SKT-IDS: Unknown attack detection method based on Sigmoid Kernel Transformation and encoder–decoder architecture - Zha, Chao; Wang, Zhiyu; Fan, Yifei; Zhang, Xingming; Bai, Bing; Zhang, Yinjie; Shi, Sainan; Zhang, Ruyun (Computers & security, 2024)
Intrusion Detection Systems (IDS) are crucial in cybersecurity for monitoring network traffic and identifying potential attacks. Existing IDS research largely focuses on known attack detection, leaving a significant gap in research regarding unknown attack detection, where achieving a balance between false alarm rate (identifying normal traffic as attack traffic) and recall rate of unknown attack detection remains challenging. To address these gaps, we propose a novel IDS based on Sigmoid Kernel Transformation and Encoder-Decoder architecture, namely SKT-IDS, where SKT stands for Sigmoid Kernel Transformation. We start with pre-training an attention-based encoder for coarse-grained intrusion detection. Then, we use this encoder to build an encoder–decoder model specifically for 0-day attack detection, training it solely on known traffic using the cosine similarity loss function. To enhance detection, we introduce a Sigmoid Kernel Transformation for feature engineering, improving the discriminative ability between normal traffic and 0-day attacks. Finally, we conducted a series of ablation and comparative experiments on the NSL-KDD and CSE-CIC-IDS2018 datasets, confirming the effectiveness of our proposed method. With a false alarm rate of 1%, we achieved recall rates for unknown attack detection of 65% and 69% on the two datasets, respectively, demonstrating significant performance improvements compared to existing state-of-the-art models.