In the Internet of Things, openness significantly increases security risks. Therefore, detecting such attacks through anomaly traffic monitoring is crucial for ensuring network security. Although existing Intrusion Detection Systems (IDS) have made some progress in detection performance using deep learning techniques (e.g., graph learning and meta-learning), the class imbalance problem remains a major challenge that needs to be addressed. To this end, we propose a Double-Anchor Contrastive Learning (DACL) framework. Firstly, we introduce a positive-negative sample construction module. Compared to traditional contrastive learning methods, this module directly constructs sample pairs, preserving more flow information and thereby enhancing the effectiveness. Secondly, we design a feature embedding and extraction module. By utilizing convolutional neural networks to extract spatial correlations among raw features, we generate more discriminative feature representations. Subsequently, we propose the double-anchor contrastive learning module, introducing a double-anchor mechanism to impose double constraints on inter-class distances, further enhancing feature representation capabilities and clustering effects. Finally, we map the learned features to a low-dimensional space via a classification network, achieving precise detection of abnormal traffic. Extensive experiments demonstrate that DACL outperforms existing works in terms of accuracy on datasets such as CIC-DDoS2017, CIC-IDS2018, and CIC-DDoS2019.

In Cognitive Radio Networks (CRNs), network fault analysis, traffic tracing, and resource optimization are challenging tasks. With the increasing number of wireless applications and the conflict with limited wireless spectrum resources, the Sniffers Channel Assignment problem in CRNs has become particularly important. To address this issue, we propose a Value Decomposition Networks-based channel selection (CSVDN) algorithm. During centralized training, the Monitoring Quality Network (MQN) is trained based on observed data, using global information to calculate the Quality of Monitoring (QoM), which is then used as a reward to guide sniffers in selecting the optimal channels. During decentralized execution, sniffers share model parameters and independently run the MQN, sequentially selecting the optimal channels. This process ensures that sniffers collectively maximize network coverage while maintaining distributed control, thereby improving efficiency and scalability in dynamic environments. The results from NS-3 simulations show that CSVDN provides a distributed and implementable channel selection solution with high scalability and practicality, making it particularly suitable for large-scale CRNs.

To improve the reliability and efficiency of underwater acoustic sensor networks (UASNs) under limited node energy and network attacks, a joint scheme of enhancing robustness and extending network lifetime is proposed in this paper. Scale-free networks exhibit strong robustness against random failures, and because of the lower number of link connections, nodes consume energy more slowly. We first introduce a new scale-free topology evolution model that adjusts the initial number of nodes according to the characteristics of UASNs. This model incorporates factors such as flow load, energy consumption, and distance into the preferential attachment mechanism, balancing the network load and enhancing its resistance to attacks. Further, based on this topology, we propose a network flow adjustment algorithm that features the joint selection of paths and corresponding power levels. Energy consumption is balanced among nodes in proportion to their residual energy, rather than by minimizing the absolute consumed power. Numerical simulations show that different topologies significantly affect network lifetime, which is the longest when the number of links added is 2. The network lifetime of the proposed scheme surpasses the state-of-the-art schemes, such as ETFLA, Initial-BA, and VODA, by up to 37.18%, 46.23%, and 128.80%, respectively.

In wireless networks, utilizing sniffers for fault analysis, traffic traceback, and resource optimization is a crucial task. However, existing centralized algorithms cannot be applied to high-density wireless networks. Therefore, distributed optimization of channel selection to maximize the monitoring rate of sensors in Wireless Monitoring Networks (WMNs) is a challenge. This paper proposes a joint double auction-based distributed channel selection algorithm (J2A-CS) to maximize overall quality of monitoring (QoM). First, sniffers are redundantly deployed in WMNs, and an initial channel allocation strategy is formulated. Subsequently, sniffers collectively act as buyers and sellers at different stages. Finally, buyers bid asynchronously, and sellers settle synchronously to maximize the seller’s marginal revenue and update the channel selection scheme. As a distributed channel selection algorithm, J2A-CS addresses the highest overall QoM issue in WMNs, demonstrating high scalability and fault tolerance. Simulation results show that J2A-CS significantly improves QoM compared to existing distributed algorithms and outperforms centralized algorithms in high-density scenarios.