A robust recommended system based on attack detection

Abstract

In the increasingly fierce competition in e-commerce sites, the recommendation system has brought great benefits to the site, but some unscrupulous businesses use the recommended system algorithm loopholes, the use of bulk injection of some fake users, and the ratings of these users with the normal user's rating. Therefore, when calculating user similarity, it is easy to enter the user ′ s neighborhood circle, because the false user takes a high score ("push attack") or a low score ("null attack") on the target project. The recommendation scores will be biased, and it is important to detect these false users for the recommendation system and maintain a good e-commerce competitive environment. In this paper, we propose a recommendation algorithm that divides user-generated ratings into normal ratings and non-generic ratings and refine and use state information to minimize the impact of spurious malicious users. Our algorithm first ensures that the recommender system is stable against the three main attack modes (random attack, average attack, power flow attack). Through the analysis of the real data, we verify the performance of the proposed scheme and compare our algorithm with the existing one. KEYWORDSattack model, collaborative filtering, nuclear attack, push attack, recommendation system INTRODUCTIONThe recommendation system is an intelligent software tool for providing advice to users and has been used in many fields such as e-commerce, movie and video websites, music network radio stations, social networks, as well as personalized reading, mailing and, advertising. 1-3In the increasingly fierce competition in e-commerce sites, the recommendation system has brought great benefits to the site, but some unscrupulous businesses use the recommended system algorithm loopholes, the use of bulk injection of some fake users and the ratings of these users with the normal user's rating. Similarly, the user's proximity circle is easily calculated when calculating the user's similarity level. Since the fake user takes a high score ("push attack") or a low score ("nuclear attack") on the target item because the normal user pair The target project's recommended rating will be biased to detect these fake users in terms of the recommendation system is very important to maintain a good competitive environment for e-commerce. If e-commerce sites are often malicious, fake users fake rating; the recommendation system is very difficult to recommend suitable items to the user.Motivatedly, in order to make their products sell well, manufacturers always want recommender systems to frequently recommend their own products while reducing or not recommending competitors' products. Instead of trying to improve the quality of their products, some bad producers do this by using cheating techniques to increase the frequency with which recommender systems recommend their products.The recommendation system is based on similar users or similar products to generate a list of recommendations, but it is also an open system that requires user par...

RETRACTED ARTICLE: Support attack detection algorithm for recommendation system based on deep learning

“…To solve this problem effectively, the recommendation system was born and has been widely used in practise [1,2]. Compared with traditional information search tools, this system can provide users with relevant and interesting information based on historical browsing data, which not only effectively improves the efficiency of information search but also presents the content that users are interested in, thus making many e-commerce enterprises profit [3]. Due to the strong participation of the recommendation system, the attacker will inject a large number of false user profiles into the system and never produce more favourable recommendation results.…”

Mitigating Human and Computer Opinion Fraud via Contrastive Learning

“…Another group of models for fraudulent profiles detection exploits diverse collection of statistical metrics for filtering the anomalies. Existing works propose such statistics as (1) deviation from the median value in a suggested recommendation set of products [14], (2) rating items with extreme rating and sentiment values [27] and (3) ratings falling within the tight time windows [28]. Nonetheless, the proposed statistics are easy to circumvent and the more advanced detection metrics should be applied.…”

“…The current study focuses on solving the task of filtering out the deceptive opinions and detecting anomalous behavior on a platform with text reviews. The emphasis on text reviews can be explained by the fact that texts are a more informative and a more reliable source of product's and seller's quality, than a star-rating system, which is easy to manipulate (see [19], [14], [27], [28]). The deceptive opinions can be generated in two ways: (1) by hiring the professional human writers to review the product for monetary gains, and (2) by exploitation of state-of-the-art (SOTA, here and after) automated language models to imitate the human speech, and this distinction is crucial, since the existing algorithms do not address this difference and focus merely on filtering out the artificial text reviews [22] and do not account for the problem of hiring the professional writers.…”

A secure and efficient WSN by employing symmetric key matrix and rectangular frame scheme

“…To defend against attacks, the ESSKM periodically changes K S . However, if one node is compromised, the attacker can use it as a zombie node to attack the system . Besides, several messages are transmitted to produce one K S , consuming some amount of energy among nodes.…”