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Found 7 papers, 1 papers with code
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks.
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
We use adversarial training together with adversarial watermarks to train a robust watermarked model.
VulBERTa: Simplified Source Code Pre-Training for Vulnerability Detection
This paper presents VulBERTa, a deep learning approach to detect security vulnerabilities in source code.
Certified Federated Adversarial Training
We model an attacker who poisons the model to insert a weakness into the adversarial training such that the model displays apparent adversarial robustness, while the attacker can exploit the inserted weakness to bypass the adversarial training and force the model to misclassify adversarial examples.
A Hybrid Graph Neural Network Approach for Detecting PHP Vulnerabilities
This paper presents DeepTective, a deep learning approach to detect vulnerabilities in PHP source code.
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems
In the continuous data domain our attack successfully hides the cyber-physical attacks requiring 2. 87 out of 12 monitored sensors to be compromised on average.
Deep Latent Defence
The level of perturbation an attacker needs to introduce in order to cause such a misclassification can be extremely small, and often imperceptible.
