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- Title
Peer-to-peer botnets: exploring behavioural characteristics and machine/deep learning-based detection.
- Authors
Kabla, Arkan Hammoodi Hasan; Thamrin, Achmad Husni; Anbar, Mohammed; Manickam, Selvakumar; Karuppayah, Shankar
- Abstract
The orientation of emerging technologies on the Internet is moving toward decentralisation. Botnets have always been one of the biggest threats to Internet security, and botmasters have adopted the robust concept of decentralisation to develop and improve peer-to-peer botnet tactics. This makes the botnets cleverer and more artful, although bots under the same botnet have symmetrical behaviour, which is what makes them detectable. However, the literature indicates that the last decade has lacked research that explores new behavioural characteristics that could be used to identify peer-to-peer botnets. For the abovementioned reasons, in this study, we propose new two methods to detect peer-to-peer botnets: first, we explored a new set of behavioural characteristics based on network traffic flow analyses that allow network administrators to more easily recognise a botnet's presence, and second, we developed a new anomaly detection approach by adopting machine-learning and deep-learning techniques that have not yet been leveraged to detect peer-to-peer botnets using only the five-tuple static indicators as selected features. The experimental analyses revealed new and important behavioural characteristics that can be used to identify peer-to-peer botnets, whereas the experimental results for the detection approach showed a high detection accuracy of 99.99% with no false alarms.
- Subjects
BOTNETS; COMPUTER network traffic; TECHNOLOGICAL innovations; INTERNET security; ANOMALY detection (Computer security); TRAFFIC flow
- Publication
EURASIP Journal on Information Security, 2024, Vol 2024, Issue 1, p1
- ISSN
1687-4161
- Publication type
Article
- DOI
10.1186/s13635-024-00169-0