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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Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks.
TL;DR: In this article, the authors compared the performance of three feature extraction algorithms, Principal Component Analysis (PCA), Auto-encoder (AE), and Linear Discriminant Analysis (LDA), on three benchmark datasets; UNSW-NB15, ToN-IoT and CSE-CIC-IDS2018.
BookDOI
Computation, Cryptography, and Network Security
TL;DR: This book will appeal to operations research analysts, engineers, community decision makers, academics, the military community, practitioners sharing the current state-of-the-art, and analysts from coalition partners.
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Network Intrusion Detection based on LSTM and Feature Embedding
TL;DR: In this paper, the authors proposed a network intrusion detection model based on LSTM network and categorical information using the embedding technique, which achieved a binary classification accuracy of 99.72%.
Proceedings ArticleDOI
Faster and More Accurate Measurement through Additive-Error Counters
TL;DR: In this article, the authors proposed additive error estimators, which are simpler, faster, and more accurate when used for network measurement, and rigorously analyzed and empirically evaluated against several other measurement algorithms on real Internet traces.
Journal ArticleDOI
New biostatistics features for detecting web bot activity on web applications
TL;DR: New Biostatistics features are proposed which are used to identify the human user presence on web applications and reveal that the proposed model is efficient in discriminating human users from web bots.
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.