Photoelectric sensors are utilized in a range of safety-critical applications, such as medical devices and autonomous vehicles. However, the public exposure of the input channel of a photoelectric sensor makes it vulnerable to malicious inputs. Several studies have suggested possible attacks on photoelectric sensors by injecting malicious signals. While a few defense techniques have been proposed against such attacks, they could be either bypassed or used for limited purposes. In this study, we propose Lightbox, a novel defense system to detect sensor attacks on photoelectric sensors based on signal fingerprinting. Lightbox uses the spectrum of the received light as a feature to distinguish the attacker’s malicious signals from the authentic signal, which is a signal from the sensor’s light source. We evaluated Lightbox against (1) a saturation attacker, (2) a simple spoofing attacker, and (3) a sophisticated attacker who is aware of Lightbox and can combine multiple light sources to mimic the authentic light source. Lightbox achieved the overall accuracy over 99% for the saturation attacker and simple spoofing attacker, and robustness against a sophisticated attacker. We also evaluated Lightbox considering various environments such as transmission medium, background noise, and input waveform. Finally, we demonstrate the practicality of Lightbox with experiments using a single-board computer after further reducing the training time.

Fire alarm and signaling systems are a networked system of fire detectors, fire control units, automated fire extinguishers, and fire notification appliances. Malfunction of these safety-critical cyber-physical systems may lead to chaotic evacuations, property damage, and even loss of human life. Therefore, reliability is one of the most crucial factors for fire detectors. Indeed, even a single report of a fire cannot be ignored, considering the importance of early fire detection and suppression. In this article, we show that wide-area smoke detectors, which are globally installed in critical infrastructures such as airports, sports facilities, and auditoriums, have significant vulnerabilities in terms of reliability; one can remotely and stealthily induce false fire alarms and suppress real fire alarms with a minimal attacker capability using simple equipment. The practicality and generalizability of these vulnerabilities has been assessed based on the demonstration of two types of sensor attacks on two commercial off-the-shelf optical beam smoke detectors from different manufacturers. Further, the practical considerations of building stealthy attack equipment has been analyzed, and an extensive survey of almost all optical beam smoke detectors on the market has been conducted. In addition, we show that the current standards of the fire alarm network connecting the detector and a control unit exacerbate the problem, making it impossible or very difficult to mitigate the threats we found. Finally, we discuss hardware- and software-based possible countermeasures for both wide-area smoke detectors and the fire alarm network; the effectiveness of one of the countermeasures is experimentally evaluated.

The consumer drone market is booming. Consumer drones are predominantly used for aerial photography; however, their use has been expanding because of their autopilot technology. Unfortunately, terrorists have also begun to use consumer drones for kamikaze bombing and reconnaissance. To protect against such threats, several companies have started “anti-drone” services that primarily focus on disrupting or incapacitating drone operations. However, the approaches employed are inadequate, because they make any drone that has intruded stop and remain over the protected area. We specify this issue by introducing the concept of safe-hijacking , which enables a hijacker to expel the intruding drone from the protected area remotely. As a safe-hijacking strategy, we investigated whether consumer drones in the autopilot mode can be hijacked via adaptive GPS spoofing. Specifically, as consumer drones activate GPS fail-safe and change their flight mode whenever a GPS error occurs, we performed black- and white-box analyses of GPS fail-safe flight mode and the following behavior after GPS signal recovery of existing consumer drones. Based on our analyses results, we developed a taxonomy of consumer drones according to these fail-safe mechanisms and designed safe-hijacking strategies for each drone type. Subsequently, we applied these strategies to four popular drones: DJI Phantom 3 Standard, DJI Phantom 4, Parrot Bebop 2, and 3DR Solo. The results of field experiments and software simulations verified the efficacy of our safe-hijacking strategies against these drones and demonstrated that the strategies can force them to move in any direction with high accuracy.

In the Bitcoin system, participants are rewarded for solving cryptographic puzzles. In order to receive more consistent rewards over time, some participants organize mining pools and split the rewards from the pool in proportion to each participant's contribution. However, several attacks threaten the ability to participate in pools. The block withholding (BWH) attack makes the pool reward system unfair by letting malicious participants receive unearned wages while only pretending to contribute work. When two pools launch BWH attacks against each other, they encounter the miner's dilemma: in a Nash equilibrium, the revenue of both pools is diminished. In another attack called selfish mining, an attacker can unfairly earn extra rewards by deliberately generating forks.