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Open AccessProceedings Article

A study of Intrusion Detection using data mining

Moorthy, +1 more
- pp 8-15
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This article is published in IEEE-International Conference On Advances In Engineering, Science And Management.The article was published on 2012-01-01 and is currently open access. It has received 10 citations till now. The article focuses on the topics: Intrusion detection system & Feature extraction.

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Proceedings ArticleDOI

A new feature selection IDS based on genetic algorithm and SVM

Hossein Gharaee, +1 more
TL;DR: This study proposes a method which can achieve more stable features in comparison with other techniques, and can reach high accuracy and low false positive rate (FPR) simultaneously, though it had earlier been achieved in earlier studies separately.
Proceedings ArticleDOI

Overview of intrusion detection using data-mining and the features selection

Nadya El Moussaid, +1 more
TL;DR: The main goal of this paper is to expose data mining techniques and approaches to improve the performance of the traditional intrusion detection system to identify known and unknown attack's patterns.

Anomaly Intrusion Detection Techniques: A Brief Review

Anurag Jain, +2 more
TL;DR: The various techniques of anomaly based intrusion detection system reported in the literature have been sorted out on the parameters like their strength and weakness and direction to intrusion detection methods based on ensemble of ML techniques are given.
Journal ArticleDOI

Web Application Attacks Detection: A Survey and Classification

Nadya El Moussaid, +1 more
- 18 Oct 2014 - 
TL;DR: This paper presents web intrusion detection system based on detection of web vulnerabilities, also approaches to improve the web application security using intrusion detection systems and scanners based on machine learning and artificial intelligence.
Journal ArticleDOI

Integrity Model based Intrusion Detection System: A Practical Approach

Qamar RayeesKhan, +3 more
- 22 Apr 2015 - 
TL;DR: The proposed research paper tries to propose a model for improving the optimum information Integrity by quantifying the intrinsic integrity attributes so that the data may not get compromised.