Intrusion detection using neural networks and support vector machines

By Srinivas Mukkamala, G. Janoski, Andrew H. Sung. 2002

901 citations recorded by the source corpus.

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

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Citations in the corpus

RETRACTED ARTICLE: Application of extreme learning machine for estimation of wind speed distribution (2016, methodology): “SVMs have better performance compared to other traditional techniques such as NN and other conventional statistical models (Collobert and Bengio 2000; Huang et al. 2002; Mukkamala et al. 2002; Sung and Mukkamala 2003; Vapnik et al. 1997; Vapnik 2000).”
Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, background): “analysis [38], time series forecasting [39], information and image retrieval [40], information security [41], biological i.”
Application of SVM and ANN for intrusion detection (2005, methodology): “[4] Sequence of system calls RIPPER Hosts, program Hidden Markov models [5,6] Sequence of system calls RIPPER Hosts, program (sendmail) [7] Sequence of system calls Hosts, program (sendmail and lpr) [8] Sequence of system calls RIPPER Hosts, program (sendmail) [9] Sequence of system calls ANN Hosts,”
DDoS Attack Detection and Mitigation Using SDN: Methods, Practices, and Solutions (2017, methodology): “Arti-ﬁcial neural networks [49], Bayesian networks [50], self-organizing map (SOM) [51], and fuzzy logic [52] principles and concepts are extensively used for detecting anomalies in IDS.”
Enhancing one-class support vector machines for unsupervised anomaly detection (2013, methodology): “[20] showed that SVM based algorithms are superior compared to ANN based algorithms for the intrusion detection problem.”
A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering (2010, methodology): “Some researchers have compared the effec- tiveness of supervised ANN with other methods such as support vector machine (SVM) and multivariate adaptive regression splines (MARS) (Mukkamala, Sung, Abraham, & Ramos, 2004; Mukkamala et al., 2002).”
A comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[325] compare a SVM-based with an NN-based hybrid misuse and anomaly detection models.”
A hybrid SVM-FFA method for prediction of monthly mean global solar radiation (2015, methodology): “…been proved that its applications show superior performance compared to prior developed methodologies such as neural network and other conventional statistical models (Vapnik et al. 1996; Joachims 1998; Collobert and Bengio 2000; Mukkamala et al. 2002; Huang et al. 2002; Sung andMukkamala 2003).”
Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, background): “19,20 While detection system is a security mechanism for the detection of such illegal activity.”
Behavior of Machine Learning Algorithms in Adversarial Environments (2010, background): “Because this approach often fails to detect novel intrusions, a variety of researchers have proposed incorporating machine learning techniques into intrusion detection systems [e.g., Mahoney and Chan, 2002, Lazarevic et al., 2003, Mukkamala et al., 2002, Eskin et al., 2002].”
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 [115], [131]–[136], are appealing because they provide”
Enhanced Network Anomaly Detection Based on Deep Neural Networks (2018, methodology): “As ConvAEs make use of GPUs for training, the training time of network with 2D input is not much different than a conventional SVM or KNN classifier.”