Bidirectional Long Short-Term Memory Networks for Efficient Network Intrusion System Classification

The rise of cyber threats in the digital age has made network intrusion detection increasingly vital. Network intrusions can compromise sensitive information and disrupt services, leading to significant financial and reputational damage. Hence, swift intrusion detection is essential to mitigate harm. Manual intrusion detection requires a deep understanding of network behavior, strong analytical skills, and constant vigilance in monitoring traffic patterns. However, traditional methods often fail to identify sophisticated threats. Machine learning and deep learning methods show great potential for enhancing the accuracy and efficiency of network intrusion detection systems. This research explores the effectiveness of Bidirectional Long Short-Term Memory networks in detecting intrusions. Experimental findings indicate that the BiLSTM model surpasses traditional machine learning techniques (such as Random Forest, Naïve Bayes, and K-Nearest Neighbors) as well as other recurrent architectures (including Gated Recurrent Unit, Bidirectional Gated Recurrent Unit, and standard Long Short-Term Memory), achieving an impressive 98% accuracy in identifying network intrusions.