Exploring machine learning techniques for fake profile detection in online social networks

The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results

Trust-Based Rating Prediction and Malicious Profile Detection in Online Social Recommender Systems

Online social networks and recommender systems have become an effective channel for influencing millions of users by facilitating exchange and spread of information. This dissertation addresses multiple challenges that are faced by online social recommender systems such as: i) finding the extent of information spread; ii) predicting the rating of a product; and iii) detecting malicious profiles. Most of the research in this area do not capture the social interactions and rely on empirical or statistical approaches without considering the temporal aspects. We capture the temporal spread of information using a probabilistic model and use non-linear differential equations to model the diffusion process. To predict the rating of a product, we propose a social trust model and use the matrix factorization method to estimate user\u27s taste by incorporating user-item rating matrix. The effect of tastes of friends of a user is captured using a trust model which is based on similarities between users and their centralities. Similarity is modeled using Vector Space Similarity and Pearson Correlation Coefficient algorithms, whereas degree, eigen-vector, Katz, and PageRank are used to model centrality. As rating of a product has tremendous influence on its saleability, social recommender systems are vulnerable to profile injection attacks that affect user\u27s opinion towards favorable or unfavorable recommendations for a product. We propose a classification approach for detecting attackers based on attributes that provide the likelihood of a user profile of that of an attacker. To evaluate the performance, we inject push and nuke attacks, and use precision and recall to identify the attackers. All proposed models have been validated using datasets from Facebook, Epinions, and Digg. Results exhibit that the proposed models are able to better predict the information spread, rating of a product, and identify malicious user profiles with high accuracy and low false positives

Behavior analysis in social networks: Challenges, technologies, and trends

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Blockchain-based recommender systems: Applications, challenges and future opportunities

Recommender systems have been widely used in different application domains including energy-preservation, e-commerce, healthcare, social media, etc. Such applications require the analysis and mining of massive amounts of various types of user data, including demographics, preferences, social interactions, etc. in order to develop accurate and precise recommender systems. Such datasets often include sensitive information, yet most recommender systems are focusing on the models' accuracy and ignore issues related to security and the users' privacy. Despite the efforts to overcome these problems using different risk reduction techniques, none of them has been completely successful in ensuring cryptographic security and protection of the users' private information. To bridge this gap, the blockchain technology is presented as a promising strategy to promote security and privacy preservation in recommender systems, not only because of its security and privacy salient features, but also due to its resilience, adaptability, fault tolerance and trust characteristics. This paper presents a holistic review of blockchain-based recommender systems covering challenges, open issues and solutions. Accordingly, a well-designed taxonomy is introduced to describe the security and privacy challenges, overview existing frameworks and discuss their applications and benefits when using blockchain before indicating opportunities for future research. 2021 Elsevier Inc.This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Evaluating collaborative filtering over time

Recommender systems have become essential tools for users to navigate the plethora of content in the online world. Collaborative filtering—a broad term referring to the use of a variety, or combination, of machine learning algorithms operating on user ratings—lies at the heart of recommender systems’ success. These algorithms have been traditionally studied from the point of view of how well they can predict users’ ratings and how precisely they rank content; state of the art approaches are continuously improved in these respects. However, a rift has grown between how filtering algorithms are investigated and how they will operate when deployed in real systems. Deployed systems will continuously be queried for personalised recommendations; in practice, this implies that system administrators will iteratively retrain their algorithms in order to include the latest ratings. Collaborative filtering research does not take this into account: algorithms are improved and compared to each other from a static viewpoint, while they will be ultimately deployed in a dynamic setting. Given this scenario, two new problems emerge: current filtering algorithms are neither (a) designed nor (b) evaluated as algorithms that must account for time. This thesis addresses the divergence between research and practice by examining how collaborative filtering algorithms behave over time. Our contributions include: 1. A fine grained analysis of temporal changes in rating data and user/item similarity graphs that clearly demonstrates how recommender system data is dynamic and constantly changing. 2. A novel methodology and time-based metrics for evaluating collaborative filtering over time, both in terms of accuracy and the diversity of top-N recommendations. 3. A set of hybrid algorithms that improve collaborative filtering in a range of different scenarios. These include temporal-switching algorithms that aim to promote either accuracy or diversity; parameter update methods to improve temporal accuracy; and re-ranking a subset of users’ recommendations in order to increase diversity. 4. A set of temporal monitors that secure collaborative filtering from a wide range of different temporal attacks by flagging anomalous rating patterns. We have implemented and extensively evaluated the above using large-scale sets of user ratings; we further discuss how this novel methodology provides insight into dimensions of recommender systems that were previously unexplored. We conclude that investigating collaborative filtering from a temporal perspective is not only more suitable to the context in which recommender systems are deployed, but also opens a number of future research opportunities

PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

Customers’ loyalty model in the design of e-commerce recommender systems

Recommender systems have been adopted in most modern online platforms to guide users in finding more suitable items that match their interests. Previous studies showed that recommender systems impact the buying behavior of e-commerce customers. However, service providers are more concerned about the continuing behavior of their customers, specifically customers’ loyalty, which is an important factor to increase service providers’ share of wallet. Therefore, this study aimed to investigate the customers’ loyalty factors in online shopping towards e-commerce recommender systems. To address the research objectives, a new research model was proposed based on the Cognition-Affect-Behavior model. To validate the research model, a quantitative methodology was utilized to gather the relevant data. Using a survey method, a total of 310 responses were gathered to examine the impacts of the identified factors on customers’ loyalty towards Amazon’s recommender system. Data was analysed using Partial Least Square Structural Equation Modelling. The results of the analysis indicated that Usability (P=0.467, t=5.139, p<0.001), Service Interaction (P=0.304, t=4.42, p<0.001), Website Quality (P=0.625, t=15.304, p<0.001), Accuracy (P=0.397, t=6.144, p<0.001), Novelty (P=0.289, t=4.406, p<0.001), Diversity (P=0.142, t=2.503, p<0.001), Recommendation Quality (P=0.423, t=7.719, p<0.001), Explanation (P=0.629, t=15.408, p<0.001), Transparency (P=0.279, t=5.859, p<0.001), Satisfaction (P=0.152, t=3.045, p<0.001) and Trust (P=0.706, t=14.14, p<0.001) have significant impacts on customers’ loyalty towards the recommender systems in online shopping. Information quality, however, did not affect the quality of the website that hosted the recommender system. The findings demonstrated that accuracy-oriented measures were insufficient in understanding customer behavior, and other quality factors, such as diversity, novelty, and transparency could improve customers’ loyalty towards recommender systems. The outcomes of the study indicated the significant impact of the website quality on customers’ loyalty. The developed model would be practical in helping the service providers in understanding the impacts of the identified factors in the proposed customers’ loyalty model. The outcomes of the study could also be used in the design of recommender systems and the deployed algorithm