TY - GEN
T1 - Sliding Time Analysis in Traffic Segmentation for Botnet Activity Detection
AU - Hostiadi, Dandy Pramana
AU - Ahmad, Tohari
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Botnets are a threat in a dangerous cyber era. Botnets involve malicious software to attack the system based on instructions from the botmaster. Previous research had introduced a botnet activity detection model, such as using activity time analysis through a sliding time-based traffic segmentation process. However, the introduced model has not analyzed the ideal time in the sliding process in the segmentation process. The sliding process is needed to detect the botnet attack activity chain correctly. This paper analyzed the ideal time in the sliding process in traffic data segmentation to detect botnet activity and obtain information about botnet attacks. It aimed to get the optimal time in the sliding process and see its effect on detection accuracy. The test was carried out using a public dataset, namely the CTU-13 dataset, based on the two detection models in previous research. The result showed that the optimal time in the sliding process was 30 minutes in both detection models, with the best scenario detection results of 231 and the best detection accuracy of 97.93%.
AB - Botnets are a threat in a dangerous cyber era. Botnets involve malicious software to attack the system based on instructions from the botmaster. Previous research had introduced a botnet activity detection model, such as using activity time analysis through a sliding time-based traffic segmentation process. However, the introduced model has not analyzed the ideal time in the sliding process in the segmentation process. The sliding process is needed to detect the botnet attack activity chain correctly. This paper analyzed the ideal time in the sliding process in traffic data segmentation to detect botnet activity and obtain information about botnet attacks. It aimed to get the optimal time in the sliding process and see its effect on detection accuracy. The test was carried out using a public dataset, namely the CTU-13 dataset, based on the two detection models in previous research. The result showed that the optimal time in the sliding process was 30 minutes in both detection models, with the best scenario detection results of 231 and the best detection accuracy of 97.93%.
KW - Botnet detection
KW - Intrusion detection systems
KW - Network security
KW - Slide windowing
KW - Traffic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85129408089&partnerID=8YFLogxK
U2 - 10.1109/ICCI54321.2022.9756077
DO - 10.1109/ICCI54321.2022.9756077
M3 - Conference contribution
AN - SCOPUS:85129408089
T3 - 5th International Conference on Computing and Informatics, ICCI 2022
SP - 286
EP - 291
BT - 5th International Conference on Computing and Informatics, ICCI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Computing and Informatics, ICCI 2022
Y2 - 9 March 2022 through 10 March 2022
ER -