Traditional federated learning has challenges such as slow model convergence, lagging situational awareness, and risk of gradient privacy leakage in cyber threat collaborative analysis capability. This study proposes a federated learning privacy-preserving collaboration model based on threat intelligence drive and hierarchical aggregation. The model optimizes participating nodes through a dynamic client selection mechanism, uses a hierarchical aggregation strategy to balance the learning of basic features and advanced threat patterns, and introduces an adaptive differential privacy mechanism to strengthen gradient protection. The experiment is based on the CIC-IDS-2018 public dataset, which covers various types of attacks in real network environments, with a data volume of approximately 5 million pieces. It is divided into 50 clients in a non-independent and identically distributed manner to simulate cross organizational collaboration scenarios. In the threat detection task, the model designed by the research institute achieved an accuracy of 91.8% and an F1 Score of 89.8% compared to baseline models such as FedAvg, FedProx, and DP FedAvg. All indicators were superior to the comparison model. In addition, in the advanced persistent threat attack scenario, the attack chain detection rate increased to 95.6%, and the average detection time was shortened to 2.8 hours. In terms of privacy protection, the Rényi privacy loss was only 2.89 with a budget of ε=3.0. The proposed model effectively improves the efficiency and timeliness of collaborative detection of cross-organizational threats while ensuring data privacy and provides a feasible solution for building a safe and reliable collaborative defense system. It should be pointed out that while improving detection performance, the model introduces additional communication overhead caused by hierarchical aggregation and dynamic selection mechanisms. The average communication traffic in the experiment was about 13.8 GB. As the number of clients expands to a larger scale, the computational load and scheduling complexity of the coordination layer will further increase. In addition, although the non-independent and identically distributed data partitioning used in the experiment can simulate real heterogeneous scenarios, the convergence efficiency of the model under extreme distributions still needs further verification. The above limitations will be optimized in future work by introducing asynchronous aggregation and lightweight communication protocols.
A constraint-aware adversarially robust Internet of Things (IoT) traffic classification system with protocol validity, device behavior consistency, and manifold-aware training and evaluation is presented in this study. In realistic IoT communication semantics, resilience as a constrained min–max optimization problem allows adversarial perturbations. Comprehensive testing on sample IoT traffic datasets shows that baseline models achieve 95.1% accuracy under benign conditions but plummet following hostile attacks. The proposed defense reduces untargeted attack success rates to <18% while achieving 81.3% accuracy at ε=0.05 and 70.6% at ε=0.10. The proposed constraint-aware adversarial framework significantly enhances IoT traffic classification by achieving 97.4% accuracy and maintaining 90.6% robustness at ε=0.10, outperforming state-of-the-art methods. It reduces attack success rates to 11.2% (untargeted) and 7.9% (targeted) through protocol-compliant perturbations and manifold-aware learning. Additionally, the model achieves an efficient trade-off with 21.4 ms latency and 650 flows/sec throughput, making it suitable for real-time edge deployment. These results demonstrate improved robustness, realism, and deployability compared to existing approaches.
The high aggregation of user relationship and behavioral data in social networks continues to aggravate privacy leaks. How to strike a balance between privacy protection and data availability has become a research hotspot. To collaboratively optimize user information security and community structure identification, this study proposes a social network privacy protection model that integrates differential privacy technology and community discovery algorithms. First, a differential privacy noise injection mechanism is constructed to perturb node data and combine it with blockchain storage to ensure that the data cannot be tampered with. Then, a community division strategy based on information entropy and mutual information is introduced to achieve high-precision community identification through modularity optimization. The accuracy of the proposed model reached 98.1% when the data set size was 800, which was about 3.4% and 9% higher than that of other models, respectively. The root mean square error was 8.2, which was about 20% lower than that of the traditional model. The convergence speed was increased to 380 iterations, which was about 15% faster than that of the comparison algorithm. The privacy protection strength and scalability scores reached 9.3 and 9.5, respectively. The simulation test results showed that, under different data types, the accuracy of the model grew from 0.87 to 0.98, and the F1 value grew from 0.84 to 0.95. The integration of differential privacy and community discovery effectively improves the privacy protection strength and structural analysis accuracy of social networks, providing a highly feasible solution for multi-scenario social data security analysis.
To make image information more authentic and complete and to prevent splicing tampered image data from affecting social fairness, this paper proposes an algorithm based on Deep Convolutional Neural Networks to detect splicing tampered digital media image privacy. Building upon deep convolutional feature extraction, this algorithm introduces a self-attention mechanism to enhance focus on tampered regions. For the first time, it innovatively applies a boundary-aware loss function to patch tampering detection, effectively addressing the issue of ambiguous boundary region detection and significantly improving localization accuracy. The experiment was conducted on datasets from the Institute of Automation, Chinese Academy of Sciences, Cover Dataset, National Institute of Standards and Technology datasets, and the 2020 Image Tampering Dataset. The algorithm demonstrated the following performance metrics: area under the receiver operating characteristic curve values of 0.971, 0.961, and 0.987, respectively; accuracy rate of 98.94%; precision rate of 97.12%; recall rate of 99.16%; F1 mean values of 0.941 and 0.952 under gamma ray and noise interference, respectively, indicating strong robustness. These results prove that the proposed algorithm can achieve precise detection of splicing tampered image privacy. It effectively addresses the problem of insufficient detection accuracy in some existing methods. It also promotes the intelligent development of detection and contributes to building a more authentic information environment in society.
The traditional network security situational awareness method is difficult to deal with high-speed multi-source data flow because it relies on a centralized data processing architecture, resulting in poor real-time performance and weak data association. Therefore, building a perception prediction model that can fuse multi-source data in real time, understand the internal structure of the network, and have cognitive reasoning capabilities similar to experts is of great theoretical significance for realizing active intelligent network security defense. The study hypothesizes that the integration of distributed flow processing, network structure mode decomposition, and a cognitive decision-making framework can build a security perception model with high-precision perception and forward-looking prediction ability. The core of this method is to integrate the above components into a unified model of “data structure cognition” three-tier collaboration: Kafka+spark stream processing layer is responsible for real-time data fusion and feature supply. The Structural Modal Modeling and Decomposition (SMMD) layer analyzes the functional topology of the network to achieve fine-grained situation decoupling; The Estimation Memory Control (EMC) cognitive layer simulates the closed-loop process of expert evaluation, experience reuse, and predictive decision-making. This architecture realizes the deep coupling of data-driven, structural understanding, and cognitive intelligence, which is different from the existing situational awareness framework that mainly relies on a single data dimension or lacks explicit cognitive reasoning. To verify this hypothesis, several experiments are designed and implemented. Firstly, a distributed stream processing framework is built based on Kafka+spark to realize the real-time fusion and feature extraction of multi-source security data. Secondly, a safety perception prediction model combining the EMC framework and SMMD is proposed. The network functional topology is analyzed by structural mode decomposition, and the EMC framework is introduced to simulate the expert cognitive decision-making process. The core findings are as follows: Experiments on the Canadian Institute for Network Security network intrusion detection dataset (cic-ids-2017) show that the accuracy of the model in predicting the macro situation level is 93.7%, and the F1-Score for identifying five types of attacks is up to 97.2%. This performance is superior to the mainstream baseline models of LSTM, TCN, and GBDT. In the verification of the real network range, the model can shorten the average response time of high-risk threats to 3.5 minutes, improve the attack containment rate to 95.2%, reduce the false positive intervention rate to 8%, and improve the analysis efficiency by about 83%. The conclusion is that the proposed model is superior to the mainstream methods in perception accuracy, response speed, and operation and maintenance efficiency. This study provides effective technical support for the construction of an active and intelligent network security protection system. This study also recognizes that the model has some limitations: Its performance depends on high-quality labeled data for initial training and pattern library construction; When dealing with large-scale networks (such as more than 10,000 devices), the computational cost of mode division and state estimation needs to be further optimized; In addition, the prediction ability of the model to the new attack mode (zero day) that has not appeared in the training data remains to be explored. Although the model has been validated on the cic-ids-2017 standard dataset and enterprise-wide, its ability to generalize to other network architectures (such as cloud native, IoT) and more complex real-world operational environments is a direction that needs to be evaluated before actual deployment in the future.
