Survey on Detecting Port Scan Attempts with Combined Analysis of Support Vector Machine and Deep Learning Algorithms
Manasee Kurkure
IDS, SVM, CICIDS2017, Cyber Terror, Deep Learning
Compared to the past security of networked systems has become a critical universal issue that influences individuals, enterprises and governments. The rate of attacks against networked systems has increased melodramatically, and the strategies used by the attackers are continuing to evolve. For example, the privacy of important information, security of stored data platforms, availability of knowledge etc. Depending on these problems, cyber terrorism is one of the most important issues in today’s world. Cyber terror, which caused a lot of problems to individuals and institutions, has reached a level that could threaten public and country security by various groups such as criminal organizations, professional persons and cyber activists. Intrusion detection is one of the solutions against these attacks. A free and effective approach for designing Intrusion Detection Systems (IDS) is Machine Learning. In this study, deep learning and support vector machine (SVM) algorithms were used to detect port scan attempts based on the new CICIDS2017 dataset.
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Unique Paper ID: 148336

Publication Volume & Issue: Volume 6, Issue 1

Page(s): 0 - 0
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Volume 6 Issue 11

Last Date 25 April 2020

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