ICPFuzzer: proprietary communication protocol fuzzing by using machine learning and feedback strategies

Pei-Yi Lin, Chia‐Wei Tien, Ting‐Chun Huang, Chin‐Wei Tien

Cybersecurity 2021 article cited by 14

Source Open-access PDF More in Software & Coding

importance 6/10 partially solved Software Testing and Debugging Techniques industrial cybersecurity stage: research

Problem

Fuzzing proprietary industrial control system communication protocols is difficult without prior knowledge of their customized, complicated structures.

Why it matters

Undiscovered vulnerabilities in industrial control systems could allow attacks on critical infrastructure.

What the AI did

ICPFuzzer uses an LSTM to learn protocol features and automatically generate mutated test data, using device responses and weight strategies to find inputs causing unusual connection states.

Results

Effectiveness verified by comparison with an open-source tool; details truncated in abstract, no numbers stated.

Impact

Approach could find vulnerabilities in ICS devices without prior protocol knowledge, per authors' verification claim.

Record

Venue	Cybersecurity
Published	2021
Work type	article
Authors	Pei-Yi Lin, Chia‐Wei Tien, Ting‐Chun Huang, Chin‐Wei Tien
Citations	14 (OpenAlex, at import time)
References	32
Model or method	LSTM
Organization	not stated in source
Primary source	https://doi.org/10.1186/s42400-021-00087-5
Open access	diamond
Keywords	fuzzing, LSTM, industrial control systems, protocol security, vulnerability discovery

Importance rating

6 / 10

significant — thousands to millions affected

ICS vulnerabilities threaten critical infrastructure (smart meters etc.); abstract calls threats huge but quantifies no scale; solution only partially verified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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