@article{ostap2022bottrop,
  title = {BotTROP: Detection of a Botnet-based Threat using Novel Data Mining Algorithm},
  author = {Hubert OSTAP and Ryszard ANTKIEWICZ},
  year = 2022,
  url = {https://ibimapublishing.com/articles/CIBIMA/2022/156851/},
  journal = {Communications of the IBIMA},
  volume = 2022,
  pages = 21,
  doi = 10.5171/2022.156851,
  abstract = {Nowadays botnet-based threat, such as ransomwares, trojans and botnets per se, is still very dangerous for our privacy and data. Depending on their management architecture (centralized, decentralized, hybrid), they could be controlled from single or multi point servers called Command&Control (C2), what makes them very difficult to detect and mitigate before malicious action takes place. The aim of this paper is to present a method of detecting botnets based on the identification of their synchronous actions. Presented method, called BotTROP, utilizes clustering and classification methods to detect synchronous action among corporate network traffic to detect malicious activity such as a botnet of any type. Furthermore, the effectiveness of the presented method was verified in numerous experiments where simulated and real-life network traffic was used.},
  keywords = {botnet, security, detection, botnet detection, botnet mitigation},
  note = Article ID: 156851
}