Journal ArticleDOI
GMDH-based networks for intelligent intrusion detection
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TLDR
The results obtained proved that the proposed intrusion detection scheme yields high attack detection rates, nearly 98%, when compared with other intelligent classification techniques for network intrusion detection.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2013-08-01. It has received 64 citations till now. The article focuses on the topics: Anomaly-based intrusion detection system & Intrusion detection system.read more
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Journal ArticleDOI
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors
TL;DR: A novel feature representation approach, namely the cluster center and nearest neighbor (CANN) approach, which shows that the CANN classifier not only performs better than or similar to k-NN and support vector machines trained and tested by the original feature representation in terms of classification accuracy, detection rates, and false alarms.
Journal ArticleDOI
Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing
Opeyemi Osanaiye,Opeyemi Osanaiye,Haibin Cai,Kim-Kwang Raymond Choo,Ali Dehghantanha,Zheng Xu,Zheng Xu,Mqhele E. Dlodlo +7 more
TL;DR: An ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection that can effectively reduce the number of features and has a high detection rate and classification accuracy when compared to other classification techniques.
Journal ArticleDOI
Ensemble-based Multi-Filter Feature Selection Method for DDoS Detection in Cloud Computing
TL;DR: In this article, an ensemble-based multi-filter feature selection method was proposed to reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.
References
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Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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Foundations of Statistical Natural Language Processing
TL;DR: This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear and provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations.
Proceedings ArticleDOI
A detailed analysis of the KDD CUP 99 data set
TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.
Intrusion detection with unlabeled data using clustering
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