Cyber-Physical Security in Intelligent Robotics: AI Approaches for Threat Detection and Prevention

Abstract

Background and scope

Intelligent robots are a combination of sensing, computing, communication, and actuation and are becoming more of an intertwined entity known as cyber-physical systems (CPS). Such systems are in the services, logistics, healthcare, and industrial automation sectors. Cyber breaches or algorithmic issues may lead to physical injury, safety issues or cause huge financial losses because the cyber components directly affect the physical systems. This paper reviews the AI-based detection and prevention strategies and provides an extended framework of safe, timely, and clarifiable defenses to manage cyber-physical security of intelligent robotics.

Issues and Exceptional Difficulties.

Robotics CPS is uniquely different in terms of safety-critical control loops, multimodal sensing diversity, physical interaction with individuals, and strict real-time constraints as opposed to traditional IT objectives.  These two enhance autonomy and create vulnerabilities are all AI elements (deep perceptual networks, reinforcement learners). Adversarial input can affect perception and decision modules, as well as model theft or data poisoning.  Moreover, the complexity of defense mechanisms is constrained by the performance of a small number of resources on robotic platforms, which requires lightweight and flexible tactics.

AI as Defender and Target

Besides being the carrier that most attacks employ, artificial intelligence (AI) can also be the most promising in detecting and preventing such attacks. Examples of modern methods are predictive maintenance to reduce exploitable failures, reinforcement-based safe controllers providing abnormally soft degradation, behavior-based models to track abnormalities in control signals, and anomaly detection of fused sensor streams. However defensive AI, itself, should be hardened to adversarial adaptation; to achieve this, detectors should be designed according to adversary knowledge and checked whenever possible.

Contributions of the Paper

The value of the paper is as follows: (1) it provides a systematic review of AI solutions to detection and prevention; (2) it presents a clear taxonomy of robotic CPS attack types, attacking sensor, network, firmware, and algorithmic layers; (3) it includes an experimental methodology uniting simulation, hardware-in-the-loop testing, and adaptive testing against white and black box adversaries; (4) it presents research questions and a set of recommendations to regulators, operators, and designers.

Implications and Structure

Intelligent robots should be made safe by cross-disciplinary solutions that combine control-theoretic safe-fallbacks, ML robustness and cryptographic integrity..  A thorough introduction, a thorough literature review, an enhanced methodology for assessing defenses in real-world scenarios, the key research questions, results, and practical suggestions for practice and further study are all included in the remaining portion of the paper.