# Analyzing Cyber-Physical Threats on Robotic Platforms +
## 2 Related Work
The authors showed that such attacks can be detected by machine and supervised learning techniques. Different learning algorithms were analyzed using real data collected by a wheeled robot equipped with a RTLS based on ultra wideband beacons. The experimental results indicated that the Multi-Layer Perceptron classifier is most effective for detecting DoS and spoofing cyber-attacks on RTLSs. [PERSON] et al. [25] proposed an orthogonal defense mechanism (ODM) to detect cyber-attacks on an operational water treatment plant. ODM stealthily monitors the plant using an independent network to collect data from various sources and corroborates the plant state. The proposed mechanism is orthogonal to any other defense mechanism that may exist. The design of the proposed ODM is generic and could be easily adapted for other CPSs. [PERSON] et al. [26] presented a real-time sensor-agnostic methodology that runs on an onboard resource-constrained autonomous vehicle to detect different first-time cyber-physical and physical-cyber attacks. Their approach is based on formulating the detection method as a binary classification problem. In [27], [PERSON] et al. developed an intrusion detection system based on decision trees against DoS and Command Injection attacks. Most recently, [PERSON] et al. [28] presented two attack detection techniques for robotic vehicles. The first technique implemented detection using decision trees and the second technique utilized deep learning to efficiently detect various attacks. Similar to the second technique, [PERSON] and [PERSON] [29] designed a two-stage intrusion detection system (IDS) to reveal the existence of intrusions and malware in autonomous robots. A deep neural network is trained to detect behavior deviations. [PERSON] et al. [30] investigated multiple security threats of Unmanned Aerial Vehicles (UAV) by listing vulnerabilities, which can be exploited to negatively impact confidentiality, integrity, and availability. The risk level of an attack was evaluated by multiplying the estimated likelihood and the attack impact. [PERSON] et al. [31] identified threats and vulnerabilities in the system's concept for Unmanned Tactical Autonomous Control and Collaboration (UTACC). [PERSON] et al. [32] experimentally analyzed the security of an ABB 6-axis IRB140/IRC5 industrial robot controller. Several software vulnerabilities in the robot main computer were exposed. These vulnerabilities are associated with the network services necessary for the robot's operation (e.g., FTP service). [PERSON] et al. [33] considered and studied the types of attacks that might occur (e.g., the robot is taken over and turned into a weapon) and their implications on rescue and recovery missions. More recently, [PERSON] et al. [19] analyzed the security threats for the Raven II robot, which is teleoperated for surgical purposes. The analysis showed that many of the robot tasks can be maliciously altered using manipulation and disruption attacks on the wireless communication link between the robot and the user (i.e., the surgeon). The implemented attacks were based on the man-in-the-middle model and they negatively impacted the usability and the safety of the robot, which might result in privacy and legal violations. Similarly, [PERSON] et al. [34] demonstrated targeted cyber-physical attacks on teleoperated surgical robots, by exploiting vulnerabilities in the robot's control system. They showed that such attacks can lead to catastrophic consequences in the physical system (RAVEN II robotic arm) and/or patient. [PERSON] et al. presented a model-based analysis framework to estimate the consequences of control commands within the real-time constraints of the control system or robot's dynamics. [PERSON] et al. [35] modeled and studied integrity attacks against CPSs such as automobiles and robotic platforms. They formulated a quadratic cost function that captures the attacker's control objectives to avoid detection. [PERSON] et al.