Robotic Vehicles (RV) rely extensively on sensor inputs to operate autonomously. Physical attacks such as sensor tampering and spoofing can feed erroneous sensor measurements to deviate RVs from their course and result in mission failures. In this paper, we present a Feed-Forward Controller based framework for automatically recovering RVs from physical attacks. We use machine learning (ML) to design an attack resilient Feed-Forward Controller (FFC), which runs in tandem with the RV's primary controller and monitors it. Under attacks, the FFC takes over from the RV's primary controller to recover the RV, and allows the RV to complete its mission successfully. Our evaluation on 6 RV systems including 3 real RVs shows that our proposed framework prevents crashes and allows RVs to complete their missions successfully despite attacks in 86% of the cases. Further, we propose designs to streamline the implementation of the FFC-based recovery and its application in new RV systems.

Physical attacks such as sensor spoofing and tampering are a growing concern for Robotic Autonomous Vehicles (RAV) such as drones and rovers. Studying the impact of these attacks and developing defense techniques is challenging, as it requires sophisticated signal injection hardware. Consequently, prior work in RAV security simulates physical attacks through software by injecting bias into sensors. However, the absence of a standardized method for attack injection compels researchers to use custom approaches. This lack of uniformity leads to challenges in reproducibility and, at times, questionable claims.We present RAVAGE a tool for injecting realistic physical attacks through software. RAVAGE is easily extensible to multiple autopilot software and RAV types. It also allows users to configure the attack parameters without any code modifications. Further, RAVAGE automates the injection of both overt and stealthy attacks, offering a comprehensive setup for RAV security experiments. We evaluate RAVAGE on three virtual and three real RAVs, targeting six different types of RAV sensors, across a wide range of missions. We find that the attacks injected by RAVAGE resulted in crashes or mission failure in over 75% of the cases while incurring less than 2% performance overhead.

Sensors are crucial for perception and autonomous operation in robotic vehicles (RV). Unfortunately, RV sensors can be compromised by physical attacks such as sensor tampering or spoofing. In this paper, we present DeLorean, a unified framework for attack detection, attack diagnosis, and recovering RVs from sensor deception attacks (SDA). DeLorean can recover RVs even from strong SDAs in which the adversary targets multiple heterogeneous sensors simultaneously. We propose a novel attack diagnosis technique that inspects the attack-induced errors under SDAs, and identifies the targeted sensors using causal analysis. DeLorean then uses historic state information to selectively reconstruct physical states for compromised sensors, enabling targeted attack recovery under single or multi-sensor SDAs. We evaluate DeLorean on four real and two simulated RVs under SDAs targeting various sensors, and we find that it successfully recovers RVs from SDAs in 93% of the cases.

Robotic Autonomous Vehicles (RAVs) rely on their sensors for perception, and follow strict mission specifications (e.g., altitude, speed, and geofence constraints) for safe and timely operations. Physical attacks can corrupt the RAVs' sensors, resulting in mission failures. Recovering RAVs from such attacks demands robust control techniques that maintain compliance with mission specifications even under attacks to ensure the RAV's safety and timely operations.

Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are robust (i.e., physically-realizable) and universally malicious, and hence represent a severe security threat to real-world DNN-based systems.

Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been given to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target a swarm member (target drone) through GPS spoofing attacks, and indirectly cause other swarm members (victim drones) to veer from their course, resulting in a collision with an obstacle. We call these Swarm Propagation Vulnerabilities. In this paper, we introduce SwarmFuzz, a fuzzing framework to capture the attacker's ability, and efficiently find such vulnerabilities in swarm control algorithms. SwarmFuzz uses a combination of graph theory and gradient-guided optimization to find the potential attack parameters. Our evaluation on a popular swarm control algorithm shows that SwarmFuzz achieves an average success rate of 48.8% in finding vulnerabilities, and compared to random fuzzing, has a 10x higher success rate, and 3x lower runtime. We also find that swarms of a larger size are more vulnerable to this attack type, for a given spoofing distance.

Swarm robotics, particularly drone swarms, are used in various safety-critical tasks. While a lot of attention has been paid to improving swarm control algorithms for improved intelligence, the security implications of various design choices in swarm control algorithms have not been studied. We highlight how an attacker can exploit the vulnerabilities in swarm control algorithms to disrupt drone swarms. Specifically, we show that the attacker can target one swarm member (target drone) through sensor spoofing attacks, and indirectly cause other swarm members (victim drones) to veer off from their course, and potentially resulting in a crash. Our attack cannot be prevented by traditional software security techniques, and it is stealthy in nature as it causes seemingly benign deviations in drone swarms. Our initial results show that spoofing the position of a target drone by 5m is sufficient to cause other drones to crash into a front obstacle. Overall, our attack achieves 76.67% and 93.33% success rate with 5m and 10m spoofing deviation respectively.

Robotic Vehicles (RV) rely extensively on sensor inputs to operate autonomously. Physical attacks such as sensor tampering and spoofing can feed erroneous sensor measurements to deviate RVs from their course and result in mission failures. In this paper, we present PID-Piper, a novel framework for automatically recovering RVs from physical attacks. We use machine learning (ML) to design an attack resilient Feed-Forward Controller (FFC), which runs in tandem with the RV's primary controller and monitors it. Under attacks, the FFC takes over from the RV's primary controller to recover the RV, and allows the RV to complete its mission successfully. Our evaluation on 6 RV systems including 3 real RVs shows that PID-Piper achieves high accuracy in emulating the RV's controller, in the absence of attacks, with no false positives. Further, PID-Piper allows RVs to complete their missions successfully despite attacks in 83% of the cases, while incurring low performance overheads.

Robotic vehicles (RVs) are cyber-physical systems that operate in the physical world under the control of software functions. They are increasing in adoption in many industrial sectors. RVs rely on sensors and actuators for system operations and navigation. Control algorithm based estimation techniques have been used in RVs to minimize the effects of noisy sensors, prevent faulty actuator output, and recently, in detecting attacks against RVs. In this paper, we propose three kinds of attacks to evade the control-based detection techniques and cause RVs to malfunction. We also propose automated algorithms for performing the attacks without requiring the attacker to expend significant effort or know specific details of the RV, making the attacks applicable to a wide range of RVs. We demonstrate these attacks on ArduPilot simulators and two real RVs (a drone and a rover) in the presence of an Intrusion Detection System (IDS) using control estimation models to monitor the runtime behavior of the system. We find that the control models are incapable of detecting our stealthy attacks, and that the attacks can have significant adverse impact on the RV's mission (e.g., cause the RV to crash or deviate from its target significantly).