Detecting malicious users in the social networks using machine learning approach

H. L. Gururaj, U. Tanuja, V. Janhavi, B. Ramesh
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Abstract

Social networking plays a very important role in today's life. It helps to share ideas, information, multimedia messages and also provides the means of communication between the users. The popular social medias such as Facebook, Twitter, Instagram, etc., where the billions of data are being created in huge volume. Every user has their right to use any social media and a large number of users allowed malicious users by providing private or sensitive information, which results in security threats. In this research, they are proposing an natural language processing (NLP) technique to find suspicious users based on the daily conversations between the users. They demonstrated the behaviour of each user through their anomaly activities. Another machine learning technique called support vector machine (SVM) classifiers to detect the toxic comments in the comments blog. In this paper, the preliminary work concentrates on detecting the malicious user through the anomaly activities, behaviour profiles, messages and comment section.
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利用机器学习方法检测社交网络中的恶意用户
社交网络在当今生活中扮演着非常重要的角色。它有助于分享想法、信息、多媒体消息,并提供用户之间的通信手段。流行的社交媒体,如Facebook, Twitter, Instagram等,其中数十亿的数据正在大量创建。每个用户都有使用任何社交媒体的权利,大量用户允许恶意用户提供私人或敏感信息,从而导致安全威胁。在这项研究中,他们提出了一种自然语言处理(NLP)技术,可以根据用户之间的日常对话来发现可疑用户。他们通过异常活动展示了每个用户的行为。另一种机器学习技术称为支持向量机(SVM)分类器,用于检测评论博客中的有毒评论。本文的前期工作主要集中在通过异常活动、行为配置文件、消息和评论部分检测恶意用户。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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