Shilling attack detection in binary data: a classification approach

Saini

Wireless Communications and Mobile Computing

et al. 2022

Clustering, also known as unsupervised learning, is one of the most significant topics of machine learning because it divides data into groups based on similarity with the aim of extracting or summarizing new information. It is one of the most often used machine learning techniques. The most significant problem encountered in this subject is the sheer volume of electronic text documents accessible, which is increasing at an exponential rate, necessitating the development of efficient ways for dealing with these papers. Furthermore, it is not practicable to consolidate all of the papers from numerous locations into a single area for processing. In this study, the primary goal is to enhance the performance of the distributed document clustering approach for clustering big, high-dimensional distributed document datasets. For distributed storage and analysis, one of the most prominent open-source implementations of the big data analytic-based MapReduce model, such as the Hadoop framework, is used in conjunction with a distributed file system and is known as the Hadoop Distributed File System, to achieve the desired results. This necessitates an improvement in the approach of the clustering operation with Elephant Herding Optimization, which will be accomplished by applying a hybridized clustering procedure. In conjunction with the MapReduce framework, this hybridized strategy is able to solve the obstacles associated with the K -means clustering method, including the initial centroids difficulty and the dimensionality problem. In this paper, we analyze the performance of the whole distributed document clustering technique for big document datasets by using a distributed document clustering framework such as Hadoop and the associated MapReduce methodology. In the end, this decides how quickly computations may be completed.

Section: Multivariate Empirical Mode Decomposition-based Gradient Sup...mentioning

confidence: 99%

“…The base SVEC carry out the classification process through detecting the split with the maximal information gain. Batmaz et al [27] designed classification approach to find the shilling attack in the collaborative recommender system, but the precision of attack detection was not at required level.…”

Section: Gradient Support Vector Entropy Boosting Classifiermentioning

confidence: 99%

[Retracted] Big Data Analytics Model for Distributed Document Using Hybrid Optimization with K‐Means Clustering

Saini

Wireless Communications and Mobile Computing

et al. 2022

Security and Communication Networks

“…In the unsupervised detection methods, the user behaviour features are extracted and the clustering algorithms are used to group attackers together [15][16][17][18][19][20][21]. ere are also detection methods that model the dataset as a graph and use the graph mining methods to identify attackers [22][23][24][25].…”

Section: Related Workmentioning

confidence: 99%

A Shilling Group Detection Framework Based on Deep Learning Techniques

Wang

Zhang

Wang

et al. 2022

Research studies have shown that online recommender systems are under the threat of group shilling attacks, in which attackers attempt to distort the recommendation results of particular items by cooperatively injecting fake profiles. Existing detection methods usually divide candidate groups at first and then use the hand-crafted features to recognize shilling groups. However, the detection performance of existing methods depends highly on the quality of candidate groups obtained. Moreover, extracting features manually is time-consuming. To overcome these limitations, we propose a shilling group detection framework based on the sparse autoencoder and modified GraphSAGE model. First, we use the sparse autoencoder to obtain rating features of users from the rating dataset. Second, we analyse user collusive degrees to calculate user transition probabilities. Third, we build a user relation graph and utilize the modified GraphSAGE model to perform user classification. Finally, shilling groups are gathered according to the neighbour relations. Extensive experiment demonstrates that the proposed framework performs better on different datasets than baseline methods.

“…A support vector machine (SVM) classifier is trained in [31] using suspicious users/items and used additional trust measurement features to discriminate attack profiles. The authors in [33] used K-nearest neighbor (KNN) and SVM. Research work in [7] created separate clusters for items and user profiles to analyze items and discriminate attack users.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

Detecting Shilling Attacks Using Hybrid Deep Learning Models

Ebrahimian

Kashef

2020

Symmetry

Recommendation systems play a significant role in alleviating information overload in the digital world. They provide suggestions to users based on past symmetric activities or behaviors. Being heavily dependent on users’ behavior, they tend to be vulnerable to shilling attacks. Therefore, protecting them from attacks’ effects is highly important. As shilling attacks have features of a large number of ratings and increasing complexity in attack models, deep learning methods become proper alternatives for more accurate attack detections. This paper proposes a hybrid model of two different neural networks, convolutional and recurrent neural networks, to detect shilling attacks efficiently. The proposed deep learning model utilizes the transformed network architecture for undertaking the attributes derived from user-rated profiles. This architecture enables modeling of the temporal and spatial information in the recommendation system’s ratings. The hybrid model overcomes the limitations of the existing shilling attack deep-learning methods to enhance the recommendation systems’ efficiency and robustness. Experimental results show that the hybrid model results in better predictions on the Movie-Lens 100 K and Netflix datasets by accurately detecting most of the obfuscated attacks compared to the state-of-art deep learning algorithms used for investigation.