Deep Learning-Powered Intrusion Detection Systems Networks Using LSTM

Abstract

Novel methods for intrusion detection systems (IDS) are essential to safeguard network environments against cyber-attacks effectively. Traditional intrusion detection systems struggle to handle modern cyber threats due to their complex patterns. This study suggests implementing an intrusion detection system that utilizes long short-term memory (LSTM) networks to tackle this issue. Identifying network traffic patterns with temporal correlations poses a challenge for intrusion detection systems (IDS) due to limitations in current models. When dealing with sequential data like flow timings, packet sizes, and protocol specifics, it is essential to have a model that can analyze changing patterns in real time. The dataset CSE-CIC-IDS2018 serves as the foundation for this study, containing a wide range of important characteristics essential for thorough evaluation. It is essential to have an intrusion detection system (IDS) that can adapt to new network behaviors, protect user privacy, and address emerging cyber threats, as mentioned in the problem statement. The proposed LSTM-based approach enables us to address these challenges and adapt innovatively and dynamically to the constantly evolving cybersecurity threats.