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Found 9 papers, 2 papers with code
Adversarially Robust Deep Learning with Optimal-Transport-Regularized Divergences
We introduce the $ARMOR_D$ methods as novel approaches to enhancing the adversarial robustness of deep learning models.
Sequential Gradient Coding For Straggler Mitigation
In our second scheme, which constitutes our main contribution, we apply GC to a subset of the tasks and repetition for the remainder of the tasks.
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants
This provides an opportunity for the defenders (i. e., malware detectors) to detect the adversarial variants by utilizing more than one view of a malware file (e. g., source code view in addition to the binary view).
Heterogeneous Domain Adaptation with Adversarial Neural Representation Learning: Experiments on E-Commerce and Cybersecurity
This incentivizes developing domain adaptation methods that leverage the knowledge in known domains (source) and adapt to new domains (target) with a different probability distribution.
Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence
In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection.
Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach
Given that most malware detectors enforce a query limit, this could result in generating non-realistic adversarial examples that are likely to be detected in practice due to lack of stealth.
Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach
While Information Extraction (IE) techniques can be used to extract the PII automatically, Deep Learning (DL)-based IE models alleviate the need for feature engineering and further improve the efficiency.
Binary Black-box Evasion Attacks Against Deep Learning-based Static Malware Detectors with Adversarial Byte-Level Language Model
Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis.
Time-Resolved fMRI Shared Response Model using Gaussian Process Factor Analysis
Some recent work has implemented probabilistic models to extract a shared representation in task fMRI.
