A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering

By G. Wang, Jin-Xing Hao, Jian Ma, Lihua Huang. Expert systems with applications, 2010

581 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/d9ba2d24cee7054469e6eeedaf9945bb8a18aaea

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Citations in the corpus

A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016, background): “Neural networks, which have been successfully applied to intrusion recognition tasks [80], [96], [97], [98], [99], [100], [101], are appealing because they provide the ability to adapt a fixed set of basis functions to input data, thus optimizing the feature space in which the readout layer operates”
A novel hybrid KPCA and SVM with GA model for intrusion detection (2014, methodology): “Thereore, various machine learning methods are developed for intrusion etection, such as decision tree [1], genetic algorithm (GA) [2], neual network [3], principal component analysis (PCA) [4], fuzzy logic”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “FC-ANN [132] provides the highest detection rate 93.”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, background): “In addition the less common attacks are often outliers (Wang et al., 2010).”
A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems (2015, background): “IDSs can be cate-gorised as anomaly detection and misuse detection or signature detection systems (Depren, Topallar, Anarim, & Ciliz, 2005; Wang, Hao, Ma, & Huang, 2010).”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, methodology): “…several machine learning techniques like decision tree (Lee et al., 2008), genetic algorithm (Shafi and Abbass, 2009), Support vector machines (Khan et al., 2007), Artificial Neural Network (Wang et al., 2010) and hybrid intelligent system (Peddabachigari et al., 2007) are explored to build an IDS.”
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “[36] Fuzzy clustering + ANN KDD-Cup 99 Anomaly detection Precision, Recall, F-measure DT/NB, ANN No Tajbakhsh et al.”
An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks (2019, methodology): “Finally, we compare the detection performance of the CNN–IDS model with those of the DNN and RNN models [34]–[36], all of which use the same dataset AE (100).”
WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks (2016, methodology): “Moreover, they offer partial solutions and they cannot be applied concurrently because they will consume high energy, which is not practical in WSNs [2, 25].”