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

TL;DR

This paper introduces a neuro-fuzzy based method called NHAD for detecting, recovering from, and removing horizontal anomalies in online social networks, achieving high accuracy across multiple datasets.

Contribution

It presents a novel self-healing neuro-fuzzy approach that effectively detects and handles horizontal anomalies in social networks, outperforming existing solutions.

Findings

Achieves up to 99.88% accuracy on synthetic datasets.

Detects 99.97% of anomalies in DARPA'98 dataset.

Operates with 99.42% accuracy on real-time traffic.

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.