Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms

The appealing features of Cloud Computing continue to fuel its adoption and its integration in many sectors such industry, governments, education and entertainment. Nevertheless, uploading sensitive data to public cloud storage services poses security risks such as integrity, availability and confidentiality to organizations. Moreover, the open and distributed (decentralized) structure of the cloud has resulted this class of computing, prone to cyber attackers and intruders. Thereby, it is imperative to develop an anomaly network intrusion system to detect and prevent both inside and outside assaults in cloud environment with high detection precision and low false warnings. In this work, we propose an intelligent approach to build automatically an efficient and effective Deep Neural Network (DNN) based anomaly Network IDS using a hybrid optimization framework (IGASAA) based on Improved Genetic Algorithm (IGA) and Simulated Annealing Algorithm (SAA). The IDS resulted is called “MLIDS” (Machine Learning based Intrusion Detection System). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Moreover, SAA was incorporated to IGA with the aim to optimize its heuristic search. Our approach consists of using IGASAA in order to search the optimal or near-optimal combination of most relevant values of the parameters included in construction of DNN based IDS or impacting its performance, like feature selection, data normalization, architecture of DNN, activation function, learning rate and Momentum term, which ensure high detection rate, high accuracy and low false alarm rate. For simulation and validation of the proposed method, CloudSim 4.0 simulator platform and three benchmark IDS datasets were used, namely CICIDS2017, NSL-KDD version 2015 and CIDDS-001. The implementation results of our model demonstrate its ability to detect intrusions with high detection accuracy and low false alarm rate, and indicate its superiority in comparison with state-of-the-art methods.