A Review of Machine Learning-Based Zero-Day Attack Detection¶
Guo, Y. (2023). A Review of Machine Learning-Based Zero-Day Attack Detection: Challenges and Future Directions. Computer Communications, 198, 175-185.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Invasive Species
- Ecology. The literal case: introduced organisms in ecosystems whose checks evolved with different species sets spread, displace natives, and reconfigure system functions. Cybersecurity. Novel malware against a population with no matching signature or heuristic, especially across a platform or air-gap crossing — the "system without controls for it" structure is identical.
This sourceEstablishes that signature-based detection cannot catch zero-day (novel) malware because no signature for previously-unseen code exists in the database, motivating anomaly/ML-based detection — the security instance of a control repertoire mismatched to a newcomer.
- Ecology. The literal case: introduced organisms in ecosystems whose checks evolved with different species sets spread, displace natives, and reconfigure system functions. Cybersecurity. Novel malware against a population with no matching signature or heuristic, especially across a platform or air-gap crossing — the "system without controls for it" structure is identical.
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