Nouf Saeed Alotaibi^*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2061-2078, 2023, DOI:10.32604/cmc.2023.039446

Abstract Intrusion Detection System (IDS) is a network security mechanism that analyses all users’ and applications’ traffic and detects malicious activities in real-time. The existing IDS methods suffer from lower accuracy and lack the required level of security to prevent sophisticated attacks. This problem can result in the system being vulnerable to attacks, which can lead to the loss of sensitive data and potential system failure. Therefore, this paper proposes an Intrusion Detection System using Logistic Tanh-based Convolutional Neural Network Classification (LTH-CNN). Here, the Correlation Coefficient based Mayfly Optimization (CC-MA) algorithm is used to extract the input characteristics for the IDS… More >

S. Karthic^1,*, S. Manoj Kumar²

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 439-453, 2022, DOI:10.32604/iasc.2022.025153

Abstract 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… More >