TY - JOUR
T1 - Multi-class intrusion detection using two-channel color mapping in IEEE 802.11 wireless network
AU - Aminanto, Muhamad Erza
AU - Wicaksono, R. Satrio Hariomurti
AU - Aminanto, Achmad Eriza
AU - Tanuwidjaja, Harry Chandra
AU - Yola, Lin
AU - Kim, Kwangjo
N1 - Funding Information:
This work was supported by the Innovations, Technology and Social Changes Group of School of Strategic and Global Studies, Universitas Indonesia, under a contract of 2021 Research Cluster Grant.
Publisher Copyright:
© 2013 IEEE.
PY - 2022/4/1
Y1 - 2022/4/1
N2 - The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusion Detection System (IDS) is one method to inspect the network traffic by leveraging state-of-the-art anomaly detection techniques. Deep learning models have been utilized to distinguish the benign and malicious traffic. However, projecting the tabular data into images before the image classification has been the main challenge of leveraging deep learning for IDS purposes. We propose the novel projection of tabular data into 2-coded color mapping for IDS purposes. The proposed method employs a feature selection method to ensure optimal dimensionality. We examined the different number of attribute subsets to obtain the relationship between the attributes. Furthermore, it takes advantage of the Convolutional Neural Network (CNN) model to classify the Wi-Fi attacks. We evaluate the proposed model using the most common Wi-Fi attacks dataset, Aegean Wi-Fi Intrusion Dataset (AWID2). The proposed method achieved an F1 score of 99.73% and a false positive rate of 0.24%. This study highlights the importance of addressing the mapping procedures from tabular data into grid-based data before deep learning training and validates the effectiveness of CNN to detect multiple types of wireless network attacks.
AB - The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusion Detection System (IDS) is one method to inspect the network traffic by leveraging state-of-the-art anomaly detection techniques. Deep learning models have been utilized to distinguish the benign and malicious traffic. However, projecting the tabular data into images before the image classification has been the main challenge of leveraging deep learning for IDS purposes. We propose the novel projection of tabular data into 2-coded color mapping for IDS purposes. The proposed method employs a feature selection method to ensure optimal dimensionality. We examined the different number of attribute subsets to obtain the relationship between the attributes. Furthermore, it takes advantage of the Convolutional Neural Network (CNN) model to classify the Wi-Fi attacks. We evaluate the proposed model using the most common Wi-Fi attacks dataset, Aegean Wi-Fi Intrusion Dataset (AWID2). The proposed method achieved an F1 score of 99.73% and a false positive rate of 0.24%. This study highlights the importance of addressing the mapping procedures from tabular data into grid-based data before deep learning training and validates the effectiveness of CNN to detect multiple types of wireless network attacks.
KW - anomaly detection
KW - convolutional neural network
KW - intrusion detection system
KW - Wireless attacks
UR - http://www.scopus.com/inward/record.url?scp=85127519709&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2022.3164104
DO - 10.1109/ACCESS.2022.3164104
M3 - Article
AN - SCOPUS:85127519709
SN - 2169-3536
VL - 10
SP - 36791
EP - 36801
JO - IEEE Access
JF - IEEE Access
ER -