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How to Train your Antivirus: RL-based Hardening through the Problem-Space
It also makes possible to provide theoretical guarantees on the robustness of the model against a particular set of adversarial capabilities.
Unraveling the Key of Machine Learning Solutions for Android Malware Detection
Android malware detection serves as the front line against malicious apps.
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)
Machine learning (ML) plays a pivotal role in detecting malicious software.
Adversarial Markov Games: On Adaptive Decision-Based Attacks and Defenses
To reliably measure robustness, it is important to evaluate against realistic and worst-case attacks.
"Real Attackers Don't Compute Gradients": Bridging the Gap Between Adversarial ML Research and Practice
Recent years have seen a proliferation of research on adversarial machine learning.
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers
Empirically, we show that existing backdoor attacks in malware classifiers are still detectable by recent defenses such as MNTD.
Realizable Universal Adversarial Perturbations for Malware
Universal Adversarial Perturbations (UAPs), which identify noisy patterns that generalize across the input space, allow the attacker to greatly scale up the generation of such examples.
Dos and Don'ts of Machine Learning in Computer Security
With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas.
Intriguing Properties of Adversarial ML Attacks in the Problem Space
Second, building on our formalization, we propose a novel problem-space attack on Android malware that overcomes past limitations.
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time
Is Android malware classification a solved problem?
