Application of SVM and ANN for intrusion detection

By Wun-Hwa Chen, Sheng-Hsun Hsu, Hwang-Pin Shen. Computers & Operations Research, 2005

422 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b06092bae394b8877e0f0dfdc1c12a451e5c1377

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Citations in the corpus

Network intrusion detection system: A systematic study of machine learning and deep learning approaches (2020, methodology): “65,66 For NIDS, the SVM algorithm can be used to enhance its efficiency and accuracy by correctly predicting the normal and malicious classes.”
A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns (2014, background): “4 mean MLP ANN on subset of KDD98 [62] 99.”
Human emotion recognition from EEG-based brain–computer interface using machine learning: a comprehensive review (2022, background): “Some units serve as input nodes, some serve as hidden nodes, and the rest serve as output nodes, doing summation and thresholding [150].”
Intrusion detection by machine learning: A review (2009, neutral): “…et al. (2006) Bouzida et al. (2004), Eskin et al. (2002), Li and Guo (2007), Liao and Vemuri (2002), Wang and Stolfo (2004), Wang et al. (2004) Chen et al. (2005), Eskin et al. (2002), Heller et al. (2003), Peddabachigari et al. (2004), Tian et al. (2004), Wang and Battiti (2006), Zhang and…”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “SVM [93] is based on the notion of the margin-either side of the hyperplane”
Predicting defect-prone software modules using support vector machines (2008, background): “…Cristianini and Shawe‐Taylor, 2000), speaker identification (Schmidt and Gish, 1996), spam categorization (Drucker et al., 1999), intrusion detection (Chen et al., 2005), cheminformatics and bioinformatics (Cai et al., 2002; Burbidge et al., 2001; Morris et al., 2001; Lin et al., 2003; Bao and…”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “However, the accuracy reported is less than that in (Chen et al., 2005), because the KDDCUP 99 dataset is more complex and comprehensive than DARPA 98 dataset.”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “It can also be one-class and multi-class based on detection type [209], [210].”
Generation of a new IDS test dataset: Time to retire the KDD collection (2013, neutral): “MLP on subset of KDD98 [27]3 99.”