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

机译:NHAD:在线社交网络中基于神经模糊的水平异常检测

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摘要

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.
机译:社交网络的使用是当今生活的基本功能。随着越来越多的在线社交媒体的出现,可用信息及其使用受到了几种异常的威胁。异常是在线欺诈的主要原因,在线欺诈允许未经授权的用户访问信息以及伪造信息。充当沉默攻击者的异常之一是水平异常。这些是由于用户针对不同来源的可变行为而导致的异常。水平异常很难检测,并且对任何网络都是危险的。在本文中,自愈神经模糊方法(NHAD)用于有效,准确地检测,恢复和消除水平异常。所提出的方法在五个范式上运行,即缺少链接,声誉获得,显着差异,信任属性和信任分数。通过三种数据集对提出的方法进行了评估:DARPA'98基准数据集,合成数据集和实时流量。结果表明,对于合成数据集中10%到30%的异常,所提出的NHAD模型的准确性在98.08%到99.88%之间。对DARPA'98数据集的评估表明,该方法优于现有解决方案,因为它为异常类别提供了99.97%的检测率。对于实时流量,建议的NHAD模型以99.90%的检测率以99.42的平均精度运行。

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