TY  - EJOU
AU  - Karthic, S. 
AU  - Kumar, S. Manoj 

TI  - Wireless Intrusion Detection Based on Optimized LSTM with Stacked Auto Encoder Network
T2  - Intelligent Automation \& Soft Computing

PY  - 2022
VL  - 34
IS  - 1
SN  - 2326-005X

AB  - In recent years, due to the rapid progress of various technologies, wireless computer networks have developed. However, the activities of the security threats and attackers affect the data communication of these technologies. So, to protect the network against these security threats, an efficient IDS (Intrusion Detection System) is presented in this paper. Namely, optimized long short-term memory (OLSTM) network with a stacked auto-encoder (SAE) network is proposed as an IDS system. Using SAE, significant features are extracted from the databases such as input NSL-KDD database and the UNSW-NB15 database. Then extracted features are given as input to the optimized LSTM which is used as an intrusion identification system. To enhance the effectiveness of the LSTM, we present the pigeon optimization algorithm (POA). Using this algorithm, weight parameters of the LSTM are chosen optimally. Finally, the proposed IDS model decides whether the input packets are intruded or not. The results confirm that the proposed IDS model surpasses the previous machine learning-based IDS models in terms of correctness, F1-score and G mean.
KW  - Optimized LSTM; stacked autoencoder; pigeon optimization algorithm; and intrusion detection system

DO  - 10.32604/iasc.2022.025153