Journal Article10.1016/J.JNCA.2015.11.016
A survey of network anomaly detection techniques
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TL;DR: This paper presents an in-depth analysis of four major categories of anomaly detection techniques which include classification, statistical, information theory and clustering and evaluates effectiveness of different categories of techniques.
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About: This article is published in Journal of Network and Computer Applications. The article was published on 01 Jan 2016. The article focuses on the topics: Intrusion detection system & Anomaly detection.
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Citations
GANomaly : semi-supervised anomaly detection via adversarial training.
Samet Akcay,Amir Atapour-Abarghouei,Toby P. Breckon +2 more
- 02 Dec 2018
TL;DR: In this paper, a conditional generative adversarial network (GAN) is used for anomaly detection in a one-class, semi-supervised learning paradigm, where an encoder-decoder-encoder sub-network is employed to map the input image to a lower dimension vector, which is then used to reconstruct the generated output image.
Survey of intrusion detection systems: techniques, datasets and challenges
TL;DR: A taxonomy of contemporary IDS is presented, a comprehensive review of notable recent works, and an overview of the datasets commonly used for evaluation purposes are presented, and evasion techniques used by attackers to avoid detection are presented.
Network Intrusion Detection for IoT Security Based on Learning Techniques
TL;DR: This survey classifies the IoT security threats and challenges for IoT networks by evaluating existing defense techniques and provides a comprehensive review of NIDSs deploying different aspects of learning techniques for IoT, unlike other top surveys targeting the traditional systems.
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A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
655
A Unifying Review of Deep and Shallow Anomaly Detection
Lukas Ruff,Jacob R. Kauffmann,Robert A. Vandermeulen,Grégoire Montavon,Wojciech Samek,Marius Kloft,Thomas G. Dietterich,Klaus-Robert Müller +7 more
- 04 Feb 2021
TL;DR: Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large collections of images or text as mentioned in this paper, and led to the introduction of a great variety of new methods.
References
Enhancing Big Data Security with Collaborative Intrusion Detection
Zhiyuan Tan,Upasana T. Nagar,Xiangjian He,Priyadarsi Nanda,Ren Ping Liu,Song Wang,Jiankun Hu +6 more
TL;DR: Vulnerabilities in cloud computing are studied and a collaborative IDS framework is proposed to enhance the security and privacy of big data.
Novel Approach for Network Traffic Pattern Analysis using Clustering-based Collective Anomaly Detection
TL;DR: This paper proposes a framework for collective anomaly detection using a partitional clustering technique to detect anomalies based on an empirical analysis of an attack’s characteristics and validates its approach by comparing its results with those from existing techniques using benchmark datasets.
Rule-Based Intrusion Detection System for SCADA Networks
Yi Yang,Kieran McLaughlin,Tim Littler,Sakir Sezer,Haifeng Wang +4 more
- 01 Jan 2013
TL;DR: A rule-based IDS for SCADA-IDS for IEC 60870-5-104 driven SCADA networks using an in-depth protocol analysis and a Deep Packet Inspection (DPI) method is presented.
74
Patent
Method and system for confident anomaly detection in computer network traffic
Igor Balabine,Alexander Velednitsky +1 more
- 20 Feb 2015
TL;DR: In this paper, the authors present a system and methods for detecting anomalies in computer network traffic with fewer false positives and without the need for time-consuming and unreliable historical baselines.
63
Intrusion Detection using Artificial Neural Network
G Poojitha,K Naveen Kumar,P Jayarami Reddy +2 more
- 29 Jul 2010
TL;DR: An Artificial Neural Network approach for Intrusion Detection that works well in detecting different attacks accurately with less false positive and negative rate and it is comparable to those reported in the literature.
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