In the era of Internet of Things (IoT), millions of heterogeneous IoT devices generate an explosion of data and services waiting to be discovered. The convergence of IoT with social networks (SIoT) interconnects multiple IoT applications and alleviates the common data sparsity and cold start problems in traditional recommendation systems. However, the social trust relationships may also be very sparse, which affects the accuracy of trust-based recommendation systems. Meanwhile, mobile devices have limited resources and are more vulnerable to malicious attacks in the IoT environment. In order to complete the trust relationship and further improve the trust-based recommendation performance, we propose a reliable and lightweight trust inference model for service recommendation in SIoT, called TIRec . First, we obtain a comprehensive weighted centrality metric (LGWC) considering both local and global contexts. Based on this, we propose a corresponding lightweight trust path selection algorithm. Then, we present a reliable trust inference calculation algorithm consist of trust propagation and aggregation strategy, which can efficiently resist two common malicious attacks. Finally, we incorporate the rating, direct trust, and indirect trust together into the matrix factorization model, and integrate the influence of truster and trustee to obtain the synthetic model for rating predication. To the best of our knowledge, this article is the first to integrate trust inference algorithm into the trust-based recommendation systems. The extensive experiments are conducted on three real-world data sets, and the results show that our TIRec model performs better than other advanced recommendation models in both “all users” view and “cold start users” view.

Insider threats represent a significant challenge in both corporate and governmental sectors. Most existing supervised learning based detection methods that rely on transforming user behavior into sequential data do not fully utilize structural information and require extensive labeled data. This reliance poses a challenge due to the scarcity of labeled data in real-world scenarios, leading to a few-shot learning situation. To address these limitations, we propose a novel Graph modularized-based Meta-learning Framework for Insider Threat Detection, named GMFITD. Specifically, GMFITD utilizes a structural reconstruction mechanism that combines a graph-based autoencoder with an attention mechanism to explore structural information and infer potential relationships between users. Additionally, we employ a graph prototype construction method coupling episodic meta-learning principle (MAML) to compute representative embeddings for few-shot learning scenarios. By leveraging MAML, the proposed method can capture prior knowledge of insider threat classification by training on similar few-shot learning tasks with few labeled samples. We further enhance the resilience of GMFITD to adversarial attacks through an edge importance estimation mechanism, which assigns higher weights to relevant edges. Extensive experiments demonstrate that our proposed GMFITD outperforms state-of-the-art methods in insider threat detection, achieving higher accuracy with fewer labeled samples and resisting adversarial attacks.

Federated learning aims to complete model training without private data sharing, but many privacy risks remain. Recent studies have shown that federated learning is vulnerable to membership inference attacks. The weight as an important parameter in neural networks has been proven effective for membership inference attacks, but it leads to significant overhead. Facing this issue, in this paper, we propose a bias-based method for efficient membership inference attacks against federated learning. Different from the weight that determines the direction of the decision surface, the bias also plays an important role in determining the distance to move along the direction. Moreover, the number of bias is way less than the weight. We consider two types of attacks: local attack and global attack, corresponding to two possible types of insiders: participant and central aggregator. For the local attack, we design a neural network-based inference, which fully learns the vertical bias changes of the member data and non-member data. For the global attack, we design a difference comparison-based inference to determine the data source. Extensive experimental results on four public datasets show that the proposed method achieves state-of-the-art inference accuracy. Moreover, experiments prove the effectiveness of the proposed method to resist some commonly used defenses.

With the rapid proliferation of crowd mobility data produced by ubiquitous mobile devices equipped with spatial positioning modules, deep neural networks (DNNs) have become widely applied in spatio-temporal trajectory modeling. However, recent studies have shown that DNNs are vulnerable to adversarial examples with strong transferability, which are crafted by introducing small perturbations to original examples but can cause catastrophic mistakes. To mitigate this vulnerability and enhance model robustness, we propose a novel self-supervised collaborative learning framework named Auto-GAN that consists of a generator for automatically learning robust latent features and a discriminator for providing comprehensive guidance to the generator. By leveraging the collaboration between the generator and discriminator, our proposed method significantly improves the denoising performance. Moreover, we combine point-level and feature-level constraints into training processes between original example reconstruction and adversarial example denoising, thereby effectively suppressing the potential “error amplification effect". Extensive experiments conducted on two representative real-world mobility datasets show that our proposed method can significantly enhance the model’s robustness against various adversarial attacks, while preserving the model’s prediction accuracy on original examples.

Simulating human mobility realistically and generating large-scale, high-quality trajectories are crucial for various location-based applications such as traffic management, epidemic spreading analysis, and location privacy protection. While the most popular model-free methods succeed by directly learning distribution of real-world data, they struggle to produce high-quality mobility data without leveraging the domain knowledge of human mobility. Moreover, such model-free methods primarily rely on auto-regressive paradigms, usually accompanied by error accumulation problem. To address the issues, we propose a model-free Domain-Knowledge Enhanced Generative Adversarial Network (DKE-GAN), which efficiently combines domain knowledge of urban context with model-free learning paradigm to generate high-quality mobility data. In addition, we incorporate reinforcement learning into the training process, thereby effectively alleviating the error accumulation. Furthermore, we introduce Trajectory Representation Learning (TRL) to convert noise-carrying raw trajectories into low-dimensional representation vectors for fully mining human mobility patterns. Extensive experiments conducted on two representative real-world mobility datasets demonstrate that our proposed method outperforms six state-of-the-art baselines, significantly achieving performance improvements in simulating human mobility.