A Global Positioning System (GPS) spoofing attack can be launched against any commercial GPS sensor in order to interfere with its navigation capabilities. These sensors are installed in a variety of devices and vehicles (e.g., cars, planes, cell phones, ships, UAVs, and more). In this study, we focus on micro UAVs (drones) for several reasons: (1) they are small and inexpensive, (2) they rely on a built-in camera, (3) they use GPS sensors, and (4) it is difficult to add external components to micro UAVs. We propose an innovative method, based on the video stream captured by a drone’s camera, for the real-time detection of GPS spoofing attacks targeting drones. The proposed method collects frames from the video stream and their location (GPS coordinates); by calculating the correlation between each frame, our method can detect GPS spoofing attacks on drones. We first analyze the performance of the suggested method in a controlled environment by conducting experiments on a flight simulator that we developed. Then, we analyze its performance in the real world using a DJI drone. Our method can provide different levels of security against GPS spoofing attacks, depending on the detection interval required; for example, it can provide a high level of security to a drone flying at altitudes of 50–100 m over an urban area at an average speed of 4 km/h in conditions of low ambient light; in this scenario, the proposed method can provide a level of security that detects any GPS spoofing attack in which the spoofed location is a distance of 1–4 m (an average of 2.5 m) from the real location.

In the area of espionage between countries, an infiltration covert channel used to trigger a silent malware installed on a network of a critical organization (such as 911 services, missile launching facility, etc.) from the outside world is extremely dangerous to the target country’s security. In order to prevent attackers from establishing such a channel, these organizations take various steps to secure their networks, to make the establishment of this type of covert channel very challenging and almost impractical to achieve; current state of the art methods are very limited and ineffective. In this paper, we show that even a strong isolation technique, such as air-gapping the network, can be circumvented by using an organizational multi function printer (MFP) to establish an infiltration covert channel in order to communicate with a malware installed on an isolated organization from the outside. We show how an attacker can leverage the light sensitivity of an MFP and use different light sources to infiltrate commands to the malware in the organization. We analyze the influence of light intensity, distance, transmission rate, ambient light, and wavelength on the covert channel. In addition we demonstrate the attack on a real organization using: (1) a laser attached to a tripod stand, (2) a laser carried by a drone, and (3) a hijacked smart bulb that is not even connected to the organization’s network and is accessed and controlled by an attacker in a passing car. We prove that locating the scanner in an inner room inside an organization doesn’t prevent an attacker from establishing the covert channel. We show how our covert channel can be established from a greater distance (900 meters) and at a higher transmission rate of 200 BPS (bits per second), than other methods used to infiltrate data to an organization, even using invisible light (covertly).

Although power LEDs have been integrated in various devices that perform cryptographic operations for decades, the cryptanalysis risk they pose has not yet been investigated. In this paper, we present optical cryptanalysis, a new form of cryptanalytic side-channel attack, in which secret keys are extracted by using a photodiode to measure the light emitted by a device's power LED and analyzing subtle fluctuations in the light intensity during cryptographic operations. We analyze the optical leakage of power LEDs of various consumer devices and the factors that affect the optical SNR. We then demonstrate end-to-end optical cryptanalytic attacks against a range of consumer devices (smartphone, smartcard, and Raspberry Pi, along with their USB peripherals) and recover secret keys (RSA, ECDSA, SIKE) from prior and recent versions of popular cryptographic libraries (GnuPG, Libgcrypt, PQCrypto-SIDH) from a maximum distance of 25 meters.

Driving under the influence of alcohol is a widespread phenomenon in the US where it is considered a major cause of fatal accidents. In this research, we present Virtual Breathalyzer, a novel approach for detecting intoxication from the measurements obtained by the sensors of smartphones and wrist-worn devices. We formalize the problem of intoxication detection as the supervised machine learning task of binary classification (drunk or sober). In order to evaluate our approach, we conducted a field experiment and collected 60 free gait samples from 30 patrons of three bars using a Microsoft Band and Samsung Galaxy S4. We validated our results against an admissible breathalyzer used by the police. A system based on this concept successfully detected intoxication and achieved the following results: 0.97 AUC and 0.04 FPR, given a fixed TPR of 1.0. Our approach can be used to analyze the free gait of drinkers when they walk from the car to the bar and vice versa, using wearable devices which are ubiquitous and more widespread than admissible breathalyzers. This approach can be utilized to alert people, or even a connected car, and prevent people from driving under the influence of alcohol.