@inproceedings{0581f82aaebe45e287287b54c86542be,
title = "PIDS: A Behavioral Framework for Analysis and Detection of Network Printer Attacks",
abstract = "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.",
author = "Asaf Hecht and Adi Sagi and Yuval Elovici",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 13th International Conference on Malicious and Unwanted Software, MALWARE 2018 ; Conference date: 22-10-2018 Through 24-10-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/MALWARE.2018.8659371",
language = "English",
series = "MALWARE 2018 - Proceedings of the 2018 13th International Conference on Malicious and Unwanted Software",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "87--94",
booktitle = "MALWARE 2018 - Proceedings of the 2018 13th International Conference on Malicious and Unwanted Software",
address = "United States",
}