Intrusion detection system: A comprehensive review

By Hung-Jen Liao, C. Lin, Y. Lin, Kuang-Yuan Tung. 2013

1,589 citations recorded by the source corpus.

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

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. Cláudio Toshio Kawakani
2. Chau Yuen
3. Bruno Bogaz Zarpelão
4. Yuren Zhou
5. Meng Zhang
6. Billy Pik Lik Lau
7. Sean Carlisto de Alvarenga
8. Rodrigo Sanches Miani

In Computer Networks and Communications

1. Billy Pik Lik Lau
2. Cláudio Toshio Kawakani
3. Chau Yuen
4. Bruno Bogaz Zarpelão
5. Yuren Zhou
6. Meng Zhang
7. Sean Carlisto de Alvarenga
8. Rodrigo Sanches Miani

In Signal Processing

1. Chau Yuen
2. Yuren Zhou
3. Meng Zhang
4. Billy Pik Lik Lau
5. U-Xuan Tan
6. Marakkalage Sumudu Hasala
7. Naveed Ul L. Hassan
8. Mohammad Ali Jabraeil Jamali

Citations in the corpus

• A survey of data fusion in smart city applications (2019, methodology): “General techniques include Bayesian inference [66], Dempster-Shafer Inference [67], and semantic approaches [68]. anomaly detection [73] and others [38].”
• A survey of intrusion detection in Internet of Things (2017, background): “However, this approach is ineffective to detect new attacks and variants of known attacks, because a matching signature for these attacks is still unknown [12, 57].”
• A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems (2015, neutral): “These systems are used to protect computer systems from the risk of theft and intruders (Liao, Lin, Lin, & Tung, 2013).”