Enhancing Network Intrusion Detection Through the Application of the Dung Beetle Optimized Fusion Model

作者：Yue Li, Jiale Zhang, Yiting Yan, Yutian Lei, Chang Yin · 发表于：IEEE Access · 年份：2024 · DOI：10.1109/access.2024.3353488 · 被引用次数：15 · 研究领域：Network Security and Intrusion Detection、Internet Traffic Analysis and Secure E-voting、Network Packet Processing and Optimization

With the rapid development of information communication and mobile device technologies, smart devices have become increasingly popular, providing convenience to households and enhancing the level of intelligence in daily life. This trend is also driving innovation and progress in various fields, including healthcare, transportation, and industry. However, as technology continues to proliferate, network security concerns have become increasingly prominent, making the protection of digital life and data security an urgent priority. Intrusion detection has always played an important role in the field of network security. Traditional intrusion detection systems predominantly rely on anomaly detection technology to identify potential intrusions by detecting abnormal patterns in network traffic. With technological advancements, machine learning-based methods have emerged as the cornerstone of modern intrusion detection, enabling more precise identification of abnormal behaviors and potential intrusions by learning the patterns of normal network traffic. In response to these challenges, this paper introduces an innovative intrusion detection model that amalgamates the Attention-CNN-BiLSTM (ACBL) and Temporal Convolutional Network (TCN) architectures. The ACBL and TCN models excel in processing spatial and temporal features within network traffic data, respectively. This integration harnesses diverse neural network structures to elevate overall model performance and accuracy. Further...