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Found 11 papers, 1 papers with code
The Adversarial Implications of Variable-Time Inference
In our examination of the timing side-channel vulnerabilities associated with this algorithm, we identified the potential to enhance decision-based attacks.
Latent SHAP: Toward Practical Human-Interpretable Explanations
Model agnostic feature attribution algorithms (such as SHAP and LIME) are ubiquitous techniques for explaining the decisions of complex classification models, such as deep neural networks.
Attacking Object Detector Using A Universal Targeted Label-Switch Patch
However, none of prior research proposed a misclassification attack on ODs, in which the patch is applied on the target object.
Improving Interpretability via Regularization of Neural Activation Sensitivity
We evaluate the interpretability of models trained using our method to that of standard models and models trained using state-of-the-art adversarial robustness techniques.
Adversarial Machine Learning Threat Analysis and Remediation in Open Radio Access Network (O-RAN)
Next, we explore the various AML threats associated with O-RAN and review a large number of attacks that can be performed to realize these threats and demonstrate an AML attack on a traffic steering model.
Evaluating the Cybersecurity Risk of Real World, Machine Learning Production Systems
Using the extension, security practitioners can apply attack graph analysis methods in environments that include ML components; thus, providing security practitioners with a methodological and practical tool for evaluating the impact and quantifying the risk of a cyberattack targeting an ML production system.
Adversarial robustness via stochastic regularization of neural activation sensitivity
Recent works have shown that the input domain of any machine learning classifier is bound to contain adversarial examples.
An Automated, End-to-End Framework for Modeling Attacks From Vulnerability Descriptions
Given a description of a security vulnerability, the proposed framework first extracts the relevant attack entities required to model the attack, completes missing information on the vulnerability, and derives a new interaction rule that models the attack; this new rule is integrated within MulVAL attack graph tool.
Autosploit: A Fully Automated Framework for Evaluating the Exploitability of Security Vulnerabilities
The results show that Autosploit is able to automatically identify the system properties that affect the ability to exploit a vulnerability in both noiseless and noisy environments.
GIM: Gaussian Isolation Machines
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data.
When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures
We evaluate our method by building an extensive dataset of adversarial examples over the popular CIFAR-10 and MNIST datasets, and training a neural network-based detector to distinguish between normal and adversarial inputs.
