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Performance Analysis of Anomaly Based Network Intrusion Detection Systems
University of Science and Technology, Chittagong.
University of Science and Technology Chittagong.
University of Science & Technology Chittagong.
University of Science and Technology Chittagong.
Visa övriga samt affilieringar
2018 (Engelska)Ingår i: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, s. 1-7Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

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.

Ort, förlag, år, upplaga, sidor
Piscataway, NJ: IEEE Computer Society, 2018. s. 1-7
Serie
Proceedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops)
Nyckelord [en]
Intrusion detection systems, machine learning, NSL-KDD, feature selection, classification model, performance analysis
Nationell ämneskategori
Datavetenskap (datalogi) Medieteknik
Forskningsämne
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-70317DOI: 10.1109/LCNW.2018.8628599ISI: 000461284400001Scopus ID: 2-s2.0-85062829453OAI: oai:DiVA.org:ltu-70317DiVA, id: diva2:1237757
Konferens
43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018
Projekt
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Forskningsfinansiär
Vetenskapsrådet, 2014-4251Tillgänglig från: 2018-08-09 Skapad: 2018-08-09 Senast uppdaterad: 2019-07-11Bibliografiskt granskad

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Andersson, Karl

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