An Attrition Game

Cyberspace is vulnerable to continuous malicious attacks. Traceability of network attacks is an effective defense means to curb and counter network attacks. In this paper, the evolutionary game model is used to analyze the network attack and defense behavior. On the basis of the quantification of attack and defense benefits, the replication dynamic learning mechanism is used to describe the change process of the selection probability of attack and defense strategies, and finally the evolutionary stability strategies and their solution curves of both sides are obtained. On this basis, the attack behavior is analyzed, and the probability curve of attack strategy and the optimal attack strategy are obtained, so as to realize the effective traceability of attack behavior.

Stackelberg game in critical infrastructures from a network science perspective

With the development of various wireless personal area networks (WPANs), the issue of security has become a crucial problem for their applications. Jamming is one of the most important methods of attack to deprive or reduce the communication service of WPANs. Most existing jamming attacks can cause negative interference, but the attack strategies are usually not adjusted against the countermeasures that are currently taken. This paper proposes an effective dynamic jamming attack (EDJam) in an 802.15.4-compliant WPAN. In this attack, a jammer who is aware of a change in the network defense strategy, e.g. the use of a dynamic retransmission mechanism, may choose a better strategy to make more damage to the network with less cost. Similarly, a well-protected network can change its defense strategy against the EDJam. This procedure of competition between the EDJam attacker and defending networks is modeled and formulated as a Stackelberg game, and a unique Nash Equilibrium point is derived in analytical format. Based on an equilibrium analysis, we discuss the condition under which a defense strategy will increase the utility of the network and a dynamic retransmission mechanism defense strategy is proposed accordingly. The simulation results show that EDjam can be more cost-efficient than continuous, random and fixed-period jamming.

Virtual humans emerged as a topic of research in HCI and they have been used for various purposes. This paper explores the behavior of chess players in a virtual chess environment to gain more understanding about chess personalities. In particular, the focus of this research is investigating attack and defense strategies used by virtual chess grandmasters against different virtual class-B personalities who vary in their strength in the different stages of a game. These attack and defense strategies have attracted much attention in the chess community and are considered among the main aspects to chess players. They occur in different phases of the game: opening, middle game and endgame. The researcher examines virtual chess players to understand the psychology of competition between two grandmasters (attacker, defender) and three class-B chess players with different personalities: (a) strong at openings; (b) weak at openings, but strong at endgames and (c) balanced player. The virtual humans in this research represent personalities of real players. The empirical players’ results showed that the personalities could influence the error and the number of moves of the game for both grandmasters and class-B players. Such findings can be used in designing virtual chess players.

Cyber-Physical Systems (CPSs) play an increasingly significant role in many critical applications. These valuable applications attract various sophisticated attacks. This paper considers a stealthy estimation attack, which aims to modify the state estimation of the CPSs. The intelligent attackers can learn defense strategies and use clandestine attack strategies to avoid detection. To address the issue, we design a Chi-square detector in a Digital Twin (DT), which is an online digital model of the physical system. We use a Signaling Game with Evidence (SGE) to find the optimal attack and defense strategies. Our analytical results show that the proposed defense strategies can mitigate the impact of the attack on the physical estimation and guarantee the stability of the CPSs. Finally, we use an illustrative application to evaluate the performance of the proposed framework.

This paper aims to investigate the security problem of nonlinear cyber-physical systems (CPSs), which poses a challenge to handle compared with linear CPSs. A series of optimization problems for nonlinear CPSs under injection attack are constructed, which are based on a general model of the nonlinear systems with repetitive operation characteristics and a novel introduction of the key technical lemma. These optimization results are more general than the existing injection attack results and the requirements for attackers to obtain system information are relaxed. Also, the form of switching applied to the attack strategy possesses several advantages, including high stealthiness, lower cost, and more flexibility. Therefore, the new optimal injection attack strategies are expected to be more widespread and provide a basis for the design of defense strategies. The key to acquiring the designed optimal attack strategies is to adopt the linear input/output (I/O) data model for these systems via introducing an estimation term of the improved projection estimation method into the linear model. Finally, a networked GLUON-6L3 manipulator example validates the effectiveness of the proposed methods. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The main purpose of this paper is to study the cyber security of nonlinear cyber-physical systems from the perspective of attackers, which can help defenders fully understand the behavior of attackers. Most of the existing attack strategies aimed at linear systems or have a priori knowledge of the attacked systems. However, there are great limitations and difficulties in practical application. In this paper, the new attack strategies are proposed by combining system identification, iterative learning and control theory, which relaxes the requirement of the attacker’s ability. In detail, the attacker only needs to obtain the control input and output data of the attacked system and design the attack strategy according to it. Among them, the first-in, first-out queue storage method is applied to remove the requirements for the storage capability of the attacker. In practical applications, the attacker does not need to store the I/O data continuously, but only store the initial value and the data of two successive iterations. The mathematical analytic forms of two optimal attack strategies are given, and then their effectiveness are verified on a welding work of a networked six-axis manipulator. In the future, we will investigate the design of attack strategies for nonlinear systems with heterogeneous dynamics and multiple delays.

In this paper, we introduce a measure to analyse the structural robustness of complex networks, which is specifically applicable in scenarios of targeted, sustained attacks. The measure is based on the changing size of the largest component as the network goes through disintegration. We argue that the measure can be used to quantify and compare the effectiveness of various attack strategies. Applying this measure, we confirm the result that scale-free networks are comparatively less vulnerable to random attacks and more vulnerable to targeted attacks. Then we analyse the robustness of a range of real world networks, and show that most real world networks are least robust to attacks based on betweenness of nodes. We also show that the robustness values of some networks are more sensitive to the attack strategy as compared to others. Furthermore, robustness coefficient computed using two centrality measures may be similar, even when the computational complexities of calculating these centrality measures may be different. Given this disparity, the robustness coefficient introduced potentially plays a key role in choosing attack and defence strategies for real world networks. While the measure is applicable to all types of complex networks, we clearly demonstrate its relevance to social network analysis.

Attack-defense game for critical infrastructure considering the cascade effect

Recursive reasoning-based training-time adversarial machine learning

Deep neural networks (DNNs) have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. Though there are several works about adversarial attack and defense strategies on domains such as images and natural language processing, it is still difficult to directly transfer the learned knowledge to graph data due to its representation structure. Given the importance of graph analysis, an increasing number of studies over the past few years have attempted to analyze the robustness of machine learning models on graph data. Nevertheless, existing research considering adversarial behaviors on graph data often focuses on specific types of attacks with certain assumptions. In addition, each work proposes its own mathematical formulation, which makes the comparison among different methods difficult. Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models. Moreover, we also compare different graph attacks and defenses along with their contributions and limitations, as well as summarize the evaluation metrics, datasets and future trends. We hope this survey can help fill the gap in the literature and facilitate further development of this promising new field.

In this paper we study the problem of assessing the effectiveness of a proactive defense-by-detection policy with a network-based moving target defense. We model the network system using a probabilistic attack graph-a graphical security model. Given a network system with a proactive defense strategy an intelligent attacker needs to perform reconnaissance repeatedly to learn about the locations of intrusion detection systems and re- plan optimally to reach the target while avoiding detection. To compute the attacker's strategy for security evaluation we develop a receding-horizon planning algorithm using a risk-sensitive Markov decision process with a time-varying reward function. Finally we implement both defense and attack strategies in a synthetic network and analyze how the frequency of network randomization and the number of detection systems can influence the success rate of the attacker. This study provides insights for designing proactive defense strategies against online and multi-stage attacks by a resourceful attacker.

Abstract5G and 6G promise to be catalysts for the Internet of Things (IoT), enabling ultra‐massive and mission‐critical IoT. The demands of new IoT applications and the large number of connected objects being announced will exacerbate the thorny issue of spectrum scarcity in wireless communications. Cognitive radio (CR) is a revolutionary technology that promises to mitigate the problem of spectrum scarcity through more efficient use of spectrum. Integrating CR into the IoT allows objects to opportunistically access spectrum resources already allocated to a Primary User (PU) without causing interference. Spectrum sensing (SS) allows objects to be aware of the PU's spectrum occupancy status. In radio environments where the PU signal is subject to multipath and shadowing effects that degrade the channel between the PU and objects, SS performed by a single object could be inaccurate and lead to incorrect decisions about the PU's status. Cooperative spectrum sensing (CSS) has been proposed to improve detection performance. However, this cooperation between objects has opened the way to a new form of attack known as the Spectrum Sensing Data Falsification (SSDF) or Byzantine attack. In a SSDF attack, attackers falsify their local sensing results before sharing them in the CSS. This attack is very harmful to the CSS and can lead to a loss of spectrum opportunities or interference with the PU. In this paper, from the attack point of view, a sophisticated Byzantine attack model that generalizes well the simple attack strategies has been proposed and allows an attacker to implement other attack strategies. From a defense point of view, a new and innovative Weighted Sequential Hypothesis Testing (WSPRT) scheme has been suggested. This ensures the security of the CSS while significantly reducing the average number of samples required for overall decision‐making in a very hostile IoT network. The results obtained from simulations carried out in various attacks scenarios show that the proposed secure CSS scheme requires at most six samples to detect the state of the PU without error when the proportion of attackers reaches 80%. This performance far exceeds that of other defense mechanisms such as classic WSPRT, SPRT, and majority rule, with which we have compared it under the same conditions. In general, for the classic WSPRT, SPRT, and majority rule mechanisms, the error rate starts to deteriorate at ratio 40% of attackers and the number of samples is greater than six and reaches 50.

Resilient control of networked control systems with stochastic denial of service attacks

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues.

This paper presents a novel multi-dimensional asymmetric game model for network attack–defense decision-making, called “Catch the Cyber Thief”. The model is built upon the concept of partially observable stochastic games (POSG) and is designed to systematically incorporate multi-dimensional asymmetry into network attack–defense problems. The attack agent is called a “thief” who wants to control a key host by exploring the unfamiliar network environment, and the defense agent is called a “police” who needs to catch the opponent before its goal is accomplished. The results indicate that the asymmetry of network attack and defense is not only influenced by attack and defense strategies but also by spatio-temporal factors such as the attacker’s initial position, network topology, and defense lag time. In addition, we have found that there may exist the “attack rhythm,” which makes “how to maintain a good attack rhythm” and “how to generate a robust defense strategy against different attackers” worth exploring. Compared with existing attack–defense game models, our game model can better generate a direct mapping relationship with real elements, enabling us to understand network attack and defense interactions better, recognize security risks, and design defense strategies that can directly serve real-world decision-making.