Modelling and Simulation of Cognitive Electronic Attack under the Condition of System of systems Combat

From the height of system-of-systems combat and operational perspective, the operations of cognitive electronic warfare (CEW) was analysed, and its main process and links were described. Secondly, the jamming effectiveness evaluation (JEE) model of cognitive electronic attack (CEA) operations was established based on the interference side, in which the change of threat degree was used as the measure index of jamming effectiveness. Then, based on the Q-learning model, an intelligent countermeasure strategy generation (ICSG) model was established, and the main steps in the model were given. Finally, on the basis the JEE model and the ICSG model, the simulation experiment was carried out for CEA operations. The result showed that combining the JEE model with the ICSG model can express the main process of the operations of CEW, as well as proved the validity of these models

Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLF-PHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.Comment: This paper appears in the Proceedings of IEEE Global Communications Conference (GLOBECOM) 2020. A full version appears in IEEE Transactions on Wireless Communications. arXiv:2004.1253

Intelligent Reflecting Surface Assisted Anti-Jamming Communications: A Fast Reinforcement Learning Approach

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS), and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLFPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLFPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions

Joint Adaptation of Frequency Hopping and Transmission Rate for Anti-Jamming Wireless Systems

Wireless transmissions are inherently vulnerable to jamming attacks. Frequency hopping (FH) and transmission rate adaptation (RA) have been separately used to mitigate jamming. When RA is used alone, it has been shown that a jammer who randomizes its power levels can force the transmitter to always operate at the lowest rate, by maintaining the average jamming power above a certain threshold. On the other hand, when only FH is used, a high throughput overhead is incurred due to frequent channel switching. In this paper, we propose to mitigate jamming by jointly optimizing the FH and RA techniques. This way, the transmitter can escape the jammer by changing its channel, adjusting its rate, or both. We consider a power-constrained 'reactive-sweep' jammer who aims at degrading the throughput of the wireless link. The jammer sweeps through the set of channels, jamming a subset of them at a time, using the optimal jamming power. We model the interactions between the legitimate transmitter and jammer as a constrained zero-sum Markov game. The transmitter's optimal defense strategy is derived by obtaining the equilibria of the constrained Markov game. This policy informs the transmitter when to hop to another channel and when to stay on the current channel. Furthermore, it gives the best transmission rate to use in both cases (hop or stay). The structure of the transmitter's optimal policy is shown to be threshold type, whereby the transmitter stays on the same channel up to a certain number of time slots after which it hops. We analyze the 'constrained Nash equilibrium' of the Markov game and show that the equilibrium defense strategy of the transmitter is deterministic. Numerical investigations show that the new scheme improves the average throughput and provides better jamming resiliency

Joint Adaptation of Frequency Hopping and Transmission Rate for Anti-Jamming Wireless Systems

Wireless transmissions are inherently vulnerable to jamming attacks. Frequency hopping (FH) and transmission rate adaptation (RA) have been separately used to mitigate jamming. When RA is used alone, it has been shown that a jammer who randomizes its power levels can force the transmitter to always operate at the lowest rate, by maintaining the average jamming power above a certain threshold. On the other hand, when only FH is used, a high throughput overhead is incurred due to frequent channel switching. In this paper, we propose to mitigate jamming by jointly optimizing the FH and RA techniques. This way, the transmitter can escape the jammer by changing its channel, adjusting its rate, or both. We consider a power-constrained "reactive-sweep" jammer who aims at degrading the throughput of the wireless link. The jammer sweeps through the set of channels, jamming a subset of them at a time, using the optimal jamming power. We model the interactions between the legitimate transmitter and jammer as a constrained zero-sum Markov game. The transmitter's optimal defense strategy is derived by obtaining the equilibria of the constrained Markov game. This policy informs the transmitter when to hop to another channel and when to stay on the current channel. Furthermore, it gives the best transmission rate to use in both cases (hop or stay). The structure of the transmitter's optimal policy is shown to be threshold type, whereby the transmitter stays on the same channel up to a certain number of time slots after which it hops. We analyze the "constrained Nash equilibrium" of the Markov game and show that the equilibrium defense strategy of the transmitter is deterministic. Numerical investigations show that the new scheme improves the average throughput and provides better jamming resiliency

Game-Theoretic Frameworks and Strategies for Defense Against Network Jamming and Collocation Attacks

Modern networks are becoming increasingly more complex, heterogeneous, and densely connected. While more diverse services are enabled to an ever-increasing number of users through ubiquitous networking and pervasive computing, several important challenges have emerged. For example, densely connected networks are prone to higher levels of interference, which makes them more vulnerable to jamming attacks. Also, the utilization of software-based protocols to perform routing, load balancing and power management functions in Software-Defined Networks gives rise to more vulnerabilities that could be exploited by malicious users and adversaries. Moreover, the increased reliance on cloud computing services due to a growing demand for communication and computation resources poses formidable security challenges due to the shared nature and virtualization of cloud computing. In this thesis, we study two types of attacks: jamming attacks on wireless networks and side-channel attacks on cloud computing servers. The former attacks disrupt the natural network operation by exploiting the static topology and dynamic channel assignment in wireless networks, while the latter attacks seek to gain access to unauthorized data by co-residing with target virtual machines (VMs) on the same physical node in a cloud server. In both attacks, the adversary faces a static attack surface and achieves her illegitimate goal by exploiting a stationary aspect of the network functionality. Hence, this dissertation proposes and develops counter approaches to both attacks using moving target defense strategies. We study the strategic interactions between the adversary and the network administrator within a game-theoretic framework. First, in the context of jamming attacks, we present and analyze a game-theoretic formulation between the adversary and the network defender. In this problem, the attack surface is the network connectivity (the static topology) as the adversary jams a subset of nodes to increase the level of interference in the network. On the other side, the defender makes judicious adjustments of the transmission footprint of the various nodes, thereby continuously adapting the underlying network topology to reduce the impact of the attack. The defender\u27s strategy is based on playing Nash equilibrium strategies securing a worst-case network utility. Moreover, scalable decomposition-based approaches are developed yielding a scalable defense strategy whose performance closely approaches that of the non-decomposed game for large-scale and dense networks. We study a class of games considering discrete as well as continuous power levels. In the second problem, we consider multi-tenant clouds, where a number of VMs are typically collocated on the same physical machine to optimize performance and power consumption and maximize profit. This increases the risk of a malicious virtual machine performing side-channel attacks and leaking sensitive information from neighboring VMs. The attack surface, in this case, is the static residency of VMs on a set of physical nodes, hence we develop a timed migration defense approach. Specifically, we analyze a timing game in which the cloud provider decides when to migrate a VM to a different physical machine to mitigate the risk of being compromised by a collocated malicious VM. The adversary decides the rate at which she launches new VMs to collocate with the victim VMs. Our formulation captures a data leakage model in which the cost incurred by the cloud provider depends on the duration of collocation with malicious VMs. It also captures costs incurred by the adversary in launching new VMs and by the defender in migrating VMs. We establish sufficient conditions for the existence of Nash equilibria for general cost functions, as well as for specific instantiations, and characterize the best response for both players. Furthermore, we extend our model to characterize its impact on the attacker\u27s payoff when the cloud utilizes intrusion detection systems that detect side-channel attacks. Our theoretical findings are corroborated with extensive numerical results in various settings as well as a proof-of-concept implementation in a realistic cloud setting