An Efficient Intrusion Detection System Using Feature Selection and Long Short-Term Memory (LSTM)

As the number of services available over the Internet increases, the network infrastructure becomes more vulnerable to malicious cyber threats. To address such issues, various techniques have been adopted in order to mitigate such malicious activity in the network. A hardware or software tool called an intrusion detection system (IDS) can watch a network for fraudulent attacks that could cause the system to malfunction. A number of deep learning and machine learning methods are being used in order to demonstrate their effectiveness in detecting such attacks. In this study, a feature selection technique and a deep learning approach based upon long short-term memory (LSTM) classifier are used to detect attacks. The proposed system was implemented on a benchmark dataset, the CICDDoS2019, and it was observed that the model achieves the best accuracy while training and testing. Additionally, a comparison with other related works shows that the model is more effective when encountering attacks.