NDLI: SSENet-2014 Dataset: A Dataset for Detection of Multiconnection Attacks

Content Provider	IEEE Xplore Digital Library
Author	Bhattacharya, S. Selvakumar, S.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Nat. Inst. of Technol., Tiruchirappalli, India (Bhattacharya, S.; Selvakumar, S.)
Abstract	Multiconnection attacks such as DoS, probe, flooding, etc., have become common and attackers have come out with sophisticated techniques as well as tools to launch variants of such attacks. This growing amount of attack and sophistication has given rise to the increasing need of efficient detection algorithm. To test and compare the performances of the proposed detection algorithms, benchmark datasets are required to represent the dynamic nature of the network. Though certain benchmark datasets are available, most datasets are either synthetic or contains suppressed information. In this paper, we introduce SSENet-2014 dataset which is generated in a real network environment. The attacks were generated using attack tools while carrying out normal activities. The description of the SSENet-2014 dataset is given. Then, a comparison is carried out with the most popular intrusion detection dataset, 10% KDD Cup 99. Two clustering approaches of K Means and Self Organizing Map (SOM) have been used in our experiments. Box plot is used to analyze the attributes of the two datasets. The results confirm the variability existing in the attribute values of 10% KDD Cup 99 and SSENet-2014 dataset. Also, it can be seen that SSENet-2014 dataset generated from a real network varies considerably from 10% KDD Cup 99 which is generated from simulated traffic.
Starting Page	121
Ending Page	126
File Size	610544
Page Count	6
File Format	PDF
e-ISBN	9781479970025
DOI	10.1109/Eco-friendly.2014.100
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-18
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Protocols K Means SSENet Testbed Creation Multiconnection Attacks Organizations Benchmark testing Self Organizing Map Dataset Analysis IP networks Probes Detection algorithms
Content Type	Text
Resource Type	Article

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