Large language models (LLMs) have transformed recommender systems through strong semantic understanding and generalization. However, the design of item identifiers remains a critical bottleneck that directly affects recommendation quality. Traditional metadata-based identifiers introduce length variability and semantic ambiguity, whereas existing collaborative indexing (CID) approaches often neglect item attributes, show limited cross-dataset generalizability, and incur high computational cost at scale. To address these limitations, we propose a Graph Neural Network (GNN)–based item indexing framework with three coordinated innovations. First, we construct attribute-enriched co-occurrence graphs and use a GNN encoder to fuse item features with collaborative signals, yielding semantically informed representations that work well for attribute-rich catalogs. Second, we replace recursive spectral clustering with hierarchical agglomerative clustering on GNN embeddings, enabling direct control of index length via tree depth and reducing hyperparameter tuning across datasets. Third, we exploit localized message passing rather than global eigendecomposition, which provides considerably better runtime efficiency and is amenable to mini-batch training, supporting online index updates as interactions evolve. Across five benchmarks, GID achieves strong average ranking performance, showing larger improvements on sparse and attribute-rich datasets while remaining competitive in dense settings. The framework is robust under both seen and unseen prompt templates, which supports practical LLM-based recommendation. On sequential recommendation, GID improves HR@10 by 7.9% on average over the strongest baseline in each dataset.

One of the trends in the evolution of Android malware is the increasing diversity of malicious behaviors, such as SMSrelated and Internet-related actions. Traditional binary or familybased classification methods are inadequate for fine-grained detection of these behaviors. Thus, multi-label classification is required to identify various malicious behaviors within a single malware sample. This paper employs an active learning strategy to add multi-behavior labels to large-scale datasets based on expert-annotated small-scale datasets. To address the issue of noisy labels (simulating real-world mislabeling), we propose CalmDroid, an active learning framework utilizing the coreset strategy, instead of the confuse-set strategy for updating the model with out-of-distribution (OOD) points. We evaluate CalmDroid's performance using the Drebin and VirusShare datasets. Experimental results demonstrate that CalmDroid achieves superior detection performance under varying noise conditions, with an accuracy improvement of up to 0.704 compared to the confuse-set strategy. In high-noise environments (15%), it reaches detection accuracy as high as 0.944. Additionally, we validate CalmDroid's capability to detect evolving malware. Despite behavioral evolution in Drebin malware across different time steps, CalmDroid consistently achieves detection rates above 70 % in the newest time step.

Trajectory data analysis is crucial in various applications but presents significant privacy risks, as location data can reveal sensitive information. Existing privacy protection methods, such as spatiotemporal K-anonymity and L-diversity, are vulnerable to semantic inference attacks, where public data is exploited to re-identify users. To address these challenges, we propose dummy-trajectory synthesis (DTS), an efficient privacy protection scheme for location-based social networks (LBSNs). DTS enhances privacy by leveraging users’ frequent behavioral sequences to generate synthetic dummy trajectories. Unlike traditional approaches, DTS considers both geographic and semantic data by segmenting historical trajectories into time periods using the OPTICS clustering algorithm. This enables the identification of regions with specific semantic attributes and the mining of semantic trajectory sequences. DTS optimizes dummy trajectory generation by combining Euclidean distances and semantic similarity, ranking historical points and establishing transition relationships. Experimental results show that DTS significantly improves privacy protection and performance compared to existing methods, without compromising service quality. DTS offers a robust solution for protecting trajectory data privacy in LBSNs against transition probability attack for joint time periods.

In the evolving landscape of Software Defined Internet of Thing (SD-IoT), the proliferation of IoT devices and applications has led to a drastic expansion of network traffic. Because of the overwhelming influx of control messages, the SDN controller may not have sufficient capacity to adequately address them. The main challenge is to properly deploy multiple controllers to enhance resource utilization and boost network performance, taking into account different factors for varying network scenarios. This paper presents a Predictive and Elitism genetic algorithm with Non-cooperative Game (PENG) strategy, tailored to address the control plane load imbalance. PENG incorporates a Gated Recurrent Units (GRU) based traffic prediction model, an improved elitism genetic algorithm, and the non-cooperative game theory to synergistically optimize the load balancing strategy. The study formulates a multi-objective optimization model that takes into account the degree of load balancing, control plane latency, and migration expenses as utility functions. This paper is structured around pivotal modules such as traffic prediction, controller overload identification, switch migration, and controller-switch mapping matrix reconfiguration. Moreover, an improved elitism genetic reallocation algorithm (IEGR) is designed, featuring a novel similarity factor to expedite convergence and improve the accuracy of identifying the optimal solution. Further, the detailed algorithm of PENG is present, outlining proactive and predictive switch migration to preemptively address potential load imbalance. The proposed methodology is simulated and the experimental results demonstrate that the proposal outperforms the comparisons in optimizing the load balancing degree, reducing average latency and migration cost.

Cross-domain interaction in social networks and mobile applications is rapidly expanding. The demand for accessing data across multiple domains from different applications is growing. Establishing robust authorization and access control mechanisms within trusted domains has become a critical foundation for data security. Despite advancements in the field of identity authentication and cross-domain access, challenges persist in various application domain transition scenarios, including cumbersome and inefficient processes, and the potential for authority misuse by malicious actors in decentralized environments. To mitigate these limitations, we propose a blockchain-based scheme that leverages consensus mechanisms to enable “one-time authentication, multidomain authorization.” This scheme enhances security attributes and performance in several key aspects. First, we developed a primary–secondary chain model compatible with multiple trusted domains, where the primary chain records user authentication and authorization information, and the secondary chain logs domain-specific user identity registration information. Nodes within the primary and secondary chains reach a rapid consensus on authentication outcomes through an improved consensus algorithm. Building on this model, we devised a certificateless cross-domain identity authentication method, rendering the authentication and authorization processes more secure and efficient. Additionally, to address the issue of centralized user authority, an optimized chameleon hash function was designed to facilitate identity revocation within a multicentric environment. Furthermore, security analyses and simulation validations were conducted to assess the performance of the proposed scheme. Compared to existing approaches, our scheme demonstrates reduced computational and communication overhead, substantiating its efficacy in streamlining cross-domain interactions.

To solve the load imbalance problem of controllers in Software Defined Networks (SDN), we present a controller reselection mechanism based on non-cooperative game theory. Load balancing among controller clusters is achieved by dynamic migration of SDN switches. The load balance of controller cluster, the average latency and the switch migration cost were adopted as utility functions. To meet Nash equilibrium strategy, we propose a Genetic Algorithm for Improved Multi-objective Optimization (GAIMO). To prevent global optimal solution from slipping into a local optimum, a similarity operator is designed to speed up the convergence and to improve the accuracy of the proposed algorithm. Experimental results show that the mechanism effectively optimizes overall resource utilization of controllers, and reduces average network latency, communication overhead, as well as the cost of switch migration, and thus optimizes entire network performance.

Location-based social networks (LBSN) is a new type of heterogeneous information network (HIN). The check-in data usually has the characteristics of a large amount of data and high sparsity. It is a problem worth studying how to effectively discover its complex community structure and accurately recommend it to users. Most HIN-based recommendation methods rely on path-based similarity, which cannot fully mine latent structure features of LBSN users and items. This paper proposes a meta-path-aware common clustering recommendation method MPNMF (meta-path-aware non-negative matrix factorization), based on non-negative matrix tri-factorization. By establishing the objective function based on non-negative matrix tri-factorization and second-order meta-path method, LBSN users and points of interest are integrated with their multi-dimensional heterogeneous relationships. The interrelated user clusters and interest point clusters can be obtained, effectively alleviating the influence of data sparsity. To solve the initial value problem of the model, this method uses the spectral cluster method, which provides a good initial value for the construction of the prediction model. It improves the operational efficiency of the model and the precision of model recommendations. Experiments on the real LBSN datasets show that the proposed method has a high recommendation precision and recall.

Secure group communication for large wireless sensor networks sparkles the research on efficient key distribution and key management mechanism. By a self-healing key distribution scheme, even if during a certain session, some broadcast messages are lost due to network faults, the users are capable of recovering the lost session keys on their own without requesting additional information exchange with the group manager. However, some self-healing key distribution schemes are unable to prevent collusion attacks effectively. In this paper, a general self-healing session key distribution approach is devised to thwart collusion attacks. Furthermore, an effective collusion resilient key distribution scheme is proposed for the secure group communication in wireless sensor networks.

The research issues of large scale wireless mesh networks (WMNs) have attracted increasing attention due to the excellent properties of WMNs. Although some proposals for WMN security framework with different security aspects have been put forward recently, it is a challenging issue of employing uniform public key cryptography to maintain trust relationships flexibly among domains and to achieve key-escrow-free anonymous access control. In this paper, a unified security framework (USF) for multi-domain wireless mesh networks is proposed, which unifies id-based encryption and certificateless signature in a single public key cryptography context. Trust relationship between different domains and anonymous access control of wireless clients can be realized by employing of cryptography operations on bilinear groups. To achieve perfect forward secrecy and attack-resilience, trust domain construction methods and authentication protocols are devised within the security framework without key escrow.