Hardening Random Forest Cyber Detectors Against Adversarial Attacks

Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks t...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computational intelligence 2020-08, Vol.4 (4), p.427-439
Main Authors: Apruzzese, Giovanni, Andreolini, Mauro, Colajanni, Michele, Marchetti, Mirco
Format: Article
Language:English
Subjects:
Adversarial samples
Algorithms
botnet
Communications traffic
Computer security
Cybersecurity
Detectors
flow inspection
Forestry
intrusion detection
Intrusion detection systems
Machine learning
Machine learning algorithms
Network analysis
Perturbation
Perturbation methods
random forest
Sensors
Training
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Summary:Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks that involve the perturbation of initial samples. Existing defenses assume unrealistic scenarios; their results are underwhelming in non-adversarial settings; or they can be applied only to machine learning algorithms that perform poorly for cyber security. We present an original methodology for countering adversarial perturbations targeting intrusion detection systems based on random forests. As a practical application, we integrate the proposed defense method in a cyber detector analyzing network traffic. The experimental results on millions of labelled network flows show that the new detector has a twofold value: it outperforms state-of-the-art detectors that are subject to adversarial attacks; it exhibits robust results both in adversarial and non-adversarial scenarios.
ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2019.2961157