Feature deduction and ensemble design of intrusion detection systems

By Srilatha Chebrolu, Ajith Abraham, Johnson P. Thomas. 2005

704 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Consistent Individualized Feature Attribution for Tree Ensembles (2018, neutral): “Though its motivation is largely heuristic [11], gain is widely used as the basis for feature selection methods [5, 13, 25].”
Data preprocessing for anomaly based network intrusion detection: A review (2011, methodology): “(Chebrolu et al., 2005)”
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “[15] investigated the performance in the use of a Markov blanket model and decision tree analysis for feature selection, which showed its capability of reducing the number of features in KDD Cup 99 from 41 to 12 features.”
An efficient intrusion detection system based on support vector machines and gradually feature removal method (2012, background): “Chebrolu, Abraham, and Thomas (2005) investigated the performance of two feature selection algorithms involving Bayesian networks (BN) and Classification Regression Trees (CRC), and developed the ensemble of both methods.”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[90] Supervised BN and CART (offline) KDD Cup [257] Feature Selection using Markov Blanket and Gini rule -5,092 training data records -6,890 testing data records - AMD Athlon 1.”
An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things (2019, methodology): “Chebrolu et al. [14] developed an ensemble technique of Bayesian networks (BN) and clas-siﬁcation and regression trees to identify attack instances.”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, methodology): “Chebrolu et al. (2005) categorized primary features in constructing an IDS that is very crucial for real world detection. Markov model and decision tree has been used in the feature selection process. Bayesian network combined with regression trees were used to build the intrusion detection model. S”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “Chebrolu et al. examined the performance of two feature selection algorithms involving Bayesian networks (BN) and Classification Regression Trees (CRC) and combined these methods for higher accuracy (Chebrolu et al., 2005).”
The use of computational intelligence in intrusion detection systems: A review (2010, background): “There are two types of anomaly detection [48].”
Network Anomaly Detection: Methods, Systems and Tools (2014, methodology): “[178] 2005 Weighted voting O N P Numeric KDDcup99 C1 Class specific ensemble model Perdisci et al.”
Explainable AI for Trees: From Local Explanations to Global Understanding (2019, methodology): “However, it becomes biased for finite ensembles of greedily built trees, and so approaches have been designed to account for this bias when using Gain for feature selection [26, 9].”