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Open AccessArticle

A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques

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Department of Computer Engineering, Kwame Nkrumah University of Science and Technology, Kumasi AK000-AK911, Ghana
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Department of Telecommunications Engineering, Kwame Nkrumah University of Science and Technology, Kumasi AK000-AK911, Ghana
*
Author to whom correspondence should be addressed.
Computers 2019, 8(4), 85; https://doi.org/10.3390/computers8040085
Received: 17 October 2019 / Revised: 8 November 2019 / Accepted: 14 November 2019 / Published: 26 November 2019
A denial of service (DoS) attack in a computer network is an attack on the availability of computer resources to prevent users from having access to those resources over the network. Denial of service attacks can be costly, capable of reaching $100,000 per hour. Development of easily-accessible, simple DoS tools has increased the frequency and reduced the level of expertise needed to launch an attack. Though these attack tools have been available for years, there has been no proposed defense mechanism targeted specifically at them. Most defense mechanisms in literature are designed to defend attacks captured in datasets like the KDD Cup 99 dataset from 20 years ago and from tools no longer in use in modern attacks. In this paper, we capture and analyze traffic generated by some of these DoS attack tools using Wireshark Network Analyzer and propose a signature-based DoS detection mechanism based on SVM classifier to defend against attacks launched by these attack tools. Our proposed detection mechanism was tested with Snort IDS and compared with some already existing defense mechanisms in literature and had a high detection accuracy, low positive rate and fast detection time. View Full-Text
Keywords: denial of service; attack tool; defense; detection; signature-based denial of service; attack tool; defense; detection; signature-based
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MDPI and ACS Style

Djanie, K.S.; Tutu, T.E.; Dzisi, G.J. A Proposed DoS Detection Scheme for Mitigating DoS Attack Using Data Mining Techniques. Computers 2019, 8, 85.

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