Shidan Sun

With the rapid increase in cyber-attacks, intrusion detection systems (IDS) have become essential for network security. However, traditional IDS methods often struggle with class imbalance, leading to asymmetric data distributions that adversely affect detection performance and model generalization. To address this issue and enhance detection accuracy, this paper proposes SE-DWNet, a residual network model incorporating an attention mechanism and one-dimensional depthwise separable convolution, trained on a symmetrically preprocessed dataset using SMOTETomek sampling. First, the feature distributions of the training and test datasets are analyzed using box plots, highlighting the impact of feature difference. To mitigate this difference and restore a more symmetric data distribution, we employ the SMOTETomek integrated sampling method in conjunction with a Focal Loss function. Subsequently, a lightweight residual network, incorporating the SE module and the Res-DWNet module, is designed to improve detection accuracy while maintaining computational efficiency. Extensive experiments on the NSL-KDD, CICIDS2018, and ToN-IoT datasets demonstrate that SE-DWNet outperforms existing neural network-based IDS models, achieving accuracy, precision, recall, and F1-score improvements ranging from 0.17% to 5.33%. The results confirm the effectiveness and superiority of the proposed approach in intrusion detection tasks.

Maritime surveillance video (MSV) target detection systems are important for maritime security and ocean economy. Hindered by many complex factors, the existing MSV target detection systems have low detection accuracy. These factors include target distance, potential occlusion from rain and fog, and limited computing power of edge devices. To overcome these factors, a high performance and lightweight maritime target detection algorithm (HPMTD) is proposed in this paper. HPMTD consists of three modules: feature extraction, shallow feature progressive fusion (SFPF), and multi-scale sensing head. In the feature extraction module, a global coordinate attention-optimized offset regression module is proposed for deformable convolution. Thus, the ability to handle low visibility and target occlusion issues is enhanced. In the SFPF module, the ghost dynamic convolution combined with low-cost adaptive spatial feature fusion is proposed. In this way, lightweight design can be realized, and multi-scale target-detecting capacity can be increased. Furthermore, multi-scale sensing head is incorporated to learn and fuse scale features more effectively, thus improving localization accuracy. To evaluate the performance of the proposed algorithm, the Singapore Maritime Dataset is adopted in our experiments. The experimental results show that the proposed algorithm can achieve a nearly 10 percent mean average precision value improvement with nearly half the model size, compared with counterparts. Furthermore, the proposed algorithm runs three times faster with only half of the computation resources, and maintains nearly same accuracy in the maritime surface with low visibility. These results demonstrate that the HPMTD achieves lightweight and high-precision detection of marine targets.

Aiming at a series of problems such as detection accuracy, calculation blocking, display delay, and so on in the ship detection of surveillance video, an improved YOLOv5 algorithm is proposed in this paper. First, to improve the detection performance, it is proposed to optimize the anchor box algorithm in the YOLOv5 network according to the ship target characteristics. Then, the t‐SNE algorithm is used to reduce and visualize the data set label information and perform weighted analysis on the processed features for low‐dimensional data. The mapped kernel k‐means clustering algorithm adaptively selects a more appropriate anchor box and considers the detection performance of large and small ship targets. Secondly, to improve the problem of computational blocking and delay, the BN scaling factor γ is used to compress the YOLOv5 network, so that the model can be reduced without reducing the detection performance. The optimized YOLOv5 framework is trained on the self‐integrated data set. The accuracy of the algorithm is increased by 2.34%, and the ship detection speed reaches 98 fps and 20 fps in the server environment and the low computing power version (Jetson nano), respectively.