Automated Intrusion Detection Using Deep Learning Techniques

Intrusion Detection Systems (IDSs) are essential for keeping an eye on and identifying dangerous or unauthorized activity occurring within computer networks. Conventional intrusion detection systems, which frequently depend on rule-based detection techniques, find it difficult to adjust to new attack patterns and changing cyber threats. This work suggests a sophisticated IDS architecture that combines Long Short-Term Memory (LSTM) networks with Deep Convolutional Neural Networks (DCNN) in a hybrid deep learning technique to get over these drawbacks. In order to improve detection accuracy, LSTM is utilized to learn temporal and sequential relationships, whereas DCNN focuses on capturing spatial patterns within network traffic data to boost detection accuracy. The NSL-KDD dataset, a refined and generally recognized benchmark for intrusion detection research, is used to train and assess the suggested model. According to experimental results, indicate that the DCNN-LSTM system is an effective and trustworthy approach for detecting threats in real-time within complex and rapidly changing network environments, offering superior detection rates and classification accuracy.