TY - GEN
T1 - Fast Binary Network Intrusion Detection based on Matched Filter Optimization
AU - Alsaadi, Hajar Saif
AU - Hedjam, Rachid
AU - Touzene, Abderezak
AU - Abdessalem, Abdelhamid
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Securing networks has become very critical task because of the continued appearance of attacks and the growing number of Internet users. The detection, classification and prevention of attacks are provided by the so-called Intrusion Detection System (IDS). In this article, we have proposed and evaluated a new model of network intrusion detection based on matched filter optimization called NIDeMFO for Network Intrusion Detection based on Matched Filter Optimization. Similar to Linear Discriminant Analysis (LDA), the goal is to design a linear filter that projects data into a space where both classes, normal and attack, are well separated. The difference with LDA is that the margin between the averages of the two classes in the projected space is controlled by a parameter. The proposed detection model is evaluated on the NSL-KDD benchmark. The results show its competitiveness and effectiveness compared to many existing detection models.
AB - Securing networks has become very critical task because of the continued appearance of attacks and the growing number of Internet users. The detection, classification and prevention of attacks are provided by the so-called Intrusion Detection System (IDS). In this article, we have proposed and evaluated a new model of network intrusion detection based on matched filter optimization called NIDeMFO for Network Intrusion Detection based on Matched Filter Optimization. Similar to Linear Discriminant Analysis (LDA), the goal is to design a linear filter that projects data into a space where both classes, normal and attack, are well separated. The difference with LDA is that the margin between the averages of the two classes in the projected space is controlled by a parameter. The proposed detection model is evaluated on the NSL-KDD benchmark. The results show its competitiveness and effectiveness compared to many existing detection models.
KW - Anomaly Detection
KW - Machine learning
KW - Matched Filter
KW - Network Intrusion Detection Systems
KW - Network security
UR - http://www.scopus.com/inward/record.url?scp=85085475688&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085475688&partnerID=8YFLogxK
U2 - 10.1109/ICIoT48696.2020.9089659
DO - 10.1109/ICIoT48696.2020.9089659
M3 - Conference contribution
AN - SCOPUS:85085475688
T3 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
SP - 195
EP - 199
BT - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Y2 - 2 February 2020 through 5 February 2020
ER -