Journal ArticleDOI
Toward developing a systematic approach to generate benchmark datasets for intrusion detection
TLDR
The intent for this dataset is to assist various researchers in acquiring datasets of this kind for testing, evaluation, and comparison purposes, through sharing the generated datasets and profiles.About:
This article is published in Computers & Security.The article was published on 2012-05-01. It has received 1050 citations till now. The article focuses on the topics: Intrusion detection system.read more
Citations
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Proceedings ArticleDOI
Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization
TL;DR: A reliable dataset is produced that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable and evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.
Journal ArticleDOI
A survey of network anomaly detection techniques
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.
Journal ArticleDOI
Network Anomaly Detection: Methods, Systems and Tools
TL;DR: This paper provides a structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomalies detection.
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.
Journal ArticleDOI
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.
References
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Journal ArticleDOI
The origin of bursts and heavy tails in human dynamics
TL;DR: It is shown that the bursty nature of human behaviour is a consequence of a decision-based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times.
Book
The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling
TL;DR: The intended audience and the goals of the book are to provide computer professionals simple and straightforward performance analysis techniques in a comprehensive textbook.
Proceedings ArticleDOI
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
Robin Sommer,Vern Paxson +1 more
TL;DR: The main claim is that the task of finding attacks is fundamentally different from these other applications, making it significantly harder for the intrusion detection community to employ machine learning effectively.
Journal ArticleDOI
Testing Intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory
TL;DR: The purpose of this article is to attempt to identify the shortcomings of the Lincoln Lab effort in the hope that future efforts of this kind will be placed on a sounder footing.
Journal ArticleDOI
Scale-free topology of e-mail networks.
TL;DR: The resulting network exhibits a scale-free link distribution and pronounced small-world behavior, as observed in other social networks, implying that the spreading of e-mail viruses is greatly facilitated in real e- mail networks compared to random architectures.
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