Biblio
As millions of IoT devices are interconnected together for better communication and computation, compromising even a single device opens a gateway for the adversary to access the network leading to an epidemic. It is pivotal to detect any malicious activity on a device and mitigate the threat. Among multiple feasible security threats, malware (malicious applications) poses a serious risk to modern IoT networks. A wide range of malware can replicate itself and propagate through the network via the underlying connectivity in the IoT networks making the malware epidemic inevitable. There exist several techniques ranging from heuristics to game-theory based technique to model the malware propagation and minimize the impact on the overall network. The state-of-the-art game-theory based approaches solely focus either on the network performance or the malware confinement but does not optimize both simultaneously. In this paper, we propose a throughput-aware game theory-based end-to-end IoT network security framework to confine the malware epidemic while preserving the overall network performance. We propose a two-player game with one player being the attacker and other being the defender. Each player has three different strategies and each strategy leads to a certain gain to that player with an associated cost. A tailored min-max algorithm was introduced to solve the game. We have evaluated our strategy on a 500 node network for different classes of malware and compare with existing state-of-the-art heuristic and game theory-based solutions.
In this paper we propose a security and cost aware scheduling heuristic for real-time workflow jobs that process Internet of Things (IoT) data with various security requirements. The environment under study is a four-tier architecture, consisting of IoT, mist, fog and cloud layers. The resources in the mist, fog and cloud tiers are considered to be heterogeneous. The proposed scheduling approach is compared to a baseline strategy, which is security aware, but not cost aware. The performance evaluation of both heuristics is conducted via simulation, under different values of security level probabilities for the initial IoT input data of the entry tasks of the workflow jobs.
Fog computing is a new computing paradigm that utilizes numerous mutually cooperating terminal devices or network edge devices to provide computing, storage, and communication services. Fog computing extends cloud computing services to the edge of the network, making up for the deficiencies of cloud computing in terms of location awareness, mobility support and latency. However, fog nodes are not active enough to perform tasks, and fog nodes recruited by cloud service providers cannot provide stable and continuous resources, which limits the development of fog computing. In the process of cloud service providers using the resources in the fog nodes to provide services to users, the cloud service providers and fog nodes are selfish and committed to maximizing their own payoffs. This situation makes it easy for the fog node to work negatively during the execution of the task. Limited by the low quality of resource provided by fog nodes, the payoff of cloud service providers has been severely affected. In response to this problem, an appropriate incentive mechanism needs to be established in the fog computing environment to solve the core problems faced by both cloud service providers and fog nodes in maximizing their respective utility, in order to achieve the incentive effect. Therefore, this paper proposes an incentive model based on repeated game, and designs a trigger strategy with credible threats, and obtains the conditions for incentive consistency. Under this condition, the fog node will be forced by the deterrence of the trigger strategy to voluntarily choose the strategy of actively executing the task, so as to avoid the loss of subsequent rewards when it is found to perform the task passively. Then, using evolutionary game theory to analyze the stability of the trigger strategy, it proves the dynamic validity of the incentive consistency condition.