TY - GEN
T1 - Network intrusion detection using multi-criteria PROAFTN classification
AU - Al-Obeidat, Feras N.
AU - El-Alfy, El Sayed M.
PY - 2014
Y1 - 2014
N2 - Network intrusion is recognized as a chronic and recurring problem. Hacking techniques continually change and several countermeasure methods have been suggested in the literature including statistical and machine learning approaches. However, no single solution can be claimed as a rule of thumb for the wide spectrum of attacks. In this paper, a novel methodology is proposed for network intrusion detection based on the multicriteria PROAFTN classification. The algorithm is evaluated and compared on a publicly available and widely used dataset. The results in this paper show that the proposed algorithm is promising in detecting various types of intrusions with high classification accuracy.
AB - Network intrusion is recognized as a chronic and recurring problem. Hacking techniques continually change and several countermeasure methods have been suggested in the literature including statistical and machine learning approaches. However, no single solution can be claimed as a rule of thumb for the wide spectrum of attacks. In this paper, a novel methodology is proposed for network intrusion detection based on the multicriteria PROAFTN classification. The algorithm is evaluated and compared on a publicly available and widely used dataset. The results in this paper show that the proposed algorithm is promising in detecting various types of intrusions with high classification accuracy.
UR - https://www.scopus.com/pages/publications/84904490436
U2 - 10.1109/ICISA.2014.6847436
DO - 10.1109/ICISA.2014.6847436
M3 - Conference contribution
AN - SCOPUS:84904490436
SN - 9781479944439
T3 - ICISA 2014 - 2014 5th International Conference on Information Science and Applications
BT - ICISA 2014 - 2014 5th International Conference on Information Science and Applications
PB - IEEE Computer Society
ER -