A NOVEL DEEP BELIEF-BASED MODEL FOR THE INTRUSION DETECTION OF NETWORK TRAFFIC

ABSTRACT

The fast expansion of networked systems, combined with the pervasive reliance on the internet, has raised worries about network security, needing new defense methods. Intrusion Detection Systems (IDS) use a variety of ways to distinguish between legitimate and malicious network traffic, including rule-based, signature-based, anomaly detection, and machine learning approaches. While signature-based IDS excel at detecting known threats, they struggle with novel attacks, prompting the development of anomaly-based IDS and machine learning methods such as Random Forest and Logistic Regression, among others. However, these techniques have scalability and computational complexity challenges. Cybersecurity remains a significant concern for organizations due to continual cyber-attack threats, which drives ongoing development into intrusion detection systems. Deep Learning (DL)-based IDSs have gained popularity due to their deep feature learning capabilities, while being resource-intensive. To overcome computational problems, this research provides an optimized deep belief-based model that combines the Genetic Algorithm, Particle Swarm Optimization, and Probabilistic Neural Network (GePP-Dbnet). This model seeks to find a balance between accuracy, training duration, and false alarm rates while identifying a diverse set of threat classes. Validation will be carried out using the benchmark datasets NSL-KDD and CSE-CIC-IDS2018, which provide realistic scenarios for assessing the model's effectiveness.

Keywords: Intrusion Detection, Attacks, Deep Learning, Deep Belief, Dataset.