An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks

By Ozgur Depren, M. Topallar, Emin Anarim, M. Kemal Ciliz. Expert systems with applications, 2005

575 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/034ea5696ec14a9687d708ad386da18c8f8cdcdb

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. Saoussen Krichen
2. Chaouki Khammassi
3. Chih-Fong Tsai
4. Aunn Raza
5. Yu-Feng Hsu
6. Ala Al-Fuqaha
7. Hunain Arif
8. Amir Hussain

In Computer Networks and Communications

1. Saoussen Krichen
2. Chaouki Khammassi
3. Chih-Fong Tsai
4. Yu-Feng Hsu
5. Ala Al-Fuqaha
6. Wei-Yang Lin
7. V. Varadharajan
8. U. Tupakula

In Signal Processing

1. Saoussen Krichen
2. Chaouki Khammassi
3. Chih-Fong Tsai
4. Ala Al-Fuqaha
5. Amir Hussain
6. V. Varadharajan
7. Rung Ching Chen
8. U. Tupakula

Citations in the corpus

• Using Rough Set and Support Vector Machine for Network Intrusion Detection System (2009, methodology): “There are two main methods of IDS: misuse and anomaly [11].”
• A novel hybrid intrusion detection method integrating anomaly detection with misuse detection (2014, unknown): “Generally, intrusion detection algorithms are categorized into two methods: misuse detection and anomaly detection (Depren, Topallar, Anarim, & Ciliz, 2005).”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “Depren et al. [194] SOM & J.48 Decision Tree Applied SOM combined with J.48 decision tree algorithm in IDS to detect anomaly and misuses intelligently.”
• 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).”
• A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “Hybrid models utilize both misuse detection and anomaly detection approaches to improve the prediction performance (Depren, Topallar, Anarim, & Ciliz, 2005; Zhang, Zulkernine, Haque, 2008).”
• A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “assumption that normal data instances lie close to their closest cluster centroid while anomalies lie far away from their closest cluster centroid [101].”
• A GA-LR wrapper approach for feature selection in network intrusion detection (2017, background): “In the last recent years, the hybrid detection approach gain few steps (Depren et al., 2005; Kim et al., 2014) but it is still far from the anomaly approach and the misuse approach.”
• Intrusion detection by machine learning: A review (2009, background): “…(2006), Zhang and Shen (2005) Schultz et al. (2001), Scott (2004), Wang et al. (2006) Chen et al. (2005), Shyu et al. (2003) Bouzida et al. (2004), Depren et al. (2005), Fan et al. (2004), Peddabachigari et al. (2004) Ramos and Abraham (2005) Balajinath and Raghavan (2000), Mukkamala et al.…”