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Addressing Adversarial Attacks Against Security Systems Based on Machine Learning
- Source :
- CyCon
- Publication Year :
- 2019
- Publisher :
- NATO CCD COE Publications, 2019.
-
Abstract
- Machine-learning solutions are successfully adopted in multiple contexts but the application of these techniques to the cyber security domain is complex and still immature. Among the many open issues that affect security systems based on machine learning, we concentrate on adversarial attacks that aim to affect the detection and prediction capabilities of machine-learning models. We consider realistic types of poisoning and evasion attacks targeting security solutions devoted to malware, spam and network intrusion detection. We explore the possible damages that an attacker can cause to a cyber detector and present some existing and original defensive techniques in the context of intrusion detection systems. This paper contains several performance evaluations that are based on extensive experiments using large traffic datasets. The results highlight that modern adversarial attacks are highly effective against machine-learning classifiers for cyber detection, and that existing solutions require improvements in several directions. The paper paves the way for more robust machine-learning-based techniques that can be integrated into cyber security platforms.
- Subjects :
- adversarial attack
Computer science
intrusion detection
0211 other engineering and technologies
evasion attacks
Evasion (network security)
Context (language use)
adversarial attacks
deep learning
machine learning
poisoning attacks
02 engineering and technology
Intrusion detection system
Machine learning
computer.software_genre
050601 international relations
Domain (software engineering)
Adversarial system
021110 strategic, defence & security studies
business.industry
Deep learning
05 social sciences
0506 political science
Damages
Malware
evasion attack
Artificial intelligence
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- CyCon
- Accession number :
- edsair.doi.dedup.....4a6378467f453d94e88dbdf5fe2946b4