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Intrusion Detection System Classification using Different Machine Learning Algorithms on KDD-99 and NSL-KDD Datasets


Affiliations
1 Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan, United States
2 School of Information Security and Applied Computing, Eastern Michigan University, Ypsilanti, Michigan, United States
 

Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Keywords

Intrusion Detection System, KDD-99 Cup, NSL-KDD, Machine Learning Algorithms.
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  • Intrusion Detection System Classification using Different Machine Learning Algorithms on KDD-99 and NSL-KDD Datasets

Abstract Views: 218  |  PDF Views: 264

Authors

Ravipati Rama Devi
Department of Computer Science, Eastern Michigan University, Ypsilanti, Michigan, United States
Munther Abualkibash
School of Information Security and Applied Computing, Eastern Michigan University, Ypsilanti, Michigan, United States

Abstract


Intrusion Detection System (IDS) has been an effective way to achieve higher security in detecting malicious activities for the past couple of years. Anomaly detection is an intrusion detection system. Current anomaly detection is often associated with high false alarm rates and only moderate accuracy and detection rates because it’s unable to detect all types of attacks correctly. An experiment is carried out to evaluate the performance of the different machine learning algorithms using KDD-99 Cup and NSL-KDD datasets. Results show which approach has performed better in term of accuracy, detection rate with reasonable false alarm rate.

Keywords


Intrusion Detection System, KDD-99 Cup, NSL-KDD, Machine Learning Algorithms.

References