Byzantine Attack and Defense in Cognitive Radio Networks: A Survey

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.Comment: Accepted by IEEE Communications Surveys and Tutoiral

Detection of False Data Injection Attacks in Smart Grid under Colored Gaussian Noise

In this paper, we consider the problems of state estimation and false data injection detection in smart grid when the measurements are corrupted by colored Gaussian noise. By modeling the noise with the autoregressive process, we estimate the state of the power transmission networks and develop a generalized likelihood ratio test (GLRT) detector for the detection of false data injection attacks. We show that the conventional approach with the assumption of Gaussian noise is a special case of the proposed method, and thus the new approach has more applicability. {The proposed detector is also tested on an independent component analysis (ICA) based unobservable false data attack scheme that utilizes similar assumptions of sample observation.} We evaluate the performance of the proposed state estimator and attack detector on the IEEE 30-bus power system with comparison to conventional Gaussian noise based detector. The superior performance of {both observable and unobservable false data attacks} demonstrates the effectiveness of the proposed approach and indicates a wide application on the power signal processing.Comment: 8 pages, 4 figures in IEEE Conference on Communications and Network Security (CNS) 201

RSU-Based Online Intrusion Detection and Mitigation for VANET

Secure vehicular communication is a critical factor for secure traffic management. Effective security in intelligent transportation systems (ITS) requires effective and timely intrusion detection systems (IDS). In this paper, we consider false data injection attacks and distributed denial-of-service (DDoS) attacks, especially the stealthy DDoS attacks, targeting the integrity and availability, respectively, in vehicular ad-hoc networks (VANET). Novel statistical intrusion detection and mitigation techniques based on centralized communications through roadside units (RSU) are proposed for the considered attacks. The performance of the proposed methods are evaluated using a traffic simulator and a real traffic dataset. Comparisons with the state-of-the-art solutions clearly demonstrate the superior performance of the proposed methods in terms of quick and accurate detection and localization of cyberattacks

Resilient Learning-Based Control for Synchronization of Passive Multi-Agent Systems under Attack

In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required communication load on the band-limited network. We define a general Byzantine attack on the event-triggered multi-agent network system and characterize its negative effects on synchronization. The Byzantine agents are capable of intelligently falsifying their data and manipulating the underlying communication graph by altering their respective control feedback weights. We introduce a decentralized detection framework and analyze its steady-state and transient performances. We propose a way of identifying individual Byzantine neighbors and a learning-based method of estimating the attack parameters. Lastly, we propose learning-based control approaches to mitigate the negative effects of the adversarial attack

A Study of Potential Security and Safety Vulnerabilities in Cyber-Physical Systems

The work in this dissertation focuses on two examples of Cyber-Physical Systems (CPS), integrations of communication and monitoring capabilities to control a physical system, that operate in adversarial environments. That is to say, it is possible for individuals with malicious intent to gain access to various components of the CPS, disrupt normal operation, and induce harmful impacts. Such a deliberate action will be referred to as an attack. Therefore, some possible attacks against two CPSs will be studied in this dissertation and, when possible, solutions to handle such attacks will also be suggested. The first CPS of interest is vehicular platoons wherein it is possible for a number of partially-automated vehicles to drive autonomously towards a certain destination with as little human driver involvement as possible. Such technology will ultimately allow passengers to focus on other tasks, such as reading or watching a movie, rather than on driving. In this dissertation three possible attacks against such platoons are studied. The first is called ”the disbanding attack” wherein the attacker is capable of disrupting one platoon and also inducing collisions in another intact (non-attacked) platoon vehicles. To handle such an attack, two solutions are suggested: The first solution is formulated using Model Predictive Control (MPC) optimal technique, while the other uses a heuristic approach. The second attack is False-Data Injection (FDI) against the platooning vehicular sensors is analyzed using the reachability analysis. This analysis allows us to validate whether or not it is possible for FDI attacks to drive a platoon towards accidents. Finally, mitigation strategies are suggested to prevent an attacker-controlled vehicle, one which operates inside a platoon and drives unpredictably, from causing collisions. These strategies are based on sliding mode control technique and once engaged in the intact vehicles, collisions are reduced and eventual control of those vehicles will be switched from auto to human to further reduce the impacts of the attacker-controlled vehicle. The second CPS of interest in this dissertation is Heating, Ventilating, and Air Conditioning (HVAC) systems used in smart automated buildings to provide an acceptable indoor environment in terms of thermal comfort and air quality for the occupants For these systems, an MPC technique based controller is formulated in order to track a desired temperature in each zone of the building. Some previous studies indicate the possibility of an attacker to manipulate the measurements of temperature sensors, which are installed at different sections of the building, and thereby cause them to read below or above the real measured temperature. Given enough time, an attacker could monitor the system, understand how it works, and decide which sensor(s) to target. Eventually, the attacker may be able to deceive the controller, which uses the targeted sensor(s) readings and raises the temperature of one or multiple zones to undesirable levels, thereby causing discomfort for occupants in the building. In order to counter such attacks, Moving Target Defense (MTD) technique is utilized in order to constantly change the sensors sets used by the MPC controllers and, as a consequence, reduce the impacts of sensor attacks

On addressing the security and stability issues due to false data injection attacks in DC microgrids an adaptive observer approach

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper proposes an observer-based methodology to detect and mitigate false data injection attacks in collaborative DC microgrids. The ability of observers to effectively detect such attacks is complicated by the presence of unknown non-linear constant power loads. This work determines that, in the presence of unknown constant power loads, the considered attack detection and mitigation problem involves non linearities, locally unobservable states, unknown parameters, uncertainty and noise. Taking into account these limitations, a distributed non linear adaptive observer is proposed to overcome these limitations and solve the concerned observation problem. The necessary conditions for the stability of the distributed scheme are found out. Moreover, numerical simulations are performed and then validated in a real experimental prototype, where communication delay, uncertainty and noise are considered.Peer ReviewedPostprint (author's final draft