In light of the escalating complexity of the cyber threat environment, the role of Collaborative Intrusion Detection Systems (CIDSs) in reinforcing contemporary cybersecurity defenses is becoming ever more critical. This paper presents a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF), an innovative methodology aimed at enhancing the efficacy of threat detection and information dissemination. To address the issue of alert collisions during data exchange, an Alternating Random Assignment Selection Mechanism (ARASM) is proposed. This mechanism aims to optimize the selection process of domain leader nodes, thereby partitioning traffic and reducing the size of conflict domains. Unlike conventional CIDS approaches that typically rely on independent node-level detection, our framework incorporates a Weighted Random Forest (WRF) ensemble learning algorithm, enabling collaborative detection among nodes and significantly boosting the system’s overall detection capability. The viability of the BCIDF framework has been rigorously assessed through extensive experimentation utilizing the NSL-KDD dataset. The empirical findings indicate that BCIDF outperforms traditional intrusion detection systems in terms of detection precision, offering a robust and highly effective solution within the realm of cybersecurity.

This study examined the relationship between cognitive adjustment and expatriates’ performance in the foreign host-country, and the moderating role of psychological capital in this relationship. The expatriates (N = 1 229; female = 32%; mean international tenure = 2.99, SD = 4.44) completed measures of cognitive adjustments (work and non-work related), psychological capital, job-performance, and organisational culture. Results from the data analysis, using the product least square method, indicate expatriates’ cognitive adjustments (work and non-work related) and psychological capital to singularly predict job performance. Psychological capital partly moderated the relationship between cognitive adjustment (work related) and expatriates’ performance.

Person re-identification (ReID) is one of the commonly used criminal investigation methods in reconnaissance. Although the current ReID has achieved robust results on single domains, the focus of researches has shifted to cross-domain in recent years, which is caused by domain bias between different datasets. Generative Adversarial Networks (GAN) is used to realize the image style transfer of different datasets to alleviate the effect of cross-domain. However, the existing GAN-based models ignore complete expressions and occlusion of pedestrian characteristics, resulting in low accuracy in feature extraction. To address these issues, we introduce a cross domain model based on feature fusion (FFGAN) to fuse global, local and semantic features to extract more delicate pedestrian features. Before extracting pedestrian features, we preprocess feature maps with a feature erasure block to solve an occlusion issue. Finally, FFGAN enables a more complete visual description of pedestrian characteristics, thereby improving the accuracy of FFGAN in identifying pedestrians. Experimental results show that the effect of FFGAN is significantly improved compared with some advanced cross-domain ReID algorithms.

Excel 4.0 Macro leverages XLM code to directly invoke system APIs and automate complex tasks, making it a widely used tool in phishing attacks, APT campaigns, and IoT intrusions in recent years. By constructing various obfuscated macro malware, attackers can easily evade firewalls and detection systems, thereby achieving persistent attacks. However, existing XLM malware defense mechanisms lack in-depth analysis of XLM malware families and behaviors, failing to integrate multi-dimensional features and semantic relationships effectively. As a result, detection systems struggle to accurately identify malicious operations in real-world attacks, leading to low robustness and accuracy. To this end, we propose XLM4Detector, a novel Excel 4.0 Macro malware detection framework based on multi-stage deobfuscation and multi-view semantic fusion. First, XLM4Detector integrates AST analysis, simulated execution, and regular expression matching to construct a multi-stage deobfuscation algorithm, enabling precise deobfuscation and XLM code extraction. Second, we introduce four feature extraction methods that capture fine-grained features at the word (string), token (function), abstract syntax tree, and semantic relationship levels. Then, we design four embedding representations (XlmWord2Vec, XlmToken2Vec, XlmAst2Vec, XlmRela2Vec) and employ a multi-view semantic fusion algorithm for feature alignment. Finally, we develop an MHSACNN-BiGRU model to capture hierarchical semantic relationships, effectively enabling XLM malware behavior detection and family classification. Experimental results demonstrate that XLM4Detector effectively reconstructs obfuscated XLM source code and accurately detects XLM malware families and behaviors. It outperforms state-of-the-art methods in detection accuracy, robustness, and generalization. Our framework provides critical technical support for IoT security defense, malicious document detection, and APT tracking.

Obtaining commercial value of private information from big data has become commonplace, which leads to misuse of information knowledge as well as violation of information owners’ rights, and curbing such behaviors has become a challenge. In this paper, we design an embedding scheme that can be applied to privacy protection of secret information, i.e., embedding confidential information such as copyright as secret information in cover images. The secret information is divided into multiple clusters, encrypted and compressed through the use of multiple-zone folding method to optimize the embedding efficiency and minimize the distortion caused by the embedding process, it realizes the privacy feature of traceability and security protection of secret information in circulation. Evaluated by security analysis and experimental results, this proposed scheme achieves IND-CPA high information security level for information protection. Compared with the state-of-the-art scheme, the computational complexity of this proposed scheme is O(Y) (Y denotes the total number of pixels), at least 6 bits of information can be embedded per pixel which improves the efficiency of embedding. In terms of the impact on the quality of cover image information after the embedding of secret information, it has better performance, and improves the manageable traceability of information.