Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to problem of storage computing. Thereby, most organizations are now migratingtheir services into cloud due its appealing features tangible advantages. Nevertheless, providing privacy security protect assets resources still very challenging issue. To address aboveissues, we propose smart approach construct automatically an efficient effective anomaly network IDS based on Deep Neural Network, by using novel hybrid optimization framework “ISAGASAA”. ISAGASAA combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) Simulated Annealing Algorithm (SAA). Our consists with aim seeking optimal or near combination pertinent values parametersincluded in building DNN impacting performance, which guarantee high detection rate, accuracy low false alarm rate. The experimental results turn out capability uncover intrusionswith demonstrate superiority comparison stateof-the-art methods.