부산대학교 도서관

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. Furthermore, a unique approach inspired by dung beetles’ natural behavior, incorporating Tent mapping-enhanced Dung Beetle Optimization Algorithm (TDBO), is leveraged for both optimizing feature selection parameters and searching for optimal model hyperparameters. The feature selection parameters obtained from TDBO are then combined with the importance ranking from the Random Forest algorithm, ensuring optimal features can be better selected to enhance model performance. This paper introduces a novel intrusion detection model, the TDBO-ACBLT model, and validates its performance using the UNSW-NW15 dataset. TDBO excels in feature selection compared to common algorithms and achieves superior parameter optimization accuracy over Harris’s Hawk Optimization (HHO), Particle Swarm Optimization (PSO), and Dung Beetle Optimization (DBO). The proposed model achieves higher accuracy than prevalent machine learning models.