Journal ArticleDOI
Anomaly-based network intrusion detection: Techniques, systems and challenges
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TLDR
The main challenges to be dealt with for the wide scale deployment of anomaly-based intrusion detectors, with special emphasis on assessment issues are outlined.About:
This article is published in Computers & Security.The article was published on 2009-02-01. It has received 1712 citations till now. The article focuses on the topics: Anomaly-based intrusion detection system & Intrusion detection system.read more
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Book ChapterDOI
Adaptive Semantics-Aware Malware Classification
TL;DR: This paper investigates the topic modeling approaches as semantics-aware solutions to the classification of malware based on logs from dynamic malware analysis using a semi-supervised learning architecture to make use of unlabeled data in classification.
Dissertation
Feature selection for intrusion detection system
TL;DR: Six feature selection algorithms are developed, and their application to intrusion detection is evaluated, and they are compared with other algorithms.
Journal ArticleDOI
A fast and noise resilient cluster-based anomaly detection
TL;DR: This paper presents a new approach, called Collective Probabilistic Anomaly Detection (CPAD), in which, the distance of the incoming new samples and the existing SGMMs is calculated, and then the new cluster is labeled the same as of the closest cluster.
Journal ArticleDOI
Relevant Feature Selection Model Using Data Mining for Intrusion Detection System
TL;DR: A new feature selection model is proposed; this model can effectively select the most relevant features for intrusion detection and is not only able to yield high detection rates but also to speed up the detection process.
Proceedings ArticleDOI
AECID: A Self-learning Anomaly Detection Approach based on Light-weight Log Parser Models.
TL;DR: This paper introduces ÆCID, a new anomaly-based IDS approach that incorporates many features motivated by recent research results, including the automatic classification of events in a network, their correlation, evaluation, and interpretation up to a dynamically-configurable alerting system.
References
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Journal ArticleDOI
LOF: identifying density-based local outliers
TL;DR: This paper contends that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier, called the local outlier factor (LOF), and gives a detailed formal analysis showing that LOF enjoys many desirable properties.
Book ChapterDOI
Fast effective rule induction
TL;DR: This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples.
Book
Outliers in Statistical Data
Vic Barnett,Toby Lewis +1 more
TL;DR: In this article, the authors present an updated version of the reference work on outliers, including new areas of study such as outliers in direction data as well as developments in fields such as discordancy tests for univariate and multivariate samples.
Journal ArticleDOI
An Intrusion-Detection Model
TL;DR: A model of a real-time intrusion-detection expert system capable of detecting break-ins, penetrations, and other forms of computer abuse is described, based on the hypothesis that security violations can be detected by monitoring a system's audit records for abnormal patterns of system usage.