wagner-group / active-learningLinks
Continuous Learning for Android Malware Detection (USENIX Security 2023)
☆72Updated 2 years ago
Alternatives and similar repositories for active-learning
Users that are interested in active-learning are comparing it to the libraries listed below
Sorting:
- ☆24Updated last year
- A curated list of malware-related papers.☆33Updated last year
- Building relation graph of Android APIs to catch the semantics between APIs, and used to enhancing Android malware detectors☆93Updated 3 years ago
- 从Androzoo下载数据集,区分年份以及良性/恶意应用,支持协程、代理、断点继续、错误重试等☆53Updated 2 years ago
- Papers, code and datasets about deep learning for Android malware defenses and malware detection☆147Updated last year
- the instructions about request access to AdvDroidZero☆12Updated last year
- Code for the paper Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers☆59Updated 3 years ago
- A novel and interpretable ML-based approach to classify malware with high accuracy and explain the classification result meanwhile.☆28Updated 2 years ago
- MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis☆44Updated 5 years ago
- ☆42Updated 4 years ago
- ☆11Updated last year
- Code for the AsiaCCS 2021 paper: "Malware makeover: Breaking ML-based static analysis by modifying executable bytes"☆55Updated last year
- Android Malware Detection with Graph Convolutional Networks using Function Call Graph and its Derivatives.☆39Updated 4 years ago
- [code] "CFGExplainer: Explaining Graph Neural Network-Based Malware Classification from Control Flow Graphs" by Jerome Dinal Herath, Prit…☆37Updated 3 years ago
- ☆16Updated 4 years ago
- adversarial examples, adversarial malware examples, adversarial malware detection, adversarial deep ensemble, Android malware variants☆57Updated 2 years ago
- Drebin - NDSS 2014 Re-implementation☆106Updated 7 years ago
- A Comprehensive Study of Learning-based Android Malware Detectors under Challenging Environments☆13Updated last year
- A curated resource list of adversarial attacks and defenses for Windows PE malware detection.☆73Updated 3 years ago
- deep learning, malware detection, predictive uncertainty, dataset shift, calibration, uncertainty quantification, android malware☆16Updated 3 years ago
- adversarial malware detection via a principled way☆22Updated 2 years ago
- 主题为”基于GAN的恶意软件对抗样本生成“。首先介绍了恶意软件发展现状,引出基于模式匹配、特征空间和问题空间三种方式去检测恶意软件。然后介绍了如何生成对抗样本攻击恶意软件检测器,详细介绍了基于GAN的恶意软件对抗样本的MalGAN框架,并对实验结果进行了对比。最后总结了结构…☆35Updated 4 years ago
- A Benchmark Dataset for Trustworthy Malware Family Classification under Concept Drift☆15Updated 7 months ago
- Code for "MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection"☆47Updated 3 years ago
- Code from the paper: Neurlux: Dynamic Malware Analysis Without Feature Engineering☆13Updated 4 years ago
- ☆24Updated 2 months ago
- ☆13Updated 3 years ago
- ☆22Updated 5 years ago
- MAB-Malware an open-source reinforcement learning framework to generate AEs for PE malware. We model this problem as a classic multi-arme…☆51Updated 6 months ago
- Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications☆144Updated 2 years ago