TY - GEN
T1 - Classification of attack types for intrusion detection systems using a machine learning algorithm
AU - Park, Kinam
AU - Song, Youngrok
AU - Cheong, Yun Gyung
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/5
Y1 - 2018/7/5
N2 - In this paper, we present the results of our experiments to evaluate the performance of detecting different types of attacks (e.g., IDS, Malware, and Shellcode). We analyze the recognition performance by applying the Random Forest algorithm to the various datasets that are constructed from the Kyoto 2006+ dataset, which is the latest network packet data collected for developing Intrusion Detection Systems. We conclude with discussions and future research projects.
AB - In this paper, we present the results of our experiments to evaluate the performance of detecting different types of attacks (e.g., IDS, Malware, and Shellcode). We analyze the recognition performance by applying the Random Forest algorithm to the various datasets that are constructed from the Kyoto 2006+ dataset, which is the latest network packet data collected for developing Intrusion Detection Systems. We conclude with discussions and future research projects.
KW - Classification
KW - Intrusion-Detection-System
KW - Kyoto2006+
KW - Labeling
KW - Supervised-Machine-Learning
UR - https://www.scopus.com/pages/publications/85050651979
U2 - 10.1109/BigDataService.2018.00050
DO - 10.1109/BigDataService.2018.00050
M3 - Conference contribution
AN - SCOPUS:85050651979
T3 - Proceedings - IEEE 4th International Conference on Big Data Computing Service and Applications, BigDataService 2018
SP - 282
EP - 286
BT - Proceedings - IEEE 4th International Conference on Big Data Computing Service and Applications, BigDataService 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2018
Y2 - 26 March 2018 through 29 March 2018
ER -