As network architectures become increasingly dynamic and complex, traditional static defense mechanisms—relying on predefined rules—are proving inadequate in coping with evolving and unknown security threats. Inspired by the regulatory logic of the human nervous system, particularly its feed-forward and feedback mechanisms, conditioned reflexes, and self-adaptive learning, this paper proposes a neural-inspired security control model that integrates predictive regulation with adaptive optimization. The model employs a multi-hypersphere modeling approach to characterize multiple normal operating states of the system, simulating the nervous system's ability to flexibly reset regulatory baselines under different conditions. This enables accurate anomaly detection and feed-forward signal generation. In the feedback loop, a reward mechanism based on potential functions guides the system in progressively refining its countermeasure strategies, enhancing adaptability to unknown attacks. Experiments were conducted on a simulated Spring Boot based Web service platform, covering two normal states and seven categories of abnormal states. All experimental data were collected from the actual system runtime. Results show that the model achieves 98.00% accuracy in anomaly detection and 98.29% correctness in feed-forward signal generation, with all abnormal scenarios successfully regulated within a limited number of steps. Furthermore, comparative analysis demonstrates that the proposed model significantly outperforms standard anomaly detection baselines, such as OC-SVM and Isolation Forest, in both detection accuracy and regulatory capability. These findings validate the model's capabilities in self-learning, generalization, and practical application in intelligent security control.

The wide spread of rumors inflicts damages on social media platforms. Detecting rumors has become an emerging problem concerning the public and government. A crucial problem for rumors detection on social media is the lack of reliably pre-annotated dataset to train classification models. To solve this problem, we propose an unsupervised model that detects rumors by measuring how well the tweets follow the normal patterns. However, the problem is challenging in how to automatically discover the normal patterns of tweets. To tackle the challenge, we first propose a novel tree variational autoencoder model that reconstructs the sentiment labels along the propagation tree of a factual tweet. Then we propose a cross-alignment method to align the multiple modalities, i.e., tree structure and propagation features, and output the final prediction results. We conduct extensive experiments on a real-world dataset collected from Weibo. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised methods and adapts better to the concept drift than state-of-the-art supervised methods.

Trust chain, which focuses on the security in trusted computing platform, is the key technology to ensure system security. Aiming to establish the trust chain for mobile terminals, this paper proposes a trusted turing machine to formally describe the trust transitive process and construct an efficient trust chain model during the system boot time and the run time. The model consists of the following two characteristics. First, the boot code and operating system image are stored in Root of Trusted Storage. This structure provides more safety, reliability, and efficiency than that proposed by Trusted Computing Group. Second, a resource-oriented protecting scheme is designed during the system run time. A process can access specific resources on the condition that it has been granted trust property by the related verifying program. In addition, we also develop a prototype of trusted mobile terminal systems. Results show that the system boot time is shortened by 5.2s. In the meantime, the dynamic trusted mechanism executed during system run time can efficiently protect platform from malicious attack while it has little impact to system performance. The proposed model has the trust transitive property of the trust chain and can be applied to build a high efficiency trusted mobile terminal. Copyright © 2013 John Wiley & Sons, Ltd.