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PIDS: A Behavioral Framework for Analysis and Detection of Network Printer Attacks

机译：PIDS：用于分析和检测网络打印机攻击的行为框架

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摘要

Nowadays, every organization might be attacked through its network printers. The malicious exploitation of printing protocols is a dangerous and underestimated threat against every printer today. This article presents PIDS (Printers' IDS), an intrusion detection system for detecting attacks on printing protocols. PIDS continuously captures various features and events obtained from traffic produced by printing protocols in order to detect attacks. As part of this research, we conducted thousands of automatic and manual printing protocol attacks on various printers and recorded thousands of the printers' benign network sessions. Then we applied various supervised machine learning algorithms to classify the collected data as normal (benign) or abnormal (malicious). We evaluated several detection algorithms in order to obtain the best detection results for malicious protocol traffic of printers. Our empirical results suggest that the proposed framework is effective in detecting printing protocol attacks, providing an accuracy of 99.9 with negligible false-positive rate.
机译：如今，每个组织都可能通过其网络打印机受到攻击。对打印协议的恶意利用对当今的每台打印机都是危险且被低估的威胁。本文介绍了PIDS（打印机IDS），这是一种用于检测对打印协议的攻击的入侵检测系统。 PIDS连续捕获从打印协议产生的流量中获得的各种功能和事件，以检测攻击。作为这项研究的一部分，我们对各种打印机进行了数千次自动和手动打印协议攻击，并记录了数千个打印机的良性网络会话。然后，我们应用了各种监督的机器学习算法，将收集到的数据分类为正常（良性）或异常（恶意）。我们评估了几种检测算法，以获得针对打印机的恶意协议流量的最佳检测结果。我们的经验结果表明，所提出的框架可有效检测打印协议攻击，提供99.9的准确率和可忽略的假阳性率。

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