TY - GEN
T1 - Intrusion Detection Using Attention-Based CNN-LSTM Model
AU - Al-Omar, Ban
AU - Trabelsi, Zouheir
N1 - Publisher Copyright:
© 2023, IFIP International Federation for Information Processing.
PY - 2023
Y1 - 2023
N2 - With the rise of sophisticated cyberattacks and the advent of complex and diverse technological systems, traditional methods of intrusion detection have become insufficient. The inability to prevent intrusions poses a severe threat to the credibility of security services, which may result in the compromise of data confidentiality, integrity, and availability. To address this challenge, research has proposed the use of Artificial Intelligence (AI) and deep learning (DL) models to enhance the effectiveness of intrusion detection. In this study, we present an Intrusion Detection System (IDS) that utilizes attention-based Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. The attention mechanism of the model allows for the identification of significant features in network traffic data for more precise predictions. Using the benchmark dataset UNSW-NB15, we validate the robustness and effectiveness of our model, achieving a detection rate of over 95%. Our results emphasize the robustness and effectiveness of the proposed system, demonstrating the immense potential of AI and DL models in bolstering intrusion detection.
AB - With the rise of sophisticated cyberattacks and the advent of complex and diverse technological systems, traditional methods of intrusion detection have become insufficient. The inability to prevent intrusions poses a severe threat to the credibility of security services, which may result in the compromise of data confidentiality, integrity, and availability. To address this challenge, research has proposed the use of Artificial Intelligence (AI) and deep learning (DL) models to enhance the effectiveness of intrusion detection. In this study, we present an Intrusion Detection System (IDS) that utilizes attention-based Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models. The attention mechanism of the model allows for the identification of significant features in network traffic data for more precise predictions. Using the benchmark dataset UNSW-NB15, we validate the robustness and effectiveness of our model, achieving a detection rate of over 95%. Our results emphasize the robustness and effectiveness of the proposed system, demonstrating the immense potential of AI and DL models in bolstering intrusion detection.
KW - Attention Based
KW - CNN-LSTM
KW - Intrusion Detection
UR - http://www.scopus.com/inward/record.url?scp=85163312237&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85163312237&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-34111-3_43
DO - 10.1007/978-3-031-34111-3_43
M3 - Conference contribution
AN - SCOPUS:85163312237
SN - 9783031341106
T3 - IFIP Advances in Information and Communication Technology
SP - 515
EP - 526
BT - Artificial Intelligence Applications and Innovations - 19th IFIP WG 12.5 International Conference, AIAI 2023, Proceedings
A2 - Maglogiannis, Ilias
A2 - Iliadis, Lazaros
A2 - MacIntyre, John
A2 - Dominguez, Manuel
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2023
Y2 - 14 June 2023 through 17 June 2023
ER -