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Intrusion Detection System Classification Using Different Machine Learning Algorithms on KDD-99 and NSL-KDD Datasets - A Review Paper
- Source :
- International Journal of Computer Science and Information Technology. 11:65-80
- Publication Year :
- 2019
- Publisher :
- Academy and Industry Research Collaboration Center (AIRCC), 2019.
-
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.
- Subjects :
- Computer science
business.industry
020206 networking & telecommunications
02 engineering and technology
Intrusion detection system
Machine learning
computer.software_genre
Term (time)
Constant false alarm rate
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Anomaly detection
Artificial intelligence
False alarm
Detection rate
business
Algorithm
computer
Subjects
Details
- ISSN :
- 09753826 and 09754660
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- International Journal of Computer Science and Information Technology
- Accession number :
- edsair.doi...........23edc76c4b7ebfb6d00c16613901ae3f