Shilling attacks pose a significant threat to the integrity and reliability of recommender systems by injecting fake user profiles to promote or demote targeted items. Existing generative approaches often suffer from unstable training dynamics and limited realism in the synthesized profiles. In this paper, we propose PGAN, a novel Penalized Generative Adversarial Network enhanced with latent space perturbations to generate high-quality, diverse, and undetectable shilling attack profiles. PGAN incorporates a gradient penalty to stabilize discriminator training and applies controlled noise perturbations in the generator's latent space to improve robustness and attack diversity. We evaluate PGAN on real-world datasets and demonstrate that it consistently outperforms traditional statistical attacks and baseline GAN-based models across multiple evaluation metrics, including Hit Ratio@K, Prediction Shift, and attack success rate. Experimental results also confirm the realism of the generated profiles through similarity analysis with genuine users. Our proposed model outperforms traditional and state-of-the-art methods, achieving HR@10 scores of 0.2051 and 0.2076 on the MovieLens and Amazon datasets, respectively.

Recommender systems (RS) are integral to modern e-commerce and content platforms. Yet, their reliance on user-item interaction data makes them vulnerable to shilling attacks, where fake data is injected to manipulate recommendations. Traditional shilling attack strategies utilize basic statistical properties of user-item data to create deceptive profiles. Still, recent advancements have shifted towards model-based attacks leveraging machine learning to enhance effectiveness and evade detection. This paper introduces GraphAush, a novel neural shilling attack model that employs Generative Adversarial Networks (GANs) with a novel generator shilling loss tailored to manipulate the user-item interaction graph. By incorporating a novel generator shilling loss function that leverages Node2Vec embeddings, GraphAush optimizes fake profile generation to maximize the effectiveness of attacks while minimizing detectability. This method overcomes the limitations of previous models, which either require intricate knowledge of the target RS or use indirect graph-based approaches. The efficacy of GraphAush was validated through experiments with multiple benchmark datasets, revealing its strong performance against traditional heuristic and other GAN-based attack methods. This innovative approach highlights a significant advancement in adversarial techniques for RS and sets a new benchmark for evaluating shilling attack strategies.

This research paper presents a comprehensive study on applying deep learning models for optimizing power balance management in smart grids. Utilizing a synthetic dataset based on a 4-node star network, this paper explores the efficacy of a deep learning model augmented to 60,000 observations, reflecting permutations of consumer nodes. The model incorporates advanced data preprocessing techniques, including feature selection, normalization, and outlier removal, alongside sophisticated machine learning strategies like Bayesian Optimization for hyperparameter tuning. The core of the research lies in evaluating the model's performance and resilience. The model was rigorously tested against various data loss scenarios using different imputation methods and assessed through metrics like accuracy, BCE Loss, and MSE Loss. These evaluations provided insights into the model's robustness and adaptability in simulating grid stability and resilience conditions.

In the realm of social network analysis and mining, recommendation systems have become indispensable algorithms in assisting users and industries in navigating the available contents or products in various domains and getting the most personalized recommendations to their interests and preferences. However, if the input data has been generated by malicious users, that poses a significant challenge to recommender systems' reliability and efficiency. One of the main threats that poses a challenge to recommender systems is shilling attacks. Shilling attacks tend to manipulate or poison the data in the systems' training phase, leading to biased or compromised recommendations. To address this challenge, we propose a robust recommender system using variational autoencoders (VAE) with Anomaly detection. Our model learns complex and non-linear patterns by exclusively focusing on the user-item interaction data, represented by a binary user-item interaction matrix, making it more resilient to classic shilling attacks. Moreover, our paper incorporates an anomaly detection mechanism, alongside the autoencoder, that analyzes the reconstruction errors, i.e. (MSE) between the original interactions and their reconstructed ones. We test the model on a real-world dataset and evaluate it using Recall@k and NDCG@k. This work enhances the trustworthiness and accuracy of recommendation algorithms, mainly when deployed in social network analysis and mining, where the potential for malicious data manipulation is a critical concern.