TY - GEN
T1 - A Deep Intrusion Detection Model for Network Traffic Payload Analysis
AU - Hojjatinia, Sina
AU - Monshizadeh, Mehrnoosh
AU - Khatri, Vikramajeet
N1 - Publisher Copyright:
© 2023 University of Split, FESB.
PY - 2023
Y1 - 2023
N2 - Recently, many studies have focused on payload analysis. However, these studies mostly apply image-based deep classifiers for layer 7 traffic analysis and not specifically for intrusion detection. Furthermore, the proposed methods mostly focus on specific types of attacks. This paper introduces a Multi-deep classifier for Payload Intrusion Detection (McPID). The proposed architecture benefits from the generalization capability of deep algorithms in order to efficiently detect a wider range of payload-based attacks such as botnet communication, brute-force (SSH, FTPS, web-attack), and DoS. In order to evaluate the performance of the introduced architecture, three publicly available datasets such as CIC-IDS-2017, UNSW 2015, and CTU-2013 are applied in experimental results.
AB - Recently, many studies have focused on payload analysis. However, these studies mostly apply image-based deep classifiers for layer 7 traffic analysis and not specifically for intrusion detection. Furthermore, the proposed methods mostly focus on specific types of attacks. This paper introduces a Multi-deep classifier for Payload Intrusion Detection (McPID). The proposed architecture benefits from the generalization capability of deep algorithms in order to efficiently detect a wider range of payload-based attacks such as botnet communication, brute-force (SSH, FTPS, web-attack), and DoS. In order to evaluate the performance of the introduced architecture, three publicly available datasets such as CIC-IDS-2017, UNSW 2015, and CTU-2013 are applied in experimental results.
KW - anomaly detection
KW - convolutional neural network.
KW - data mining
KW - deep learning
KW - payload analysis
KW - security
UR - http://www.scopus.com/inward/record.url?scp=85174520889&partnerID=8YFLogxK
U2 - 10.23919/SoftCOM58365.2023.10271641
DO - 10.23919/SoftCOM58365.2023.10271641
M3 - Conference article in proceedings
AN - SCOPUS:85174520889
T3 - SoftCOM
BT - 2023 31st International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2023
A2 - Begusic, Dinko
A2 - Rozic, Nikola
A2 - Radic, Josko
A2 - Saric, Matko
PB - IEEE
T2 - International Conference on Software, Telecommunications and Computer Networks
Y2 - 21 September 2023 through 23 September 2023
ER -