Baokang Zhao

SDN data plane generally utilizes TCAM to accommodate flow tables for fast packet classification, which also leads to serious problem of high energy consumption. Existing techniques are difficult to stably achieve satisfactory energy-saving effect especially under network traffic jitters. To address this problem, this paper first designs an elastic energy-saving cache to always keep sufficient number of active exact flows for stably high cache hit rates even under network traffic jitters. Particularly, we adaptively adjust the number of cache segments, in terms of the relationship between current cache hit rate and its preset expected range, to maintain high cache hit rate. Meanwhile, we regulate the threshold of packet inter-arrival time for identifying active exact flows, in accordance with current cache occupancy rate, to match the number of active exact flows with cache capacity. Furthermore, we theoretically derive cache occupancy rate based on randomly mapping assumption of each active exact flow and cache hit rate based on the assumption of flow activity degree model. Subsequently, we build an elastically energy-saving flow table storage architecture, by applying the elastic energy-saving cache to always enable a majority of incoming packets to bypass energy-hungry TCAM flow table lookups. Finally, we set up an experimental SDN platform to evaluate its performance on real network traffic traces. Experimental results indicate that our built flow table storage architecture steadily achieves high energy saving rates around 82.43% even under network traffic jitters, with the increase of 6.32˜7.55% compared to the state-of-the-art one.

Anomaly detection performance in sensor data is highly sensitive to model hyperparameters, which is central to reliable monitoring in mobile Internet security and industrial IoT (IIoT) scenarios. We propose an IWOA-LightGBM-based anomaly detection method for sensor data. For machine learning-based anomaly detection methods, hyperparameter selection often determines model performance, so we propose an Improved Whale Optimization Algorithm (IWOA) and further use it to optimize the hyperparameters of the LightGBM algorithm. To avoid falling into local optima and accelerate algorithm convergence, the WOA is improved by integrating nonlinear convergence factor, adaptive inertia weight factor and stochastic differential mutation strategy. Experimental results show that during hyperparameter optimization for LightGBM model training, the IWOA achieves faster convergence and higher computational efficiency compared to the Whale Optimization Algorithm (WOA), with anomaly detection accuracy exceeding 90%.

Quantum clock synchronization underpins modern secure communications and critical infrastructure, yet its fundamental dependence on channel reciprocity introduces an exploitable vulnerability to asymmetric delay attacks. Current attack strategies rely on static delays, limiting their ability to target application-specific stability requirements. Here, we propose a tunable asymmetric delay attack (T-ADA) that dynamically controls delay parameters to induce manipulate synchronization accuracy. Through experimental implementation, we demonstrate how tailored attack trajectories can selectively compromise system stability across different scenarios. This work uncovers key vulnerabilities in synchronization protocols under customizable attacks and provide a foundation for developing secure and resilient quantum clock synchronization systems.

The rapid proliferation of Internet of Things (IoT) technologies intensifies network anomaly detection challenges because of high-dimensional and redundant data. To address this issue, we propose causal gray wolf optimization (CGWO), a framework that integrates the global search capabilities of gray wolf optimization (GWO) with causal inference to distinguish causal features from spurious correlations. CGWO employs a four-stage process: 1) data preprocessing with causal weight transformation; 2) causal analysis via potential outcome models (POMs); 3) feature subset optimization using a fitness function balancing accuracy and dimensionality; and 4) anomaly detection with an enhanced convolutional autoencoder (ECAE). By prioritizing causal relationships over statistical correlations, CGWO minimizes redundant features while increasing model interpretability. When evaluated with benchmark datasets (NSL-KDD and UNSW-NB15), CGWO achieves 99.8% accuracy (14 features) for five-class classification with NSL-KDD and 94.2% accuracy (ten features) for ten-class classification with UNSW-NB15, outperforming conventional methods by more than 6%. The framework reduces feature dimensions by 65%–80% without performance loss, demonstrating robustness, computational efficiency (120–150 s per dataset), and scalability for edge computing. These results validate the effectiveness of CGWO in balancing dimensionality reduction and interpretable model design for complex network environments.

In the paradigm of Software-Defined Networking (SDN), its data plane generally perform packet forwarding based on flow table lookup on TCAM with high energy consumption. Popular energy-saving methods employ caching techniques for most packets to bypass energy-intensive TCAM lookups. However, existing energy-saving caches cannot adapt to network traffic fluctuation with sufficient utilization of cache space due to non-negligible hash conflicts. To overcome this issue, we design a traffic-aware energy-saving cache with optimal utilization for TCAM flow tables in SDN data plane. In particular, we first devise a nearly conflict-free hashing algorithm for the cache called FelisCatus, which provides three candidate locations for each incoming flow by adjacent hopping, and searches for an empty or replaceable entry for each conflicting flow by co-directional kicking. Then, we propose an adaptive adjustment mechanism of flow activity criterion, i.e., packet inter-arrival time threshold, for enabling the cache to consistently accommodate the most active exact flows in network traffic. Furthermore, we build an energy-efficient SDN flow table storage architecture by applying the above cache and exploiting the accessing features of different memories. Finally, we verify the performance of our designed energy-saving cache and flow table storage architecture by experiments with backbone network traffic traces. Experimental results indicate that, our designed energy-saving cache obtains stable and high hit rates around 75% even under network traffic fluctuation, and our proposed flow table storage architecture achieve high energy saving rates around 71%, with the increase of 7.89% compared to state-of-the-art ones.