Anomaly-based Intrusion Detection Systems are critical components of modern security systems. They often rely on Machine Learning (ML) to detect potential attacks and have gained increased popularity over time, due to new technologies and dangers. There are many proposed anomaly-based systems using different ML algorithms and techniques, however there is no standard benchmark to compare these based on quantifiable measures.
We have proposed a benchmark that measures both accuracy and performance to produce objective metrics that can be used in the evaluation of each algorithm implementation. In this paper, the benchmark will be used to compare four different ML algorithms (Naive Bayes, Support Vector Machines, Neural Networks, and K-means Clustering) on the NSL-KDD dataset. The experimental results show the differences in accuracy and performance between these algorithms on the dataset, and also how this benchmark can be used to create useful metrics for comparisons.
|Commitee:||Ebert, Todd, Zhang, Wenlu|
|School:||California State University, Long Beach|
|Department:||Computer Engineering and Computer Science|
|School Location:||United States -- California|
|Source:||MAI 57/05M(E), Masters Abstracts International|
|Keywords:||Anomaly-based, Benchmark, Cybersecurity, Intrusion detection systems, Machine learning, Nsl-kdd|
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