3 research outputs found
Detection and Filtering of Collaborative Malicious Users in Reputation System using Quality Repository Approach
Online reputation system is gaining popularity as it helps a user to be sure
about the quality of a product/service he wants to buy. Nonetheless online
reputation system is not immune from attack. Dealing with malicious ratings in
reputation systems has been recognized as an important but difficult task. This
problem is challenging when the number of true user's ratings is relatively
small and unfair ratings plays majority in rated values. In this paper, we have
proposed a new method to find malicious users in online reputation systems
using Quality Repository Approach (QRA). We mainly concentrated on anomaly
detection in both rating values and the malicious users. QRA is very efficient
to detect malicious user ratings and aggregate true ratings. The proposed
reputation system has been evaluated through simulations and it is concluded
that the QRA based system significantly reduces the impact of unfair ratings
and improve trust on reputation score with lower false positive as compared to
other method used for the purpose.Comment: 14 pages, 5 figures, 5 tables, submitted to ICACCI 2013, Mysore,
indi
Accumulative time-based ranking method to reputation evaluation in information networks
With the rapid development of modern technology, the Web has become an
important platform for users to make friends and acquire information. However,
since information on the Web is over-abundant, information filtering becomes a
key task for online users to obtain relevant suggestions. As most Websites can
be ranked according to users' rating and preferences, relevance to queries, and
recency, how to extract the most relevant item from the over-abundant
information is always a key topic for researchers in various fields. In this
paper, we adopt tools used to analyze complex networks to evaluate user
reputation and item quality. In our proposed accumulative time-based ranking
(ATR) algorithm, we incorporate two behavioral weighting factors which are
updated when users select or rate items, to reflect the evolution of user
reputation and item quality over time. We showed that our algorithm outperforms
state-of-the-art ranking algorithms in terms of precision and robustness on
empirical datasets from various online retailers and the citation datasets
among research publications
An overview on user profiling in online social networks
Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors