TY - GEN
T1 - Evaluating the Impact of Feature Selection Methods on SNMP-MIB Interface Parameters to Accurately Detect Network Anomalies
AU - Al-Naymat, Ghazi
AU - Hambouz, Ahmed
AU - Al-Kasassbeh, Mouhammd
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Many approaches have evolved to enhance the process of detecting network anomalies 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 the hardware resources (CPU, memory, and bandwidth). In this paper, we introduce an efficient detection model to detect network anomalies using Lazy. IBk as a machine learning classifier, Correlation, and ReliefF as an approach for attribute evaluators only SNMP-MIB interface parameters. This model achieves a high accuracy of 99.94% with minimal hardware resources consumption. Thus, this model can be adopted in the intrusion detection system (IDS) to increase its performance and efficiency.
AB - Many approaches have evolved to enhance the process of detecting network anomalies 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 the hardware resources (CPU, memory, and bandwidth). In this paper, we introduce an efficient detection model to detect network anomalies using Lazy. IBk as a machine learning classifier, Correlation, and ReliefF as an approach for attribute evaluators only SNMP-MIB interface parameters. This model achieves a high accuracy of 99.94% with minimal hardware resources consumption. Thus, this model can be adopted in the intrusion detection system (IDS) to increase its performance and efficiency.
KW - Network attacks
KW - SNMP
KW - SNMP-MIB interface parameters
KW - anomaly detection
UR - https://www.scopus.com/pages/publications/85081383240
U2 - 10.1109/ISSPIT47144.2019.9001882
DO - 10.1109/ISSPIT47144.2019.9001882
M3 - Conference contribution
AN - SCOPUS:85081383240
T3 - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
BT - 2019 IEEE 19th International Symposium on Signal Processing and Information Technology, ISSPIT 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2019
Y2 - 10 December 2019 through 12 December 2019
ER -