Anomaly-Based NIDS Using Artificial Neural Networks Optimised with Cuckoo Search Optimizer

Rithesh, K.

doi:10.1007/978-981-13-5802-9_3

Cited by 2 publications

(1 citation statement)

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“…A Cuckoo-based FS algorithm is proposed in [125] to preprocess network data aimed at improving the IDS detection accuracy in cloud environments. A Cuckoo search strategy has been also used in [126] to optimize Artificial Neural Networks when dealing with traffic anomaly detection issues. Again, coupled with SVM, Cuckoo search has been adopted in FS to deal with problem of phishing mail detection [127].…”

Section: Cuckoo Searchmentioning

confidence: 99%

Supervised feature selection techniques in network intrusion detection: A critical review

Mauro

Galatro²,

Fortino

et al. 2021

Engineering Applications of Artificial Intelligence

103

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Machine Learning (ML) techniques are becoming an invaluable support for network intrusion detection, especially in revealing anomalous flows, which often hide cyber-threats. Typically, ML algorithms are exploited to classify/recognize data traffic on the basis of statistical features such as inter-arrival times, packets length distribution, mean number of flows, etc. Dealing with the vast diversity and number of features that typically characterize data traffic is a hard problem. This results in the following issues: i) the presence of so many features leads to lengthy training processes (particularly when features are highly correlated), while prediction accuracy does not proportionally improve; ii) some of the features may introduce bias during the classification process, particularly those that have scarce relation with the data traffic to be classified. To this end, by reducing the feature space and retaining only the most significant features, Feature Selection (FS) becomes a crucial pre-processing step in network management and, specifically, for the purposes of network intrusion detection. In this review paper, we complement other surveys in multiple ways: i) evaluating more recent datasets (updated w.r.t. obsolete KDD 99) by means of a designed-from-scratch Python-based procedure; ii) providing a synopsis of most credited FS approaches in the field of intrusion detection, including Multi-Objective Evolutionary techniques; iii) assessing various experimental analyses such as feature correlation, time complexity, and performance. Our comparisons offer useful guidelines to network/security managers who are considering the incorporation of ML concepts into network intrusion detection, where trade-offs between performance and resource consumption are crucial.

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Section: Cuckoo Searchmentioning

confidence: 99%