NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection in Online Social Networks

Vishal Sharma, Ravinder Kumar, Wen-Huang Cheng, Mohammed Atiquzzaman, Kathiravan Srinivasan*, Albert Y. Zomaya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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 behavior 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 percent anomalies in synthetic dataset ranges between 98.08 and 99.88 percent. The evaluation over DARPA'98 dataset demonstrates that the proposed approach is better than the existing solutions as it provides 99.97 percent detection rate for anomalous class. For real-time traffic, the proposed NHAD model operates with an average accuracy of 99.42 at 99.90 percent detection rate.

Original languageEnglish
Article number8322278
Pages (from-to)2171-2184
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number11
StatePublished - 1 Nov 2018


  • Horizontal anomaly
  • neuro-fuzzy model
  • reputation
  • social networks


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