Traditional access control systems exhibit limitations in providing flexible authorization and fine-grained access in the face of increasingly complex and dynamic access scenarios. This paper proposes a context-aware risk access control model to address these challenges. By developing a multi-level contextual risk indicator system, the model comprehensively considers real-time contextual information associated with access requests, dynamically evaluates the risk level of these requests, and compares the outcomes with predefined risk policies to facilitate access decisions. This approach enhances the dynamism and flexibility of access control. To improve the accuracy and reliability of risk assessments, we propose a combination weighting method grounded in game theory. This method reconciles subjective biases and the limitations of objective data by integrating both subjective and objective weighting techniques, thus optimizing the determination process for risk factor weights. Furthermore, smart contracts are introduced to monitor user behavior during access sessions, thereby preventing malicious attacks and the leakage of sensitive information. Finally, the model’s performance and authorization granularity are assessed through empirical experiments. The results indicate that the model effectively addresses the requirements of dynamic and fine-grained access scenarios, improving the system’s adaptability to risk fluctuations while safeguarding sensitive information.

The exponential rise of phishing attacks has become a critical threat to online security, exploiting both system vulnerabilities and human psychology. Although anti-phishing blacklists serve as a primary defense mechanism, they are limited by incomplete coverage and delayed updates, making them susceptible to evasion by sophisticated attackers. This study presents a comprehensive security analysis of anti-phishing blacklists and introduces two novel cloaking attacks—Feature-Driven Cloaking and Transport Layer Security (TLS)-Based Cloaking—that exploit vulnerabilities in the automated detection systems of anti-phishing entities (APEs). Using real-world data and employing machine learning techniques, the Random Forest (RF) classifier emerged as the most effective among all tested supervised classifiers, achieving 100% accuracy in distinguishing APEs from regular users and enabling attackers to bypass blacklist detection. Key findings highlight critical security flaws in major APEs, including limited infrastructure diversity, feature implementation inconsistencies, and vulnerabilities to Web Real-Time Communication (WebRTC) Internet Protocol (IP) leaks. These weaknesses extend the operational lifespan of phishing websites, heightening risks to users. The results emphasize the need for APEs to implement more robust and adaptive defenses and propose mitigation strategies to enhance the resilience of the anti-phishing ecosystem.

Cyberattacks, especially Advanced Persistent Threats (APTs), have become more complex. These evolving threats challenge traditional defense systems, which struggle to counter long-lasting and covert attacks. Cybersecurity Knowledge Graphs (CKGs), enabled through the integration of multi-source CTI, introduce novel approaches for proactive defense. However, building CKGs faces challenges such as unclear terminology, overlapping entity relationships in attack chains, and differences in CTI across sources. To tackle these challenges, we propose the CyberKG framework, which improves entity recognition and relation extraction using a SecureBERT_Plus-BiLSTM-Attention-CRF joint architecture. Semantic features are captured using a domain-adapted SecureBERT_Plus model, while temporal dependencies are modeled through BiLSTM. Attention mechanisms highlight key cross-sentence relationships, while CRF incorporates ATT&CK rule constraints. Hierarchical clustering (HAC), based on contextual embeddings, facilitates dynamic entity disambiguation and semantic fusion. Experimental evaluations on the DNRTI and MalwareDB datasets demonstrate strong performance in extraction accuracy, entity normalization, and the resolution of overlapping relations. The constructed knowledge graph supports APT tracking, attack-chain provenance, proactive defense prediction.

Currently, most quantum homomorphic encryption (QHE) schemes only allow a single evaluator (server) to accomplish computation tasks on encrypted data shared by the data owner (user). In addition, the quantum computing capability of the evaluator and the scope of quantum computation it can perform are usually somewhat limited, which significantly reduces the flexibility of the scheme in quantum network environments. In this paper, we propose a novel $(t,n)$-threshold QHE (TQHE) network scheme based on the Shamir secret sharing protocol, which allows $k(t\le k\le n)$ evaluators to collaboratively perform evaluation computation operations on each qubit within the shared encrypted sequence. Moreover, each evaluator, while possessing the ability to perform all single-qubit unitary operations, is able to perform arbitrary single-qubit gate computation task assigned by the data owner. We give a specific (3, 5)-threshold example, illustrating the scheme’s correctness and feasibility, and simulate it on IBM quantum computing cloud platform. Finally, it is shown that the scheme is secure by analyzing encryption/decryption private keys, ciphertext quantum state sequences during transmission, plaintext quantum state sequence, and the result after computations on the plaintext quantum state sequence.

With the increasing economic globalization and the cooperation system of division of labor in the automobile industry, the multi-core network complex cooperative relationship between upstream and downstream enterprises and automobile manufacturing enterprises is gradually formed. Therefore, the automobile manufacturing enterprises at the core of the industrial chain, how to effectively control the upstream and downstream cooperative enterprises in the wide area manufacturing environment, has gradually become a severe challenge to the competition of the industrial chain. In view of the above problems, this article abandoned the idea of traditional single industry chain evaluation, and innovatively put forward a multi-core and cross-chain cooperative evaluation scheme based on the cloud platform environment. First of all, a multi-core and cross-chain cooperative relationship model is constructed through using the industrial cooperative relationship between upstream and downstream enterprise clusters and core enterprises. Combined with this model, a multi-level framework supporting multi-core and cross-chain evaluation based on cloud platform is explored and constructed. Then, taking the evaluation of the downstream marketing chain as an example, the first-level and second-level correlation index system based on multi-core and cross-chain is constructed one by one. Combined with the internal relationship between the two levels of index elements, this article establishes a corresponding multi-core and cross-chain index weight system. In order to solve the problem of multi-core and cross-chain evaluation in cloud environment, this article constructs a multi-core and cross-chain evaluation model based on cloud platform cooperation relationship through the semantic research of cooperation relationship between upstream and downstream enterprises and core enterprises. Then the corresponding cross-chain cooperative evaluation algorithm is given to solve the model. Finally, the feasibility and effectiveness of the multi-core and cross-chain evaluation method are verified by example and simulation. The method studied in this article provides a scientific and feasible decision support scheme to solve the problem of large-scale business collaboration evaluation for across-industry chains in wide area cloud manufacturing environment.