NDLI: Network intrusion detection system using J48 Decision Tree

Content Provider	IEEE Xplore Digital Library
Author	Sahu, S. Mehtre, B.M.
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. & Inf. Sci., Univ. of Hyderabad, Hyderabad, India (Sahu, S.) \|\| CIAM Lab., IDRBT, Hyderabad, India (Mehtre, B.M.)
Abstract	As the number of cyber attacks have increased, detecting the intrusion in networks become a very tough job. For network intrusion detection system (NIDS), many data mining and machine learning techniques are used. However, for evaluation, most of the researchers used KDD Cup 99 data set, which has widely criticized for not showing current network situation. In this paper we used a new labelled network dataset, called Kyoto 2006+ dataset. In Kyoto 2006+ data set, every instant is labelled as normal (no attack), attack (known attack) and unknown attack. We use Decision Tree (J48) algorithm to classify the network packet that can be used for NIDS. For training and testing we used 134665 network instances. The generated rules works with 97.2% correctness for detecting the connection i.e., no attack, known attack or unknown attack.
Starting Page	2023
Ending Page	2026
File Size	5326369
Page Count	4
File Format	PDF
ISBN	9781479987900
e-ISBN	9781479987924
DOI	10.1109/ICACCI.2015.7275914
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-10
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Kyoto data set Accuracy Machine learning algorithms Intrusion detection J48 algorithm Feature extraction Data Mining Decision trees Data mining Intrusion Detection System Decision tree
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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