A deep learning‐based approach for software vulnerability detection using code metrics
importance 7/10
ai assisted
Software Engineering Research cybersecurity stage: research
Problem
Detecting software vulnerabilities traditionally depends on human experts manually defining features, which is tedious and subjective.
Why it matters
Vulnerabilities can devastate information security, affecting the economy, social stability, and national security.
What the AI did
A deep learning approach uses code metrics as objective code characterisation, feeding sequences of metrics into a composite neural network to detect vulnerabilities.
Results
not stated in source
Impact
Evidence supports only that a method was proposed; no measured outcomes are stated.
Record
| Venue | IET Software |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Fazli Subhan, Xiaoxue Wu, Lili Bo, Xiaobing Sun, Muhammad Rahman |
| Citations | 42 (OpenAlex, at import time) |
| References | 37 |
| Model or method | Composite CNN + long short-term memory (LSTM) network |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1049/sfw2.12066 |
| Open access | gold |
| Keywords | vulnerability detection, deep learning, code metrics, CNN, LSTM, software security |
Importance rating
7
significant — thousands to millions affected
Abstract states vulnerabilities can devastate information security, affecting the economy, social stability, and national security — broad societal impact, though unquantified.
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