BotChase: Graph-Based Bot Detection Using Machine Learning

Article Properties
Cite
Daya, Abbas Abou, et al. “BotChase: Graph-Based Bot Detection Using Machine Learning”. IEEE Transactions on Network and Service Management, vol. 17, no. 1, 2020, pp. 15-29, https://doi.org/10.1109/tnsm.2020.2972405.
Daya, A. A., Salahuddin, M. A., Limam, N., & Boutaba, R. (2020). BotChase: Graph-Based Bot Detection Using Machine Learning. IEEE Transactions on Network and Service Management, 17(1), 15-29. https://doi.org/10.1109/tnsm.2020.2972405
Daya, Abbas Abou, Mohammad A. Salahuddin, Noura Limam, and Raouf Boutaba. “BotChase: Graph-Based Bot Detection Using Machine Learning”. IEEE Transactions on Network and Service Management 17, no. 1 (2020): 15-29. https://doi.org/10.1109/tnsm.2020.2972405.
Daya AA, Salahuddin MA, Limam N, Boutaba R. BotChase: Graph-Based Bot Detection Using Machine Learning. IEEE Transactions on Network and Service Management. 2020;17(1):15-29.
Refrences
Title Journal Journal Categories Citations Publication Date
Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains 2011
MOA: Massive online analysis 2010
BotGrep: Finding P2P bots with structured graph analysis 2010
Wide-scale botnet detection and characterization 2007
Rishi: Identify bot contaminated hosts by IRC nickname evaluation 2007
Citations
Title Journal Journal Categories Citations Publication Date
Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning

 Security and Communication Networks
  • Science: Science (General): Cybernetics: Information theory
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Technology: Technology (General): Industrial engineering. Management engineering: Information technology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
 4 2021
A Lightweight Hybrid Detection Method for Botnet

 International Journal of Circuits, Systems and Signal Processing 2021
Citations Analysis
Category Category Repetition
Science: Science (General): Cybernetics: Information theory1
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication1
Technology: Technology (General): Industrial engineering. Management engineering: Information technology1
Science: Mathematics: Instruments and machines: Electronic computers. Computer science1
The category Science: Science (General): Cybernetics: Information theory 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning and was published in 2021. The most recent citation comes from a 2021 study titled Improving the Accuracy of Network Intrusion Detection with Causal Machine Learning. This article reached its peak citation in 2021, with 2 citations. It has been cited in 2 different journals. Among related journals, the Security and Communication Networks cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year