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Performance Analysi...
Performance Analysis of Anomaly Based Network Intrusion Detection Systems
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- Abedin, Md. Zainal (författare)
- University of Science and Technology, Chittagong
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- Siddiquee, Kazy Noor E Alam (författare)
- University of Science and Technology Chittagong
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- Bhuyan, M. S. (författare)
- University of Science & Technology Chittagong
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- Karim, Razuan (författare)
- University of Science and Technology Chittagong
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- Hossain, Mohammad Shahadat, 1968- (författare)
- University of Chittagong, Bangladesh
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- Andersson, Karl, 1970- (författare)
- Luleå tekniska universitet,Datavetenskap
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(creator_code:org_t)
- Piscataway, NJ : IEEE Computer Society, 2018
- 2018
- Engelska.
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Ingår i: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops). - Piscataway, NJ : IEEE Computer Society. ; , s. 1-7
- Relaterad länk:
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http://ieeelcn.org
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https://ltu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Medieteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Media and Communication Technology (hsv//eng)
Nyckelord
- Intrusion detection systems
- machine learning
- NSL-KDD
- feature selection
- classification model
- performance analysis
- Pervasive Mobile Computing
- Distribuerade datorsystem
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)