Biblio

Currently, mobile ad hoc networks (MANETs) are widely used due to its self-configuring feature. However, it is vulnerable to the malicious jammers in practice. Traditional anti-jamming approaches, such as channel hopping based on deterministic sequences, may not be the reliable solution against intelligent jammers due to its fixed patterns. To address this problem, we propose a distributed game theory-based multi-agent anti-jamming (DMAA) algorithm in this paper. It enables each user to exploit all information from its neighboring users before the network attacks, and derive dynamic local policy knowledge to overcome intelligent jamming attacks efficiently as well as guide the users to cooperatively hop to the same channel with high probability. Simulation results demonstrate that the proposed algorithm can learn an optimal policy to guide the users to avoid malicious jamming more efficiently and rapidly than the random and independent Q-learning baseline algorithms,

Integrity verification of cloud data is of great importance for secure and effective cloud storage since attackers can change the data even though it is encrypted. Traditional integrity verification schemes only let the client know the integrity status of the remote data. When the data is corrupted, the system cannot hold the server accountable. Besides, almost all existing schemes assume that the users are credible. Instead, especially in a dynamic operation environment, users can deny their behaviors, and let the server bear the penalty of data loss. To address the issues above, we propose an accountable dynamic storage integrity verification (ADS-IV) scheme which provides means to detect or eliminate misbehavior of all participants. In the meanwhile, we modify the Invertible Bloom Filter (IBF) to recover the corrupted data and use the Mahalanobis distance to calculate the degree of damage. We prove that our scheme is secure under Computational Diffie-Hellman (CDH) assumption and Discrete Logarithm (DL) assumption and that the audit process is privacy-preserving. The experimental results demonstrate that the computational complexity of the audit is constant; the storage overhead is \$O(\textbackslashtextbackslashsqrt n )\$, which is only 1/400 of the size of the original data; and the whole communication overhead is O(1).As a result, the proposed scheme is not only suitable for large-scale cloud data storage systems, but also for systems with sensitive data, such as banking systems, medical systems, and so on.