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.
In the big data environment, federated learning faces multiple challenges such as privacy leakage, poisoning attacks, and communication overload. Existing methods, mostly functioning as point defenses, struggle to simultaneously balance security, efficiency, and utility. This research aims to construct a multi-layered federated learning security system encompassing “source protection, process defense, and global optimization”. The study builds a cloud-edge-end collaborative architecture, integrating differential privacy with Shamir's secret sharing to achieve data source perturbation and gradient share transmission. Through Mixup data augmentation combined with gradient clustering, it proactively detects poisoning attacks and introduces federated unlearning to remediate malicious impacts post factum. Based on static Bayesian games, it dynamically allocates privacy budgets to achieve a Nash equilibrium between personalized privacy and model utility. Experiments conducted on the CIFAR-10 and FEMNIST datasets, using a convolutional neural network as the base model and comparing it with the Vanilla FL model and module ablation versions, demonstrate the following: the FAA achieves a communication overhead of only 91.9 MB with 75 clients. Mixup combined with gradient clustering maintains an accuracy of 69.7% under 24% poisoning attacks. The game-theoretic framework attains a privacy-utility balance coefficient of up to 0.91. In complex dynamic scenarios, the multi-layered framework achieves an accuracy of 76.9%. This system exhibits robust security and adaptability under various attacks, providing a systematic solution for the practical deployment of federated learning.
Malware exhibits characteristics such as rapid variant evolution, sophisticated obfuscation techniques, and frequent zero-day attacks. Existing detection methods suffer from issues like insufficient feature extraction, weak generalization capabilities, and difficulty in capturing code semantic information. This paper proposes a malware detection and classification algorithm based on the fusion of Graph Neural Networks (GNN) and attention mechanisms. First, this paper transforms the control flow graph and function call graph of malware into a heterogeneous graph structure, extracting node and edge features. Second, it employs a Graph Convolutional Network (GCN) for multi-layer feature aggregation, introducing a multi-head attention mechanism to adaptively learn the weights of key code snippets. Then, it reduces dimensionality and integrates global features through a graph pooling layer, utilizing a fully connected layer for binary classification detection and multi-class family identification of malware. Finally, adversarial training is applied to enhance the model's robustness. Verified on a public dataset containing 15000 samples, the overall detection accuracy reached 98.7%, the recall rate reached 98.8%, and the detection rate for confused samples increased to 96.1%. The experimental results show that this method can effectively identify variants of malicious software and has strong practical value.
In response to the difficulty of balancing privacy protection and system efficiency in energy data trading, this article analyzes the limitations of existing methods: static pseudonym mechanisms can easily lead to long-term identity link risks, traditional zk-SNARKs schemes have high computational overhead, and Raft consensus mechanisms lack robustness in adversarial environments. To address the above challenges, an integrated privacy protection scheme based on dynamic pseudonyms and lightweight zk-SNARKs is proposed. This scheme breaks the temporal correlation of transactions through a dynamic pseudonym generation mechanism, uses blockchain level batch processing proofs to reduce the computational and storage overhead of zero knowledge proofs, and introduces an LSTM based node health assessment model and incremental log synchronization mechanism to enhance the error tolerance and synchronization efficiency of the Raft consensus algorithm. The experimental results show that the proposed scheme outperforms traditional methods in terms of privacy, transaction processing performance, and system availability, effectively achieving a balance between privacy protection and operational efficiency, and providing a feasible technical path for energy data trading.
Traditional auditing processes are inefficient and produce low-quality audit reports due to human intervention. This research project constructs a novel automated auditing architecture based on smart contracts, comprising four functional modules: (i) data acquisition, (ii) rule encoding, (iii) execution verification, and (iv) report output. This paper demonstrates how to achieve a high-throughput, low-latency, and verifiable automated auditing system by utilizing technologies such as multi-source data cross-validation, formal encoding of audit rules, privacy protection based on zero-knowledge proofs, and cross-chain communication. The developed novel auditing process can shorten the traditional audit cycle to 8 to 15 days, reduce manual operation costs by 37.5% to 44.4%, reduce the error rate to 0.2% to 0.5%, and exhibit high fault tolerance during disaster recovery, making it an effective approach to achieve digital transformation of auditing processes.
This paper proposes a federated learning (FL) framework that incorporates adaptive gradient compression and dynamic aggregation to address communication efficiency and data privacy issues in the context of FL with limited sample size and non-IID data distributions in edge devices and resource-scarce environments. This proposed framework incorporates dynamic gradient compression techniques that compress gradients based on their magnitude and variance to ensure high communication efficiency with minimal loss in model accuracy. Meanwhile, the proposed framework incorporates dynamic aggregation techniques that assign different weights to clients based on their reliability to ensure effective model convergence in heterogeneous and scarce data distributions. Data privacy in the proposed framework is ensured through secure aggregation and Differential Privacy (DP) techniques. Experimental results on various datasets, including LEAF, FEMNIST, Reddit, and Shakespeare, show that the proposed framework ensures communication efficiency of over 70%, preserves model accuracy with minimal loss at 1-2%, and achieves 30% faster convergence speed compared to traditional FL techniques. These results show that the proposed framework is applicable in real-world scenarios in mobile edge computing and IoT applications, where communication efficiency and data privacy are significant factors for model convergence and deployment. The combination of gradient compression and dynamic aggregation in FL with strong privacy guarantees makes this framework a powerful tool for model convergence in heterogeneous scenarios.
With the continuous growth of data scale in high-density scenarios such as the Internet of Things and the Internet of Vehicles, the repeated storage and frequent transmission of massive data not only causes waste of computing and storage resources, but also significantly increases the risk of sensitive information leakage. Therefore, this study innovatively proposes a data deduplication method that integrates asymmetric homomorphic encryption and federated learning. First, a novel asymmetric homomorphic encryption algorithm is designed using the conjugate search problem and the discrete logarithm problem. This algorithm ensures the indistinguishability of cipher-text while providing a cryptographic foundation for data comparability in the ciphertext state, resolving the inherent conflict between privacy protection and data deduplication. Based on this, the proposed encryption algorithm is combined with a federated learning framework to construct an efficient data processing flow that supports ciphertext deduplication, achieving secure identification and filtering of redundant privacy data. The experimental findings reveal that the encryption cost of the introduced encryption algorithm under 128-bit security strength is only 62.5% of the traditional Paillier scheme, and the ciphertext size is reduced by about 42.4%. When conducting deduplication testing in Internet of Vehicles scenarios, the proposed method achieves a duplicate detection rate of 97.4% on a million-level dataset. Moreover, under the condition of maintaining full encrypted processing, the storage requirements are reduced by an average of 38.6%, and the cross-node communication overhead is reduced by about 29.4%. In summary, the proposed method combines high security, high detection rate, and low overhead in high-density scenarios, achieving a balance between privacy protection and data deduplication efficiency. This research provides a scalable, deployable and practical engineering value technology path for privacy data management in the Internet of Things, industrial Internet, smart cities and other fields.
In response to problems such as a lack of trust, low resource utilization rates, conflicts due to multiple constraints, and security risks associated with sharing 5G wireless access network resources, this study proposes an efficient, trustworthy, and secure distributed resource sharing system and optimizes the resource allocation strategy. First, it performs virtual decoupling and atomic modeling for the three core computing resources: spectrum, security, and computing power. It also designs a five-layer distributed resource-sharing framework that integrates blockchain and software-defined networks. Additionally, it proposes an improved delegated proof-of-stake consensus mechanism, as well as an asymmetric encryption transaction authentication and resource status traceability mechanism. Second, for the multi-constraint conflict issue, it designs a multi-agent deep deterministic strategy gradient secure resource allocation algorithm integrating long-term and short-term memory state prediction. The verification experiments were carried out based on the 5G-RAN public resource scheduling dataset in accordance with the 3GPP TR38.901 protocol specification. The experimental hardware was equipped with Intel Core i9-13900K processor, NVIDIA RTX 4090 graphics card, etc. The simulation platform was built on the Ubuntu 22.04 LTS system using the PyTorch 2.1.0 deep learning framework and the NS-3 3.36 simulation tool. The comparison benchmarks were mainstream centralized resource allocation schemes, blockchain, federated deep reinforcement learning schemes, and consortium chain hierarchical cross-slice schemes. The experimental results showed that the resource utilization rate of this framework reached 89.3%, the transaction delay was only 21.8 ms, the service quality satisfaction and security compliance rate were 96.7% and 98.2% respectively, the double-spend attack resistance rate and resource status traceability accuracy rate both reached 99.9%, and all related indicators were significantly superior to the existing comparison schemes. This study provided technical support for 5G resource collaboration in scenarios such as industrial internet and vehicle networking, effectively solving the trust bottleneck and scheduling problems in distributed environments. However, the research has not fully considered the adaptability of resource scheduling in extreme network environments. The computational power consumption of the algorithm in large-scale node deployment scenarios must be optimized further. The computational cost of the blockchain and multi-agent deep reinforcement learning components is high. Additionally, the system's scalability in ultra-dense 5G scenarios must be improved. To a certain extent, this framework's immediate large-scale practical application in complex 5G network environments is limited.
This study proposes a secure routing planning algorithm that integrates an improved Dempster-Shafer Evidence Theory (D-S ET) and enhanced Multi-Objective Ant Colony Optimization (MOACO) to balance security protection and Energy Consumption (EC) in Wireless Sensor Networks (WSN). For the D-S ET, we correct third-party recommendation bias by calculating evidence distance and introducing discount coefficients, thereby optimizing its conflict handling mechanism, solving the problem of node credibility misjudgment caused by traditional evidence fusion, and achieving accurate Node Trust Evaluation (NTE) through a direct and indirect dual trust mechanism. For MOACO, we integrated elite retention strategy, improved crowding distance, and mutation convergence operation to optimize the Pareto optimal solution set, improving the search accuracy and stability of the algorithm, and achieving multi-objective routing optimization with node trust value and remaining energy as the core objectives. Based on a 100 node WSN simulation environment, the algorithm was compared with typical baseline methods under consistent initial parameter settings. The experimental results show that when the proportion of Malicious Nodes (MN) is 25%, the detection rate of MN in this algorithm is 84.5%, and the false positive rate is 9.2%, which is better than the comparative methods. In terms of routing performance, it extends the lifecycle of WSN to 937 rounds, maintaining a stable throughput of 4344 bps and a minimum average delay of 33 ms. Without black hole attacks, the MN is only 37.5 J. Faced with 10 single type black hole attack nodes, its MN decreases by 4.8%, and the packet loss rate is controlled at 9.9%, demonstrating excellent anti-attack performance. This algorithm effectively balances the security and energy efficiency of WSN, and innovative improvements to the core algorithm provide reliable technical support for the efficient and stable operation of WSN, with significant practical application value.
With the increasingly complex network environment, intrusion detection systems are faced with severe challenges such as high-dimensional feature redundancy, category imbalance and low detection accuracy. Aiming at these problems, this paper proposes a multi-objective and multi-strategy collab-orative optimization intrusion detection model (MCO-IDM). The model innovatively integrates multi-objective optimization techniques to simultaneously optimize conflicting objectives such as minimizing the number of features, maximizing detection accuracy and minimizing false alarm rates, and integrates collaborative search strategies such as dynamic adaptive mechanisms and swarm intelligence optimization algorithms (such as CMO-BOA) to achieve efficient trade-offs through Pareto frontier search and weight adjustment. The test results show that on globally public datasets such as KDD CUP99, NSL-KDD and CIC-IDS2017, MCO-IDM achieves the highest accuracy rate of 97.8%, the false alarm rate is reduced to 4.3%, and the training time is controlled within 185.3 seconds. At the same time, it maintains high robustness and scalability under different data scales. These results confirm the effectiveness of the model in feature selection, parameter optimization and multi-policy collaboration, and provide a new scheme with high precision and strong practicability for network intrusion detection.
Real-time video streaming over wireless networks has become increasingly reliant on adaptive bitrate (ABR) control to mitigate variability in bandwidth, latency, and user mobility. However, existing ABR algorithms are predominantly reactive, operate on limited network observability, and largely ignore the computational and bandwidth overhead introduced by encryption, which is now ubiquitous in edge-assisted multimedia delivery. This paper presents a machine-learning driven adaptive bitrate optimization framework that jointly addresses predictive bandwidth estimation, mobility dynamics, and security constraints in edge-assisted video transmission. We formulate bitrate selection as a stochastic optimization problem and develop a cross-layer system model that characterizes network evolution, user mobility, and cryptographic overhead. An edge-hosted learning engine leverages supervised prediction and reinforcement-driven control to proactively select bitrates using features derived from transport behavior, playback state, and security cost. We implement the proposed approach in a prototype edge-streaming platform and evaluate performance under realistic wireless traces, user mobility patterns, and multi-user contention. Experimental results demonstrate that the proposed system reduces stall probability by up to 42%, improves average Quality of Experience (QoE) by up to 27%, and maintains equitable performance under multi-user load, while introducing only modest cryptographic overhead. We further analyze the security-performance trade-offs, identify risk factors in encrypted media pipelines, and quantify the operational limits of edge execution. The results highlight the importance of integrating prediction, security-awareness, and scalability into ABR design, and demonstrate the potential of edge-hosted learning models to enable secure, high-quality, and resource-efficient video streaming in mobile environments.
The fast development of the Internet of Things (IoT) has introduced significant threats due to the growing number of connected devices and the vulnerability of data transmitted. Conventional security systems often fail to address the specific requirements of IoT networks because of the limited resources of individual devices and the complexity of the interconnected ecosystem in which they are implemented. The research introduces SecureNet-IoT, a modern security framework that will strengthen the security of connected systems and networks. The framework uses a tuned Intelligent Random Forest (ASSO-IRF) that uses an ASSO to predict the behaviour of IoT devices and distinguish between various types of attacks. SecureNet-IoT actively detects and neutralizes the possible vulnerabilities within the environments of IoT and fog computing. The information was gathered on IoT devices in smart homes and industrial IoTs, including device communication, communication logs, and network traffic, which were pre-processed with a Kalman filter to eliminate noise and normalization techniques to normalize the data. The model classifies devices as authentic, breached, or fake and uses a suggested model to forecast malicious acts. It approximates the possibilities of transitions between states, thus being able to detect threats early. In addition, the framework also analyses device communications to enhance predictive accuracy. The metrics used to assess performance were accuracy (98.96%), F1-score (96.31%), precision (98.78%), and recall (96.24%). The framework demonstrates the system's effectiveness in preventing malicious behavior by successfully categorizing device states, estimating transition probabilities, and analyzing device communications, ultimately enhancing IoT system security and integrity.
With the increasing complexity and security threats in cyberspace, network security situation assessment has become a key technology to ensure digital security. This study proposes a hybrid model integrating graph convolutional neural networks and multi-layer perceptrons to address the limitations of traditional methods in capturing the topological associations of complex networks and dynamic threat responses. First, this model uses graph convolutional neural networks to aggregate node neighborhood information and capture topological features. Then, it conducts deep nonlinear feature learning through multi-layer perceptrons. Finally, it screens key information through pooling layers. Finally, the situation level assessment is achieved by the Softmax classifier. Experiments showed that the accuracy rates of the model on the CICIDS and UNSW-NB15 datasets reached 96.5% and 94.3% respectively, and its performance was superior to that of the comparison models. In the simulation and dynamic environment tests, the model evaluation results were stable, with an average evaluation time of only 66.19 ms and a resource utilization rate of 53.87%. The hybrid model constructed in this study effectively overcomes the challenges of feature fusion and classification in complex network environments. It provides a novel solution for efficiently and accurately assessing network security situations and has significant practical application value.
With the acceleration of informatization and digitization, the tax system has generated massive amounts of data with diverse types and large scales. However, the data sharing across regions and institutions faces challenges on privacy protection and compliance. Therefore, a distributed machine learning privacy protection algorithm for tax big data is proposed, and a multi-layer secure transmission mechanism combining differential privacy, homomorphic encryption, and secure multi-party computation is designed. In the experiment, real invoices and tax declaration data from provincial tax bureaus, as well as simulated data generated based on these data, are selected to compare various existing methods. The results showed that the accuracy in classification tasks reached 0.87, which was 2.35%–8.75% higher than that of traditional distributed methods. In the regression task, the mean square error and mean absolute error were reduced by 10%–40% and 22.03%, respectively. Compared to homomorphic encryption methods, the designed method reduced communication overhead by 59.67% and achieved a fault tolerance of 96.38% under the 10% node dropout rate. In addition, the accuracy decrease in poisoning attack scenarios was only 25.29%, which was superior to other methods. This algorithm can achieve high predictive performance and robustness while ensuring privacy and compliance, providing effective technical support for intelligent tax governance.
Data security in information collection systems faces challenges such as malicious attacks, data leaks, and delayed disaster recovery. This paper proposes a data security model for disaster recovery in information collection systems based on Artificial Intelligence (AI) encryption algorithms. By introducing a dynamic encryption algorithm driven by Deep Learning (DL), this model achieves real-time secure encryption and intelligent key management for collected data. First, feature extraction is performed on the data stream, and a Convolutional Neural Network (CNN) is used to identify abnormal access behavior, triggering a multi-factor dynamic encryption mechanism. Second, a Generative Adversarial Network (GAN) is used to check the integrity of backup data to prevent tampering and loss. Finally, distributed key storage and access auditing are implemented based on blockchain technology. The proposed model maintains a data encryption speed of 3.1–3.3 ms, a recovery efficiency of 4.09 ms, and a data integrity verification accuracy of 99.5%. This approach effectively improves the security and recovery reliability of disaster recovery data in information collection systems, providing a new approach for data security assurance.
As power systems become increasingly digitalized and intelligent, security threats to power communication networks exhibit characteristics of multi-source, complexity, and stealth. Traditional rule-based or threshold-based security monitoring methods struggle to meet the demands of refined situational awareness. Addressing challenges such as the difficulty of integrating multi-source heterogeneous data, high false alarm rates in alerts, and the complex propagation mechanisms of link congestion, this paper proposes a power network security situational awareness framework that integrates information entropy quantification, LDA semantic topic enhancement, and XGBoost ensemble learning. This approach first performs multi-source data preprocessing through weighted fusion and Kalman smoothing. It then constructs vulnerability severity and attack impact models based on information entropy, enabling a quantifiable representation of the power network security posture. Building upon this foundation, an LDA-XGBoost-based false alarm detection model is developed, significantly enhancing alert credibility and classification accuracy. Additionally, an active-passive adjustment mecha-nism optimizes communication link congestion states. Experimental results demonstrate that the proposed solution reduces data redundancy by 81.8%, elevates anomaly detection accuracy to 96.8%, achieves a 98.50% resistance rate against encryption cracking, and effectively improves link status indices across multiple cases.
