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IV-IDM: Reliable Intrusion Detection Method based on Involution and Voting | IEEE Conference Publication | IEEE Xplore

IV-IDM: Reliable Intrusion Detection Method based on Involution and Voting


Abstract:

Intrusion detection is critical in the area of cyberspace security. Deep learning methods, especially CNN, have been widely used in intrusion detection in recent years. N...Show More

Abstract:

Intrusion detection is critical in the area of cyberspace security. Deep learning methods, especially CNN, have been widely used in intrusion detection in recent years. Network traffic is usually converted into images for processing. However, images converted from network traffic do not have multi-channel features like real-world pictures and have explicit long-distance dependencies between pixels. These characteristics will cause weak performance and poor explanation, making images converted from network traffic unsuitable to be processed by CNN. Besides, most works only consider the first few packets (named head packets) in a flow, which contains the information about connection establishment and interaction between two parts. However, the last few packets (named tail packets) are omitted, resulting in the loss of information about disconnection. To handle the above problems, we propose a reliable intrusion detection model called IV-IDM. Instead of convolution, IV-IDM uses a new structure, involution. Involution has the properties of spatial-specific and channel-agnostic and is more suitable for intrusion detection tasks than convolution. We also propose I-Res, which is constructed based on involution and is used as the base classifier of IV-IDM. We use head and tail packets of a flow as the inputs to two I-Res respectively to learn richer information and employ a voting algorithm to integrate the results of these two parts to promote the robustness of the model. Finally, IV-IDM is evaluated by the ISCX-IDS-2012 and the CIC-IDS-2017 datasets. The experimental results demonstrate that IV-IDM outperforms the state-of-the-art models and is qualified for intrusion detection.
Date of Conference: 16-20 May 2022
Date Added to IEEE Xplore: 11 August 2022
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Conference Location: Seoul, Korea, Republic of

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