Drebin : � Efficient and Explainable Detection of Android Malware in Your Pocket

By DroidKungFu, LinuxLotoor, Opfake, GoldDream, GingerMaster, MobileTx, BaseBridge, FakeRun, Iconosys, SendPay and 2 more. 2014

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Citations in the corpus

Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, methodology): “Drebin also provides an interpretation of its decision, by highlighting the most suspicious (or benign) features that contributed to the decision [3].”
MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, background): “%) than DroidAPIMiner, which reports a maximum detection rate of 97.8%, and Drebin, which identifies only 94% of malware.”
Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, unknown): “D.2.4 [Software Engineering]: Software/Program Verification— Validation; D.4.6 [Operating Systems]: Security and Protection— Invasive software”
ANDRUBIS -- 1,000,000 Apps Later: A View on Current Android Malware Behaviors (2014, methodology): “Another available malware dataset is the one used by Drebin [39] for classifying Android malware.”
On the effectiveness of system API-related information for Android ransomware detection (2018, methodology): “We considered a dataset composed of 17 744 Android malware samples that do not belong to the ransomware category, taken from the following sources: (i) Drebin dataset, one of the most recent, publicly available datasets of malicious Android applications6 (which also contains the samples from the Gen”
Stealth attacks: An extended insight into the obfuscation effects on Android malware (2015, unknown): “Recently, an anti-malware system based on machine learning techniques (Drebin) has been proposed [37].”
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks (2018, methodology): “2 Android Malware Detection The Drebin data [1] consists of around 120,000 legitimate and around 5000 malicious Android applications, labeled using the VirusTotal service.”
Explaining Black-box Android Malware Detection (2018, methodology): “Among the various detection strategies proposed by companies and academic researchers, those based on machine learning have shown the most promising results, due to their flexibility against malware variants and obfuscation attempts [1], [7].”
AVclass: A Tool for Massive Malware Labeling (2016, methodology): “Some of the datasets overlap, most notably the Drebin [1] dataset is a superset of MalGenome [33].”
Machine Unlearning of Features and Labels (2021, methodology): “In particular, we employ datasets for spam filtering [49], Android malware detection [5], diabetis forecasting [24] and prediction of income based on census data [23].”