Open AccessJournal Article
Machine learning techniques for intrusion detection
TLDR
A framework for building intrusion detection models is described that can distinguish between legitimate and illegitimate traffic and can able to signals attacks in real time, before malicious attacks occur.Abstract:
In cyber security, intrusion detection is the act of detecting malicious attacks. Unauthorized users would never gain access to the system. The computer security is to limit the access to a computer system. An Intrusion detection system (IDS) is a software that monitors a single or a network of computers from malicious activities that steals the system information. IDS can distinguish between legitimate and illegitimate traffic and can able to signals attacks in real time, before malicious attacks occur. In this paper we describe a framework for building intrusion detection (ID) models. Machine learning algorithms are used for detecting attacks and helps the users to develop secure information systems.read more
Citations
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Book ChapterDOI
A Deep Learning Approach for Network Intrusion Detection Using Non-symmetric Auto-encoder
TL;DR: In this article, a non-symmetric deep auto-encoder (NDAE-IDS) is proposed to detect network-based intrusions by NIDS in binary class and multi-class problems.
References
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Journal ArticleDOI
Deep Learning Approach for Intelligent Intrusion Detection System
R. Vinayakumar,Mamoun Alazab,K. P. Soman,Prabaharan Poornachandran,Ameer Al-Nemrat,Sitalakshmi Venkatraman +5 more
TL;DR: A highly scalable and hybrid DNNs framework called scale-hybrid-IDS-AlertNet is proposed which can be used in real-time to effectively monitor the network traffic and host-level events to proactively alert possible cyberattacks.
Proceedings ArticleDOI
Using Rough Set and Support Vector Machine for Network Intrusion Detection System
TL;DR: RST and SVM schema could improve the false positive rate and accuracy and the method is effective to decrease the space density of data.
Journal Article
A Survey on Machine Learning: Concept,Algorithms and Applications
Kajaree Das,Rabi Narayan Behera +1 more
TL;DR: This paper focuses on explaining the concept and evolution of Machine Learning, some of the popular Machine Learning algorithms and try to compare three most popular algorithms based on some basic notions.
Posted Content
Evaluation of Machine Learning Algorithms for Intrusion Detection System
TL;DR: In this article, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset, which succeeded to compute several performance metrics in order to evaluate the selected classifiers.
Proceedings ArticleDOI
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
TL;DR: DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS) and it is concluded that a DNN of 3 layers has superior performance over all the other classical machine learning algorithms.