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

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, as highlighted by recent published researches. 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 (ML) algorithms to classify the collected data as normal (benign) or abnormal (malicious). We evaluated several detection algorithms, feature selection methods, and the features needed in order to obtain the best detection results for 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 fall-positive rate.

💡 Research Summary

The paper introduces PIDS (Printers’ Intrusion Detection System), a novel network‑based IDS specifically designed to detect malicious activity targeting the printing protocols of networked printers and multifunction devices (MFPs). Recognizing that printers expose a wide range of wired and wireless interfaces and support multiple protocol families (PJL, PCL, PostScript, etc.), the authors argue that traditional security measures often overlook this attack surface. While prior work has examined printer vulnerabilities or focused on 3‑D printer security, no study to date has applied supervised machine‑learning (ML) techniques to the detection of attacks on conventional printer protocols.

PIDS’s core methodology consists of (1) capturing printer traffic at the TCP session level, (2) extracting a lightweight set of 75 metadata‑only features, and (3) feeding the resulting feature vectors into a suite of supervised classifiers. The feature set is divided into three categories: size (28 features, e.g., bytes sent/received per side, packet size statistics), time (25 features, e.g., session duration, inter‑packet intervals), and generic TCP properties (22 features, e.g., flag counts, packet counts). Crucially, each feature is computed for both communication directions—side A (the initiator of the TCP three‑way handshake) and side B (the responder)—thereby capturing the asymmetric behavior that printers exhibit compared to typical client hosts.

To evaluate the approach, the authors built a comprehensive dataset comprising both benign and malicious sessions. The benign portion consists of 8,813 sessions recorded over two months from eight office printers (OKI, Xerox, Brother, HP, etc.) in a university environment that was assumed to be attack‑free. The malicious portion was generated using an extended version of the open‑source PRET (Printer Exploitation Toolkit). The extension adds two key capabilities: automation (the tool can launch a configurable number of attack sessions, each containing 1–20 malicious commands) and randomization (commands are shuffled in order, timing, and grouping to emulate diverse attacker strategies). Across ten printer models and three protocol families, the authors produced roughly 4,000 malicious sessions, covering a wide spectrum of attacks such as DoS, privilege escalation, firmware manipulation, and data leakage.

Multiple supervised algorithms were trained and tested, including Random Forest, Support Vector Machine, k‑Nearest Neighbors, and Decision Trees. Using 10‑fold cross‑validation, Random Forest achieved the best performance: 99.9 % overall accuracy with a false‑positive rate approaching zero. Feature‑selection experiments revealed that the full 75‑feature vector yields the highest detection rate, yet even a reduced set of the top 30–40 features maintains >99 % accuracy, indicating that the system can be tuned for lower computational overhead without sacrificing security.

Implementation details emphasize that PIDS operates on metadata alone, avoiding deep payload inspection. This design choice reduces CPU and memory requirements, enabling deployment directly on the printer hardware, on a nearby network TAP, or on a switch port mirroring configuration. Because the system does not rely on protocol‑specific payload parsing, it remains robust against variations in vendor‑specific encoding and is future‑proof against new attack tools that still use low‑level printing commands.

The authors acknowledge several limitations. The dataset reflects traffic from 2016–2017 and may not capture newer printer models, encrypted printing protocols (e.g., IPP over TLS), or the effects of heavy multi‑printer environments. Moreover, real‑world enterprise networks could introduce additional noise and concurrent traffic that were not simulated.

Future work is outlined along four axes: (1) online learning and concept‑drift detection to keep the model up‑to‑date with evolving attack patterns; (2) extending the framework to other office devices such as scanners, projectors, and IoT printers, creating a unified “office‑device IDS”; (3) adapting the feature extraction to handle encrypted protocol streams while preserving the lightweight nature of the system; and (4) long‑term field trials in production environments to validate operational stability and false‑positive impact.

In summary, PIDS demonstrates that a carefully engineered set of metadata‑based features, combined with standard supervised ML classifiers, can reliably detect a broad range of printer‑protocol attacks with near‑perfect accuracy and negligible false alarms. Its lightweight, protocol‑agnostic design makes it a practical candidate for immediate deployment in corporate networks, filling a notable gap in current cybersecurity defenses.