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