location of IoT devices may significantly
change the level of network security of the organizational network.
In this paper, we quantify the level of network security based on
an augmented attack graph analysis that accounts for the physical
location of IoT devices and their communication capabilities. We
use the depth-first branch and bound (DFBnB) heuristic search algorithm to solve two optimization problems: Full Deployment with
Minimal Risk (FDMR) and Maximal Utility without Risk Deterioration (MURD). An admissible heuristic is proposed to accelerate the
search. The proposed method is evaluated using a real network with
simulated deployment of IoT devices. The results demonstrate (1)
the contribution of the augmented attack graphs to quantifying the
impact of IoT devices deployed within the organization on security,
and (2) the effectiveness of the optimized IoT deployment.

Link

CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning

Yisroel Mirsky, Tom Mahler, Ilan Shelef, Yuval Elovici

Department of Information Systems Engineering, Ben-Gurion University, Israel Soroka University Medical Center. 3 Apr 2019

CT-GAN: Malicious Tampering of 3D Medical Imagery using Deep Learning

Yisroel Mirsky, Tom Mahler, Ilan Shelef, Yuval Elovici

Department of Information Systems Engineering, Ben-Gurion University, Israel Soroka University Medical Center. 3 Apr 2019

In 2018, clinics and hospitals were hit with numerous attacks
leading to significant data breaches and interruptions in
medical services. An attacker with access to medical records
can do much more than hold the data for ransom or sell it on
the black market.
In this paper, we show how an attacker can use deeplearning to add or remove evidence of medical conditions
from volumetric (3D) medical scans. An attacker may perform
this act in order to stop a political candidate, sabotage research,
commit insurance fraud, perform an act of terrorism, or
even commit murder. We implement the attack using a 3D
conditional GAN and show how the framework (CT-GAN)
can be automated. Although the body is complex and 3D
medical scans are very large, CT-GAN achieves realistic
results which can be executed in milliseconds.
To evaluate the attack, we focused on injecting and
removing lung cancer from CT scans. We show how three
expert radiologists and a state-of-the-art deep learning AI are
highly susceptible to the attack. We also explore the attack
surface of a modern radiology network and demonstrate one
attack vector: we intercepted and manipulated CT scans in an
active hospital network with a covert penetration test.

Link

Analysis of Location Data Leakage in the Internet Traffic of Android-based Mobile Devices

Nir Sivan, Ron Bitton, Asaf Shabtai

Department of Software and Information Systems Engineering Ben-Gurion University of the Negev. 12 Dec 2018

Analysis of Location Data Leakage in the Internet Traffic of Android-based Mobile Devices

Nir Sivan, Ron Bitton, Asaf Shabtai

Department of Software and Information Systems Engineering Ben-Gurion University of the Negev. 12 Dec 2018

In recent years we have witnessed a shift towards personalized, context-based applications and services for mobile device users. A key component of many of these services is the ability to infer the current location and predict the future location of users based on location sensors embedded in the devices. Such knowledge enables service providers to present relevant and timely offers to their users and better manage traffic congestion control, thus increasing customer satisfaction and engagement. However, such services suffer from location data leakage which has become one of today’s most concerning privacy issues for smartphone users.

BGU researchers focused specifically on location data that is exposed by Android applications via Internet network traffic in plaintext (i.e., without encryption) without the user’s awareness. An empirical evaluation, involving the network traffic of real mobile device users, aimed at: (1) measuring the extent of location data leakage in the Internet traffic of Android-based smartphone devices; and (2) understanding the value of this data by inferring users’ points of interests (POIs).

The key findings of this research center on the extent of this phenomenon in terms of both ubiquity and severity.

Link

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