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
Citations
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Journal ArticleDOI
Towards a standard feature set for network intrusion detection system datasets
TL;DR: In this paper, the authors proposed and evaluated standard NIDS feature sets based on the NetFlow network meta-data collection protocol and system, and compared two NetFlow-based feature set variants, a version with 12 features, and another one with 43 features.
Posted Content
GAN Ensemble for Anomaly Detection
Xu Han,Xiaohui Chen,Li-Ping Liu +2 more
TL;DR: The theoretical analysis of GANs and GAN ensembles explains the role of a GAN discriminator in anomaly detection, and evaluation of ensembled constructed from four types of base models show that these ensemble clearly outperform single models in a series of tasks of anomaly detection.
Posted Content
Anomaly detection in online social networks
TL;DR: The detection of anomalies in online social networks is composed of two sub-processes; the selection and calculation of network features, and the classification of observations from this feature space.
Journal ArticleDOI
A Review of Research Work on Network-Based SCADA Intrusion Detection Systems
TL;DR: A structured evaluation methodology is proposed that encompasses detection techniques, protected protocols, implementation tools, test environments and IDS performance and special attention is focused on assessing implementation maturity as well as the applicability of each surveyed solution in the Future Internet environment.
Proceedings Article
Classifying attacks in a network intrusion detection system based on artificial neural networks
TL;DR: A new approach of intrusion detection system based on neural network is presented, which detects the attacks and classify them in 6 groups with the approximately 90.78% accuracy with the two hidden layers of neurons in the neural network.
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.