Biblio

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.

Cyber deception plays an important role in both proactive and reactive defense systems. Internet of Battlefield things connecting smart devices of any military tactical network is of great importance. The goal of cyber deception is to provide false information regarding the network state, and topology to protect the IoBT's network devices. In this paper, we propose a novel deceptive approach based on game theory that takes into account the topological aspects of the network and the criticality of each device. To find the optimal deceptive strategy, we formulate a two-player game to study the interactions between the network defender and the adversary. The Nash equilibrium of the game model is characterized. Moreover, we propose a scalable game-solving algorithm to overcome the curse of dimensionality. This approach is based on solving a smaller in-size subgame per node. Our numerical results show that the proposed deception approach effectively reduced the impact and the reward of the attacker

This chapter presents a game‐theoretic model to analyze attack–defense scenarios that use fake nodes (computing devices) for deception under consideration of the system deploying defense resources to protect individual nodes in a cost‐effective manner. The developed model has important applications in the Internet of Battlefield Things (IoBT). Our game‐theoretic model illustrates how the concept of the Nash equilibrium can be used by the defender to intelligently choose which nodes should be used for performing a computation task while deceiving the attacker into expending resources for attacking fake nodes. Our model considers the fact that defense resources may become compromised under an attack and suggests that the defender, in a probabilistic manner, may utilize unprotected nodes for performing a computation while the attacker is deceived into attacking a node with defense resources installed. The chapter also presents a deception‐based strategy to protect a target node that can be accessed via a tree network. Numerical results provide insights into the strategic deception techniques presented in this chapter.

In this chapter, we develop a defense strategy to secure Internet of Battlefield Things (IoBT) based on a hypergame employing deceptive techniques. The hypergame is played multiple rounds. At each round, the adversary updates its perception of the attack graph and chooses the next node to compromise. The defender updates its perceived list of compromised nodes and actively feeds false signals to the adversary to create deception. The hypergame developed in this chapter provides an important theoretical framework for us to model how a cyberattack spreads on a network and the interaction between the adversary and the defender. It also provides quantitative metrics such as the time it takes the adversary to explore the network and compromise the target nodes. Based on these metrics, the defender can reboot the network devices and reset the network topology in time to clean up all potentially compromised devices and to protect the critical nodes. The hypergame provides useful guidance on how to create cyber deceptions so that the adversary cannot obtain information about the correct network topology and can be deterred from reaching the target critical nodes on a military network while it is in service.

This work presents ChainFS, a middleware system that secures cloud storage services using a minimally trusted Blockchain. ChainFS hardens the cloud-storage security against forking attacks. The ChainFS middleware exposes a file-system interface to end users. Internally, ChainFS stores data files in the cloud and exports minimal and necessary functionalities to the Blockchain for key distribution and file operation logging. We implement the ChainFS system on Ethereum and S3FS and closely integrate it with FUSE clients and Amazon S3 cloud storage. We measure the system performance and demonstrate low overhead.