Intrusion detection by machine learning: A review

By Chih-Fong Tsai, Yu-Feng Hsu, Chia-Ying Lin, Wei-Yang Lin. Expert systems with applications, 2009

1,037 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/727f14e94f33f7bf9d1fa9d3d4649cc828a30ba6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Manuel Günther
2. Andras Rozsa
3. Terrance E. Boult
4. Ethan M. Rudd
5. Naveen K. Chilamkurti
6. André Costa Drummond
7. Paulo Angelo Alves Resende
8. Wei Peng

In Computer Networks and Communications

1. Naveen K. Chilamkurti
2. André Costa Drummond
3. Paulo Angelo Alves Resende
4. Wei Peng
5. Rabei Alhadad
6. Nasrin Sultana
7. Ke He
8. Dan Dongseong Kim

In Signal Processing

1. Ala Al-Fuqaha
2. Monowar Hussain Bhuyan
3. Jawwad Ahmed Shamsi
4. Amir Hussain
5. Wei Peng
6. Weiqing Sun
7. Junaid Qadir
8. Arturo Geigel

Citations in the corpus

• Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “In early NIDS, feature reduction is performed manually by security experts using filter methods, where each feature is treated as an independent variable, and only the discriminative features are used in classification [72].”
• Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, methodology): “can also be used to effectively perform non-linear classification [33].”
• A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, result): “Tsai et al. (2009) compare 55 works published between 2000 and 2007.”
• An efficient intrusion detection system based on support vector machines and gradually feature removal method (2012, methodology): “Since SVM has manifested its robustness and efficiency in the network action classification, it therefore becomes a popular method widely used in IDS, as shown in Tsai’s review (Tsai et al., 2009).”
• A systems and control perspective of CPS security (2019, background): “For a review on applications of anomaly detection in computer networks, the reader is referred to [199], [200].”
• Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “[43], the majority of the IDS experiments were performed on the KDD Cup 99 datasets.”
• Machine Learning for Anomaly Detection: A Systematic Review (2021, background): “Also, Tsai et al. [22] reviewed intrusion detection, but the authors focused on machine learning techniques.”
• Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, background): “SVM maximizes the margin between support vectors because separating all classes is necessary [34].”
• Unsupervised Learning Trojan (2014, background): “, 2010; McNelis, 2005), medical applications (Kononenko, 2001), and intrusion detection (Tsai et al., 2009; Sabhnani & Serpen, 2003a), among many others.”