Abusive adversarial agents and attack strategies in cyber‐physical systems

Abstract

The exponential increase in IoT device usage has spawned numerous cyberspace innovations. IoT devices, sensors, and actuators bridge the gap between physical processes and the cyber network in a cyber-physical system (CPS). Cyber-physical system is a complex system from a security perspective due to the heterogeneous nature of its components and the fact that IoT devices can serve as an entry point for cyberattacks. Most adversaries design their attack strategies on systems to gain an advantage at a relatively lower cost, whereas abusive adversaries initiate an attack to inflict maximum damage without regard to cost or reward. In this paper, a sensor spoofing attack is modelled as a malicious adversary attempting to cause system failure by interfering with the feedback control mechanism. It is accomplished by feeding spoofed sensor values to the controller and issuing erroneous commands to the actuator. Experiments on a Simulink-simulated linear CPS support the proof of concept for the proposed abusive ideology, demonstrating three attack strategies. The impact of the evaluations stresses the importance of testing the CPS security against adversaries with abusive settings for preventing cyber-vandalism. Finally, the research concludes by highlighting the limitations of the proposed work, followed by recommendations for the future.

Consumers Triggering Factory Robots Over the Internet to Optimize Purchasing Systems

“…The Firewall server acts as a gate to filter out rogue software and IP addresses from accessing the laptop. So, if 168.74.5.4.2 is the IP address of the PLC server or hub that controls the robots, the Firewall server software must be modified such that the connection from outside to this PLC is allowed [22].…”

Stochastic Models to Mitigate Sparse Sensor Attacks in Continuous-Time Non-Linear Cyber-Physical Systems

“…Distributed secure state estimation is useful against systemic attacks such as Byzantine attacks [25]. System states are deducted through an optimization process where linear models represent the sensors' outputs and graphs [26], Markov chains [27], binary decision trees [28], and other mathematical paradigms (such as the Lipschitz continuity) [29] are used to represent the interconnections and transmissions among nodes. Custom quasi-linear models for specific applications, such as seriesparallel systems, have been also reported [30,31].…”

“…Finally, as every sensor node n m has a different function T m , the whole CPS is represented by a set of N different Eq. ( 27), which can be represented in one vector expression (28).…”

Wake-Up Security: Effective Security Improvement Mechanism for Low Power Internet of Things

“…Research on memory management transactions due to data immutability is needed Energy-management algorithm for attacks on the IoT Lack of research on security performance [44][45][46][47][48] Algorithm Mutual authentication algorithm Network and system compression Focused on specific performance improvement for each algorithm algorithms Potential security threat prediction model Regarding framework type, a security and privacy protection framework combined with multilevel trust management was proposed, and a user-centered privacy protection service was provided via the introduction of a hybrid encryption algorithm and lightweight encryption [33]. IoT-based healthcare systems have emerged as promising areas wherein patient data can be stored and transmitted securely [34].…”