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首页> 外文期刊>IEEE transactions on dependable and secure computing >Towards Learning-Based, Content-Agnostic Detection of Social Bot Traffic
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Towards Learning-Based, Content-Agnostic Detection of Social Bot Traffic

机译：走向基于学习的，内容 - 社交机器人交通的不可知论报

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摘要

With the fast-growing popularity of online social networks (OSNs), the security and privacy of OSN ecosystems becomes essential for the public. Among threats OSNs face, malicious social bots have become the most common and detrimental. They are often employed to violate users' privacy, distribute spam, and disturb the financial market, posing a compelling need for effective social bot detection solutions. Unlike traditional social bot detection approaches that have strict requirements on data sources (e.g., private payload information, social relationships, or activity histories), this article proposes a method called BotFlowMon that relies only on content-agnostic flow-level data as input to identify OSN bot traffic. BotFlowMon introduces several new algorithms and techniques to classify social bot traffic from real OSN user traffic, including aggregating network flow records to obtain OSN transaction data, fusing transaction data to extract features and visualize flows, and an innovative density-valley-based clustering algorithm to subdivide each transaction into individual actions. The evaluation shows BotFlowMon can identify the traffic from social bots with a 96.1 percent accuracy, which, based on the worst case study on a testing machine, only takes no more than 0.71 seconds on average after it sees the traffic.
机译：随着在线社交网络（OSNS）的快速增长普及，OSN生态系统的安全性和隐私对公众来说至关重要。在威胁OSNS面临的威胁中，恶意的社交机器人已成为最常见和不利的。他们经常被用来违反用户的隐私，分发垃圾邮件，并扰乱金融市场，对有效的社交机器人检测解决方案构成了令人信服的需求。与具有严格对数据源的严格要求的传统社交机器人检测方法（例如，私人有效载荷信息，社交关系或活动历史）不同，本文提出了一种称为BotFlowmon的方法，该方法仅依赖于内容 - 不可知流程级数据作为识别的输入OSN BOT流量。 Botflowmon引入了几种新的算法和技术，用于将社交机器人流量分类到真实OSN用户流量，包括聚合网络流记录，以获取OSN交易数据，融合事务数据以提取特征和可视化流，以及基于创新的密度 - 谷的聚类算法将每个交易细分为单独的行动。评估显示BotFlowmon可以从社交机器人识别具有96.1％的精度的社交机器的流量，这是根据测试机器上最坏的情况研究，平均只需要不超过0.71秒，在看完流量之后。

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