Cyber propaganda has become an increasingly sophisticated tool for manipulating public perception and discourse within online social networks (OSNs). The effectiveness of cyber propaganda is strongly influenced by the interplay between individual awareness and the underlying topology of OSNs that facilitates the spread of propaganda. However, existing interventions primarily focus on continuous control strategies, which may not be feasible in certain real-world scenarios. Therefore, effectively suppressing the spread of cyber propaganda while taking into account the above impact factors remains a challenging problem. In this study, we propose a methodology that combines the optimal impulse control (OIC) theory with a novel propagation model to address this problem. Our propagation model is the first to take into account the effects of the cognitive differences and interconnectivity of OSNs on the dynamics of cyber propaganda. By employing the OIC framework and our newly developed propagation model, we formulate an OIC problem. The goal is to find impulse strategies that optimally balance the cost of intervention against its effectiveness. Using the impulse maximum principle, we establish the necessary conditions for optimal impulse strategies and construct an algorithm to solve the OIC problem. Our numerical experiments, conducted on three distinct social networks, demonstrated that: 1) awareness levels play a crucial role in effectively suppressing the spread of cyber propaganda on OSNs; and 2) our impulse strategies are significantly superior to random strategies in terms of suppression effect, thereby evidencing their cost-effectiveness.

Social engineering malware, which exploits both technical and human vulnerabilities, presents challenging for individuals and organizations. However, existing studies typically focus on either technical or human vulnerabilities through case studies or questionnaires, ignoring their combined importance in mitigating such threats. This study pioneers the introduction of a mathematical model to analyze and mitigate the dynamics associated with these combined vulnerabilities. To achieve this, this study proposes an innovative framework, which integrates (a) acoupled malware-opinion dynamics modelto capture the interplay between both types of vulnerabilities, and (b) anoptimal impulse control approachto strategically mitigatingsocial engineering malware. Within this framework, we define an optimization problem, aimed at balancing control costs and malware severity. We derive theoretical conditions for optimal impulse strategies that achieve this balance and develop an iterative algorithm, the convergence and scalability of which have been empirically validated. Experimental results on three real-world social networks and synthetic scale-free networks demonstrate that our strategies consistently achieve an optimal balance by minimizing total expenses, including control costs and losses associated with malware. This finding underscores the effectiveness of routine patching and ongoing security awareness training in standard cybersecurity practices. Further experiments indicate that the strategic, early, and frequent deployment of patches in specific scenarios can effectively reduce unnecessary losses, enhancing overall cybersecurity resilience.

Cyber propaganda wars significantly impact users on Online Social Networks (OSNs), potentially altering their psychological/ideological attitudes and behaviors. Understanding these behavioral dynamics necessitates models that can effectively capture the propagation of dual competitive information, encompassing both propaganda and counter-propaganda campaigns by both conflicting parties. However, current models do not adequately account for competitive information spreading in dual setting and it lacks efficient strategies for managing both propaganda and counter-propaganda investments. To bridge these gaps, our study presents an innovative netwORked dIfferENTial gAme wiTh hybrId cONtrol (ORIENTATION) framework that integrates differential game with 1) a degree-based network model characterizing the spreading dynamics of dual competitive information for both parties; and 2) a dual hybrid control mechanism consisting of investment rates by continuous-time propaganda and discrete-time counter-propaganda. Using this framework, we formulate the Hybrid-contrOlled Differential GamE (HODGE) problem. We theoretically derive the necessary conditions for Nash equilibrium, and develop an iterative algorithm, termed theHODGEalgorithm, to numerically approximate the Nash equilibrium. Our experiments, performed on different groups of OSNs, reveal that the resulting strategy profiles consistently outperform several alternative profiles in terms of cost-effectiveness. Scalability assessment for theHODGEalgorithm is then carried out on OSNs with different scales, demonstrating its strong performance in terms of computational efficiency, scalability and practicability. Additional experimental results suggest that a decrease in the lower bounds of the investment rates in both propaganda and counter-propaganda campaigns and an early implementation of counter-propaganda strategies can significantly enhance cost-effectiveness, offering strategic insights for those engaged in cyber propaganda war.

In this paper, we propose an identity-preserving face hallucination (IPFH) method via deep reinforcement learning. Most existing methods ultra-resolve facial visual information in guidance of appearance similarity which rarely attend to recovering the semantic property, undermining further face analysis (e.g., recognition). We present a visual-semantic hallucinator relying on deep reinforcement learning to adaptively repair local details for the restoration of both identity and appearance characteristics. Specifically, we first capture the facial global topology structure to roughly recover the visual information with the pixel-wise similarity constraint. To super-resolve more photo-realistic faces, we explore the contextual interdependency to reconstruct facial local textural details (e.g., over-smoothed edges) with the constraints of visual and identity similarity. In terms of the visual similarity constraint, we develop the dual domain network with bidirectional consistency on both HR domain and LR domain to improve the appearance quality. Moreover, we introduce the identity constraint to encourage hallucinated faces to satisfy the identity property. Experimental results on several benchmarks demonstrate our method achieves promising performance on the recovery of visual and semantic information.