Cyber Physical Systems (CPS) are characterized by their ability to integrate the physical and information or cyber worlds. Their deployment in critical infrastructure have demonstrated a potential to ...transform the world. However, harnessing this potential is limited by their critical nature and the far reaching effects of cyber attacks on human, infrastructure and the environment. An attraction for cyber concerns in CPS rises from the process of sending information from sensors to actuators over the wireless communication medium, thereby widening the attack surface. Traditionally, CPS security has been investigated from the perspective of preventing intruders from gaining access to the system using cryptography and other access control techniques. Most research work have therefore focused on the detection of attacks in CPS. However, in a world of increasing adversaries, it is becoming more difficult to totally prevent CPS from adversarial attacks, hence the need to focus on making CPS resilient. Resilient CPS are designed to withstand disruptions and remain functional despite the operation of adversaries. One of the dominant methodologies explored for building resilient CPS is dependent on machine learning (ML) algorithms. However, rising from recent research in adversarial ML, we posit that ML algorithms for securing CPS must themselves be resilient. This article is therefore aimed at comprehensively surveying the interactions between resilient CPS using ML and resilient ML when applied in CPS. The paper concludes with a number of research trends and promising future research directions. Furthermore, with this article, readers can have a thorough understanding of recent advances on ML-based security and securing ML for CPS and countermeasures, as well as research trends in this active research area.
Research into the optimization of wireless communications continues to be of interest due to its prominent role in Internet-of-things and cyber physical systems (CPS) related applications. With ...emerging technologies like federated learning (FL) and software defined networks deployed over the air, the design of wireless networks must be rethought to align well with technological advancements. Intelligent Reflective Surfaces (IRS), a technology that enhances the controllability of the channel between the transmitter and the receiver has demonstrated a great potential to enhance communications especially in challenging terrains. In this research, as a case study, we consider the deployment of IRS with unmanned aerial vehicles to enhance wireless communications for battlefield scenarios. We also study the security concerns in such deployment using a deep reinforcement learning (DRL) coupled with defensive deception approach. Specifically, data-driven power allocation in communication channels using RL is leveraged upon to obfuscate the attack surface, lure jammers into designated channels and ultimately mitigate attempted denial-of-service attacks. Simulation experiments carried out attest to the veracity of the proposed approach.
Advancements in information and communication technologies have contributed significantly to the optimization of next generation wireless communications networks. Moreover, wireless communications ...play a huge role in electronic warfare and the emergence of new technologies continue to provoke a corresponding revolution in battlefield operations. Nevertheless, the widened attack surface that result from the increased wireless networking of battlefield devices lure attackers like jammers and eavesdroppers who seek to explore the vulnerability of the network through the physical layer. Consequently, it is imperative to direct research efforts not only towards advancing spectral and energy efficiency but also secure wireless communications. In order to address the challenges, we leverage on the recent developments in the use of machine learning (ML) for wireless communications to propose a novel approach for secure wave-form transmission in battlefield operations. Specifically, we use the learning-based end-to-end representation of communication systems where the transmitter and receiver are represented by two deep neural networks (DNN). The goal is to prevent the eavesdropper from accessing the communications between the transmitter and the receiver without necessarily a feedback mechanism as in the conventional communication system. In comparison to traditional optimization methods that are usually iterative in nature, the advantages of employing deep learning (DL) includes its ability to address the challenges of statistically inclined methods. Using simulations, we compare the proposed method with other methods to ascertain its credibility.
Improved safety, high mobility and environmental concerns in transportation systems across the world and the corresponding developments in information and communication technologies continue to drive ...attention towards Intelligent Transportation Systems (ITS). This is evident in advanced driver-assistance systems such as lane departure warning, adaptive cruise control and collision avoidance. However, in connected and autonomous vehicles, the efficient functionality of these applications depends largely on the ability of a vehicle to accurately predict it operating parameters such as location and speed. The ability to predict the immediate future/next location (or speed) of a vehicle or its ability to predict neighbors help in guaranteeing integrity, availability and accountability, thus boosting safety and resiliency of the Vehicular Network for Mobile Cyber Physical Systems (VCPS). In this paper, we proposed a secure movement-prediction for connected vehicles by using Kalman filter. Specifically, Kalman filter predicts the locations and speeds of individual vehicles with reference to already observed and known information such posted legal speed limit, geographic/road location, direction etc. The aim is to achieve resilience through the predicted and exchanged information between connected moving vehicles in an adaptive manner. By being able to predict their future locations, the following vehicle is able to adjust its position more accurately to avoid collision and to ensure optimal information exchange among vehicles.
Vehicular cyber physical systems (VCPS) will play a vital role in the quest to develop intelligent transportation systems (ITS) and smart cities around the world. Consequently, researchers in ...academia, industry and government continue to leverage on emerging technologies like software defined networking (SDN), blockchain, cloud computing and machine learning (ML) to improve the overall efficiency of these intelligent systems. Recently, mobile edge computing (MEC) has been used to enhance content caching and efficient resource allocation and therefore advance the development of data-intensive and delay-constrained applications that improve the driving experience in VCPS. Security and privacy concerns that endanger the safety of lives and infrastructure necessitate the need to use federated learning (FL), a distributed ML algorithm that employs learning at the edge to ensure that data remains at the different vehicles and thus enhance greater efficiency. In this paper therefore, we propose the use of FL, together with differential privacy to improve the resiliency of VCPS to adversarial attacks in connected vehicles.
Deep Learning for Cyber Deception in Wireless Networks Olowononi, Felix O.; Anwar, Ahmed H.; Rawat, Danda B. ...
2021 17th International Conference on Mobility, Sensing and Networking (MSN),
2021-Dec.
Conference Proceeding
Wireless communications networks are an integral part of intelligent systems that enhance the automation of various activities and operations embarked by humans. For example, the development of ...intelligent devices imbued with sensors leverages emerging technologies such as machine learning (ML) and artificial intelligence (AI), which have proven to enhance military operations through communication, control, intelligence gathering, and situational awareness. However, growing concerns in cybersecurity imply that attackers are always seeking to take advantage of the widened attack surface to launch adversarial attacks which compromise the activities of legitimate users. To address this challenge, we leverage on deep learning (DL) and the principle of cyber-deception to propose a method for defending wireless networks from the activities of jammers. Specifically, we use DL to regulate the power allocated to users and the channel they use to communicate, thereby luring jammers into attacking designated channels that are considered to guarantee maximum damage when attacked. Furthermore, by directing its energy towards the attack on a specific channel, other channels are freed up for actual transmission, ensuring secure communication. Through simulations and experiments carried out, we conclude that this approach enhances security in wireless communication systems.
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