Biblio

Adversarial attacks have been the hot research field in artificial intelligence security. Decision-based black-box adversarial attacks are much more appropriate in the real-world scenarios, where only the final decisions of the targeted deep neural networks are accessible. However, since there is no available guidance for searching the imperceptive adversarial perturbation, boundary attack, one of the best performing decision-based black-box attacks, carries out computationally expensive search. For improving attack efficiency, we propose a novel black box explanation guided decision-based black-box adversarial attack. Firstly, the problem of decision-based adversarial attacks is modeled as a derivative-free and constraint optimization problem. To solve this optimization problem, the black box explanation guided constrained random search method is proposed to more quickly find the imperceptible adversarial example. The insights into the targeted deep neural networks explored by the black box explanation are fully used to accelerate the computationally expensive random search. Experimental results demonstrate that our proposed attack improves the attack efficiency by 64% compared with boundary attack.

Publically available relational data without security protection may cause data protection issues. Watermarking facilitates solution for remote sharing of relational database by ensuring data integrity and security. In this research, a reversible watermarking for numerical relational database by using evolutionary technique has been proposed that ensure the integrity of underlying data and robustness of watermark. Moreover, mRMR based feature subset selection technique has been used to select attributes for implementation of watermark instead of watermarking whole database. Binary Bat algorithm has been used as constraints optimization technique for watermark creation. Experimental results have shown the effectiveness of the proposed technique against data tempering attacks. In case of alteration attacks, almost 70% data has been recovered, 50% in deletion attacks and 100% data is retrieved after insertion attacks. The watermarking based on evolutionary technique (WET) i.e., mRMR based Binary Bat Algorithm ensures the data accuracy and it is resilient against malicious attacks.

Because of poor performance of heuristic algorithms on virtual machine placement problem in cloud environments, a multi-objective constraint optimization model of virtual machine placement is presented, which taking energy consumption and resource wastage as the objective. We solve the model based on the proposed discrete firefly algorithm. It takes firefly's location as the placement result, brightness as the objective value. Its movement strategy makes darker fireflies move to brighter fireflies in solution space. The continuous position after movement is discretized by the proposed discrete strategy. In order to speed up the search for solution, the local search mechanism for the optimal solution is introduced. The experimental results in OpenStack cloud platform show that the proposed algorithm makes less energy consumption and resource wastage compared with other algorithms.