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Intrusion Detection using Deep Learning Long Short-term Memory with Wrapper Feature Selection Method

Recently, many companies move to use cloud com-puting systems to enhance their performance and productivity. Using these cloud computing systems allows the execution of applications, data, and infrastructures on cloud platforms (i.e., online), which increase the number of attacks on such systems. As a resulting, building robust Intrusion detection systems (IDS) is needed. The main goal of IDS is to detect normal and abnormal network trafﬁc. In this paper, we propose a hybrid approach between an Enhanced Binary Genetic Algorithms (EBGA) as a wrapper feature selection (FS) algorithm and Long Short-Term Memory (LSTM). A novel injection method to prevent premature convergence of the GA is proposed in this paper. An intelligent k-means algorithm is employed to examine the solution distribution in the search space. Once 80% of the solutions belong to one cluster, an injection method (i.e., add new solutions) is used to redistribute the solutions over the search space. EBGA will reduce the search space as a preprocessing step, while LSTM works as a binary classiﬁcation method. UNSW-NB15, a real-world public dataset, is used in this work to evaluate the proposed system. The obtained results show the ability of feature selection method to enhance the overall performance of LSTM.