2019/05/14 by Ghazi Al-Naymatm, Al-Naymatm, Ghazi, Ahmed Hambouz +3
Computer Science · #Advanced Malware Detection Techniques #Anomaly Detection Techniques and Applications #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.1906.00865
openalex publication_date 2019/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many approaches have evolved to enhance network attacks detection anomaly using SNMP-MIBs. Most of these approaches focus on machine learning algorithms with a lot of SNMP-MIB database parameters, which may consume most of hardware resources (CPU, memory, and bandwidth). In this paper we introduce an efficient detection model to detect network attacks anomaly using Lazy.IBk as a machine learning classifier and Correlation, and ReliefF as attribute evaluators on SNMP-MIB interface parameters. This model achieved accurate results (100%) with minimal hardware resources consumption. Thus, this model can be adopted in intrusion detection system (IDS) to increase its performance and efficiency.