NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks
Journal
IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109/TKDE.2018.2818163
Journal Volume
30
Journal Issue
11
Pages
2171
Date Issued
2018-04-18
Author(s)
Vishal Sharma
Ravinder Kumar
Mohammed Atiquzzaman
Kathiravan Srinivasan
Albert Y. Zomaya
Abstract
Use of social network is the basic functionality of today's life. With the
advent of more and more online social media, the information available and its
utilization have come under the threat of several anomalies. Anomalies are the
major cause of online frauds which allow information access by unauthorized
users as well as information forging. One of the anomalies that act as a silent
attacker is the horizontal anomaly. These are the anomalies caused by a user
because of his/her variable behaviour towards different sources. Horizontal
anomalies are difficult to detect and hazardous for any network. In this paper,
a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery,
and removal of horizontal anomalies efficiently and accurately. The proposed
approach operates over the five paradigms, namely, missing links, reputation
gain, significant difference, trust properties, and trust score. The proposed
approach is evaluated with three datasets: DARPA'98 benchmark dataset,
synthetic dataset, and real-time traffic. Results show that the accuracy of the
proposed NHAD model for 10% to 30% anomalies in synthetic dataset ranges
between 98.08% and 99.88%. The evaluation over DARPA'98 dataset demonstrates
that the proposed approach is better than the existing solutions as it provides
99.97% detection rate for anomalous class. For real-time traffic, the proposed
NHAD model operates with an average accuracy of 99.42% at 99.90% detection
rate.
Subjects
Horizontal anomaly; social networks; reputation; neuro-fuzzy model; OUTLIER DETECTION; MODEL; Computer Science - Networking and Internet Architecture; Computer Science - Networking and Internet Architecture; cs.SI
Publisher
IEEE COMPUTER SOC
Description
14 Pages, 16 Figures, 5 Tables, Accepted in IEEE Transactions on
Knowledge and Data Engineering (2018)
Knowledge and Data Engineering (2018)
Type
journal article