The Performance of Location Aware Shilling Attacks in Web Service Recommendation

2017 ◽  
Vol 14 (3) ◽  
pp. 53-66 ◽  
Author(s):  
Min Gao ◽  
Xiang Li ◽  
Wenge Rong ◽  
Junhao Wen ◽  
Qingyu Xiong
Keyword(s):  
Web Service ◽  
Service Recommendation ◽  
Location Factors ◽  
Attack Model ◽  
Location Aware ◽  
Random Models ◽  
Successful Technique ◽  
The Impact ◽  
Attack Models

The location aware collaborative filtering (LACF) is one of the most successful technique of predicting the Quality of Service (QoS) in Internet of Things (IoT) service recommendation systems. However, the openness of CF web service recommendation renders them vulnerable to the injection of attack profiles consisting of apocryphal QoS values (also identified as shilling attacks). Combined with location factors, such profiles might exert greater impact on the LACF compared with traditional CF method. Unfortunately, to the best of the authors' knowledge, there is few research on such kind of attack model in the literature. Therefore, in this paper, the authors first construct three kinds of attack models including LAA, LAB, and LAR (location aware - average, bandwagon, and random) models and compare the impact of the classical shilling attacks (CSA) and location aware shilling attacks (LASA) on LACF. Furthermore, the authors use two attack detectors to compare the robustness of CSA and LASA. The experimental results on WS-DREAM dataset indicate that the LACF indeed suffers from CSA and LASA. Besides, in comparison with CSA, the LASA models do not always exert more influence on the LACF and the profiles injected by LASA are easier to be detected.

Location-Based Collaborative Filtering for Web Service Recommendation

2019 ◽  
Vol 12 (1) ◽  
pp. 34-40
Author(s):  
Mareeswari Venkatachalaappaswamy ◽  
Vijayan Ramaraj ◽  
Saranya Ravichandran
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Information Filtering ◽  
Service Selection ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Service Recommendation ◽  
Location Aware

Background: In many modern applications, information filtering is now used that exposes users to a collection of data. In such systems, the users are provided with recommended items’ list they might prefer or predict the rate that they might prefer for the items. So that, the users might be select the items that are preferred in that list. Objective: In web service recommendation based on Quality of Service (QoS), predicting QoS value will greatly help people to select the appropriate web service and discover new services. Methods: The effective method or technique for this would be Collaborative Filtering (CF). CF will greatly help in service selection and web service recommendation. It is the more general way of information filtering among the large data sets. In the narrower sense, it is the method of making predictions about a user’s interest by collecting taste information from many users. Results: It is easy to build and also much more effective for recommendations by predicting missing QoS values for the users. It also addresses the scalability problem since the recommendations are based on like-minded users using PCC or in clusters using KNN rather than in large data sources. Conclusion: In this paper, location-aware collaborative filtering is used to recommend the services. The proposed system compares the prediction outcomes and execution time with existing algorithms.

QoS Prediction for Web Service Recommendation with Network Location-Aware Neighbor Selection

2016 ◽  
Vol 26 (04) ◽  
pp. 611-632 ◽  
Author(s):  
Yuyu Yin ◽  
Song Aihua ◽  
Gao Min ◽  
Xu Yueshen ◽  
Wang Shuoping
Keyword(s):  
Web Service ◽  
Real World ◽  
Missing Values ◽  
Prediction Models ◽  
Service Recommendation ◽  
Qos Prediction ◽  
Network Location ◽  
Collaborative Recommendation ◽  
Location Aware ◽  
Neighbor Selection

Web service recommendation is one of the key problems in service computing, especially in the case of a large number of service candidates. The QoS (quality of service) values are usually leveraged to recommend services that best satisfy a user’s demand. There are many existing methods using collaborative filtering (CF) to predict QoS missing values, but very limited works can leverage the network location information in the user side and service side. In real-world service invocation scenario, the network location of a user or a service makes great impact on QoS. In this paper, we propose a novel collaborative recommendation framework containing three novel prediction models, which are based on two techniques, i.e. matrix factorization (MF) and network location-aware neighbor selection. We first propose two individual models that have the capability of using the user and service information, respectively. Then we propose a unified model that combines the results of the two individual models. We conduct sufficient experiments on a real-world dataset. The experimental results demonstrate that our models achieve higher prediction accuracy than baseline models, and are not sensitive to the parameters.

Location-aware and personalized collaborative filtering for web service recommendation

2016 ◽  
Vol 9 (5) ◽  
pp. 686-699 ◽  
Author(s):  
Jianxun Liu ◽  
Mingdong Tang ◽  
Zibin Zheng ◽  
Xiaoqing Liu ◽  
Saixia Lyu
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Service Recommendation ◽  
Location Aware

scholarly journals A model of the process of package delivering over a data transmission channel in the context of computer attacks by an intruder

2021 ◽  
Vol 2021 (2) ◽  
pp. 229-241
Author(s):  
Vera L. LUKICHEVA ◽  
◽  
Andrey A. PRIVALOV ◽  
Daniil D. TITOV ◽  
◽  
...  
Keyword(s):  
Data Transmission ◽  
Random Variables ◽  
Distribution Functions ◽  
Quality Of Data ◽  
Attack Model ◽  
Transmission Channels ◽  
Performance Quality ◽  
Computer Attacks ◽  
The Impact

Objective: To analyze the impact of computer attacks on the performance quality of data transmission channels and channeling systems. It is also necessary to take into account the capabilities of an intruder to introduce malware into channeling systems when committing a computer attack. Methods: To determine the required design ratios, several options for setting various distribution functions characterizing the parameters used as input data and types of inbound streams have been considered, taking into account the parameters of the intruder’s computer attack model set by the values of the probability of successful attack. Mathematical modeling is carried out using the method of topological transformation of stochastic networks. The exponential, momentum and gamma distributions are considered as distribution functions of random variables. The solutions are presented for inbound streams corresponding to the Poisson, Weibull, and Pareto models. Results: The proposed approach makes it possible to assess the performance quality of data transmission channels in the context of computer attacks. These assessments make it possible to analyze the state and develop guidelines for improving the performance quality of communication channels against the destructive information impact of the intruder. Various variants of the functions of random variables distribution and various types of the inbound stream were used for modeling, making it possible to compare them, as well as to assess the possibility of using them in channels that provide users with different services. Practical importance: The modeling results can be used to build communication management decision support systems, as well as to detect attempts of unauthorized access to the telecommunications resource of transportation management systems. The proposed approach can be applied in the development of threat models to describe the capabilities of the intruder (the ‘Intruder Model’).

scholarly journals An effective method for clustering-based web service recommendation

2022 ◽  
Vol 12 (2) ◽  
pp. 1571
Author(s):  
Ha Huy Cuong Nguyen ◽  
Bui Thanh Khiet ◽  
Van Loi Nguyen ◽  
Thanh Thuy Nguyen
Keyword(s):  
Web Services ◽  
Web Service ◽  
Performance Metrics ◽  
Research Problem ◽  
Quality Of Services ◽  
The Novel ◽  
Service Recommendation ◽  
Functional Relevance ◽  
Web Service Classification

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets.

scholarly journals Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

CONVERTER ◽  
2021 ◽  
pp. 583-589
Author(s):  
Li Ziman
Keyword(s):  
Web Services ◽  
Web Service ◽  
Autonomous System ◽  
Recommendation System ◽  
Service Recommendation ◽  
Filtering Method ◽  
Location Aware ◽  
The Face ◽  
Location Clustering ◽  
User Location

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

scholarly journals Qos Web Service Design Based on Collaborative Filtering

CONVERTER ◽  
2021 ◽  
pp. 107-115
Author(s):  
Yu-ping LI, Ke LI, Zhan-jie Guo
Keyword(s):  
Collaborative Filtering ◽  
Web Service ◽  
Prediction Accuracy ◽  
Recommendation System ◽  
Service Recommendation ◽  
Recommendation Algorithm ◽  
Similar Weight ◽  
Definition Of ◽  
Collaborative Prediction

In the process of web service recommendation, the prediction accuracy of Web Service missing Quality of Service (QoS) value will have an important impact on the rationality of service recommendation. Therefore, combined with spatiotemporal similarity perception, this paper proposes a new web service QoS collaborative filtering recommendation algorithm. This paper designs the framework of web service recommendation system from the perspective of QoS collaborative prediction, and gives the definition of related parameter set. Aiming at the problem that some services in the traditional Top-k algorithm are not similar to the target services, the spatial-temporal similarity perception combined with similar weight is used to predict the missing data to improve the prediction accuracy. In this paper, the calculation process of the algorithm is given through a simple example. The effectiveness of the algorithm is verified by the experimental results.

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