An Automatic Source Code Vulnerability Detection Approach Based on KELM
Problem
Detecting source code vulnerabilities traditionally requires manually defined rules and features that are costly and perform weakly in practice.
Why it matters
Undetected code vulnerabilities can be exploited, and manual feature engineering does not scale.
What the AI did
A kernel-based Extreme Learning Machine (KELM) approach that automatically extracts vulnerability features while avoiding the heavy iterative training cost of Bi-LSTM, using non-iterative training for fast convergence.
Results
not stated in source
Impact
The abstract describes the method's motivation but is truncated before any measured performance results.
Record
| Venue | Security and Communication Networks |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Gaigai Tang, Lin Yang, Shuangyin Ren, Lianxiao Meng, Feng Yang, Huiqiang Wang |
| Citations | 12 (OpenAlex, at import time) |
| References | 30 |
| Model or method | KELM (kernel Extreme Learning Machine) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1155/2021/5566423 |
| Open access | hybrid |
| Keywords | vulnerability detection, KELM, Bi-LSTM, source code, kernel method |
Importance rating
significant — thousands to millions affected
Vulnerability detection affects software security broadly; manual feature engineering costly and weak, but abstract gives no scale of impact.
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