ShillDetector: a binary grey wolf optimization technique for detection of shilling profiles

Baliyan, Niyati

doi:10.1007/s12652-021-03320-8

Cited by 2 publications

(4 citation statements)

References 32 publications

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“…Reference 23 has proposed a shilling attack detection method against binary CF frameworks relying on binary ratings rather than numerical ones by utilizing newly introduced several model‐specific attributes in addition to existing ones. Finally, Reference 24 has introduced the ShillDetector attack detection method that takes advantage of high correlation shill profiles and removes correlated redundant features by utilizing swarm intelligence technique and grey wolf optimization.…”

Section: Related Workmentioning

confidence: 99%

“…It can be considered that applying an attribute A to the set S can divide the set into clusters (S 1 , S 2 , … , S m ). We calculate the expected information for all subsets using the formula given in Equation (24).…”

Section: Dataset and Evaluation Metricsmentioning

confidence: 99%

“…To prevent systems from such manipulations and thus achieve a more robust and fair system, many recent studies have aimed to develop practical attack profile detection procedures by using statistical approaches, 20,21 classification‐, 22‐24 or clustering‐based methods 25,26 . However, these detection approaches are typically designed for single criterion‐based RSs and cannot be directly applied to multi‐criteria environment due to their internal mechanism.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, such bias toward target items propagated by shilling attacks adversely affects the beyond-accuracy recommendation quality such as coverage and diversity 18 and jeopardizes the fair representation of the items, resulting in unfair competition in the market. 19 To prevent systems from such manipulations and thus achieve a more robust and fair system, many recent studies have aimed to develop practical attack profile detection procedures by using statistical approaches, 20,21 classification-, [22][23][24] or clustering-based methods. 25,26 However, these detection approaches are typically designed for single criterion-based RSs and cannot be directly applied to multi-criteria environment due to their internal mechanism.…”

Section: Introductionmentioning

confidence: 99%

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A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

Kaya

Yalçin

Kaleli

2023

Computational Intelligence

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Recommender systems are emerging techniques guiding individuals with provided referrals by considering their past rating behaviors. By collecting multi-criteria preferences concentrating on distinguishing perspectives of the items, a new extension of traditional recommenders, multi-criteria recommender systems reveal how much a user likes an item and why user likes it; thus, they can improve predictive accuracy. However, these systems might be more vulnerable to malicious attacks than traditional ones, as they expose multiple dimensions of user opinions on items. Attackers might try to inject fake profiles into these systems to skew the recommendation results in favor of some particular items or to bring the system into discredit. Although several methods exist to defend systems against such attacks for traditional recommenders, achieving robust systems by capturing shill profiles remains elusive for multi-criteria rating-based ones. Therefore, in this study, we first consider a prominent and novel attack type, that is, the power-item attack model, and introduce its four distinct variants adapted for multi-criteria data collections. Then, we propose a classification method detecting shill profiles based on various generic and model-based user attributes, most of which are new features usually related to item popularity and distribution of rating values. The experiments conducted on three benchmark datasets conclude that

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Section: Related Workmentioning

confidence: 99%

Section: Dataset and Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

Kaya

Yalçin

Kaleli

2023

Computational Intelligence

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Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

et al. 2021

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Highway tunnels are one of the paramount infrastructure systems that affect the welfare of communities. They are vulnerable to higher limits of deterioration, yet there are limited available funds for maintenance and rehabilitation. This state of circumstances entails the development of a deterioration model to forecast the performance condition behavior of critical tunnel elements. Accordingly, this research paper proposes an integrated deterioration prediction model for five highway tunnel elements, namely, cast-in-place tunnel liners, concrete interior walls, concrete portal, concrete ceiling slab, and concrete slab on grade. The developed deterioration model is envisioned in two fundamental components, which are model calibration and model assessment. In the first component, an integrated model of Gaussian process regression and a grey wolf optimization algorithm (GWO-GPR) is introduced for deterioration behavior prediction of highway tunnel elements. In this regard, the grey wolf optimizer is exploited to improve the prediction accuracies of the Gaussian process through optimal estimation of its hyper parameters and to automatically interpret the significant deterioration factors. The second component involves three tiers of performance evaluation comparison, statistical significance comparisons, and consolidated ranking to assess the prediction accuracies of the developed GWO-GPR model. In this regard, the developed model is validated against six widely acknowledged machine learning models, which are back-propagation artificial neural network, Elman neural network, cascade forward neural network, generalized regression neural network, support vector machines, and regression tree. Results demonstrate that the developed GWO-GPR model significantly outperformed other deterioration prediction models in the five tunnel elements. In cast-in-place tunnel liners it accomplished a mean absolute percentage error, mean absolute error, root mean square percentage error, root relative squared error, and relative absolute error of 1.65%, 0.018, 0.21%, 0.018, and 0.147, respectively. In this context, it was inferred that the developed GWO-GPR model managed to reduce the prediction errors of the back-propagation artificial neural network, Elman neural network, and support vector machines by 84.71%, 76.91%, and 69.6%, respectively. It can be concluded that the developed deterioration model can assist transportation agencies in creating timely and cost-efficient maintenance schedules of highway tunnels.

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ShillDetector: a binary grey wolf optimization technique for detection of shilling profiles

Cited by 2 publications

References 32 publications

A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

A novel classification‐based shilling attack detection approach for multi‐criteria recommender systems

Hybrid Grey Wolf Optimization-Based Gaussian Process Regression Model for Simulating Deterioration Behavior of Highway Tunnel Components

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