Performance Analysis of Anomaly Based Network Intrusion Detection Systems
| Autor: | M. S. Bhuyan, Mohammad Shahadat Hossein, Md. Zainal Abedin, Kazy Noor E Alam Siddiquee, Karl Andersson, Razuan Karim |
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| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Network security
Computer science 02 engineering and technology Intrusion detection system computer.software_genre Medieteknik Data modeling Naive Bayes classifier feature selection Data integrity 0202 electrical engineering electronic engineering information engineering performance analysis Media and Communication Technology Vulnerability (computing) NSL-KDD business.industry classification model Computer Sciences Supervised learning 020206 networking & telecommunications Random forest Statistical classification machine learning Datavetenskap (datalogi) Multilayer perceptron Intrusion detection systems 020201 artificial intelligence & image processing Data mining business computer Computer network |
| Zdroj: | LCN Workshops |
| Popis: | Because of the increased popularity and fast expansion of the Internet as well as Internet of things, networks are growing rapidly in every corner of the society. As a result, huge amount of data is travelling across the computer networks that lead to the vulnerability of data integrity, confidentiality and reliability. So, network security is a burning issue to keep the integrity of systems and data. The traditional security guards such as firewalls with access control lists are not anymore enough to secure systems. To address the drawbacks of traditional Intrusion Detection Systems (IDSs), artificial intelligence and machine learning based models open up new opportunity to classify abnormal traffic as anomaly with a self-learning capability. Many supervised learning models have been adopted to detect anomaly from networks traffic. In quest to select a good learning model in terms of precision, recall, area under receiver operating curve, accuracy, F-score and model built time, this paper illustrates the performance comparison between Naïve Bayes, Multilayer Perceptron, J48, Naïve Bayes Tree, and Random Forest classification models. These models are trained and tested on three subsets of features derived from the original benchmark network intrusion detection dataset, NSL-KDD. The three subsets are derived by applying different attributes evaluator’s algorithms. The simulation is carried out by using the WEKA data mining tool. A belief-rule-based DSS to assess flood risks by using wireless sensor networks |
| Databáze: | OpenAIRE |
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