A Real-Time P2P Bot Host Detection in a Large-Scale Network Using Statistical Network Traffic Features and Apache Spark Streaming Platform

S. Saravanan, G. Prakash, B. Uma Maheswari
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Abstract

Nowadays, Peer-to-Peer (P2P) bots play a significant role in launching attacks such as phishing, distributed denial-of-service (DDoS), email spam, click fraud, cryptocurrency mining, etc. The analysis of statistical network traffic features of hosts is one of the commonly used methods to detect P2P bots. Modern P2P bot detection systems need to extract features from massive streaming network traffic as the size of the Internet keeps increasing every day. However, traditional detection systems have trouble detecting bots in real-time in large-scale networks as they are not implemented on big data streaming platforms. Hence, this work proposes a network flow-based P2P bot detection system implemented on Apache Spark Structured Streaming Platform to detect P2P bots in real time by analyzing massive streaming network traffic data generated from large-scale networks. Such detection of P2P bots is based on statistical network traffic features: destination diversity ratio, control packets ratio, and total source bytes sent in a flow. There are two components in the proposed system: the first component detects potential P2P hosts using the Destination Diversity Ratio (DDR), and the second component finds out P2P bot hosts from the P2P hosts identified by the first component. Furthermore, the performance of the detection components depends on the time window at which statistical features are extracted. Hence, this work also conducted experiments to study the effect of different time windows on detection components. The proposed system is evaluated using real-world datasets and achieves a True Positive Rate (TPR) of 99.87%.
基于统计网络流量特征和Apache Spark流媒体平台的大规模网络实时P2P Bot主机检测
如今,点对点(P2P)机器人在发起网络钓鱼、分布式拒绝服务(DDoS)、电子邮件垃圾邮件、点击欺诈、加密货币挖掘等攻击方面发挥着重要作用。分析主机的统计网络流量特征是检测P2P僵尸程序的常用方法之一。随着互联网规模的日益扩大,现代P2P僵尸检测系统需要从海量的流网络流量中提取特征。然而,传统的检测系统很难在大规模网络中实时检测机器人,因为它们没有在大数据流平台上实现。因此,本文提出了一种基于网络流量的P2P机器人检测系统,该系统在Apache Spark结构化流媒体平台上实现,通过分析大规模网络产生的海量流网络流量数据,实时检测P2P机器人。P2P机器人的检测基于统计网络流量特征:目的集集比、控制包比、流中发送的源字节总数。该系统包含两个组件:第一个组件使用目的地多样性比(DDR)检测潜在的P2P主机,第二个组件从第一个组件识别的P2P主机中找出P2P bot主机。此外,检测组件的性能取决于提取统计特征的时间窗口。因此,本工作还进行了实验,研究不同时间窗对检测分量的影响。该系统使用真实数据集进行了评估,并实现了99.87%的真阳性率(TPR)。
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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