DroidAPIMiner: Mining API-Level Features for Robust Malware Detection in Android

By Yousra Aafer, Wenliang Du, Heng Yin. 2013

750 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Battista Biggio
2. Fabio Roli
3. Giorgio Giacinto
4. Davide Maiorca
5. Igino Corona
6. Ambra Demontis
7. Marco Melis
8. Konrad Rieck

In Software

1. Fabio Martinelli
2. Yang Xiang
3. Jun Zhang
4. Daniele Sgandurra
5. Andrea Saracino
6. Gianluca Dini
7. Surya Nepal
8. A. Aiken

In Signal Processing

1. Battista Biggio
2. Fabio Roli
3. Giorgio Giacinto
4. Davide Maiorca
5. Ambra Demontis
6. Marco Melis
7. Igino Corona
8. Giorgio Fumera

Citations in the corpus

• Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017, background): “DURING the last decade, machine learning has been increasingly applied in security-related tasks, in response to the increasing variability and sophistication of modern attacks [1], [3], [6], [27], [33].”
• Apposcopy: semantics-based detection of Android malware through static analysis (2014, methodology): “The DroidAPIMiner tool [6] also considers API features and uses machine learning to automatically classify an application as malicious or benign.”
• 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.”
• MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention (2018, background): “[38] statically analyzes app to derive a set of features for malware detection at application-level and evaluates several classifiers for Android apps.”
• A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “Eighty sensitive API function calls were extracted from the file classes.dex by 126:19 using DroidAPIMiner [105].”
• A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, background): “According to [35], those selected APIs are useful to distinguish malware and benign applications.”
• DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “However, DroidAPIMiner builds on a k-nearest neighbor classifier that induces a significant runtime overhead and impedes operating the method on a smartphone.”
• MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, methodology): “For each app in our datasets, we extract the list of API calls, using Androguard 9 , since, as explained in Section IV-E, these constitute the features used by D ROID APIM INER [2], against which we compare our system.”
• DL-Droid: Deep learning based android malware detection using real devices (2019, background): “Arp et al. (2014) , Aafer et al. (2013) , Yerima et al. (2015a) , Fan et al. (2017) , Yerima et al. (2015b) , Kang et al. (2016b) , Cen et al. (2015) , Westyarian et al. (2015) and Kang et al. (2016a) are few examples of detection methods using static analysis.”
• Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, neutral): “D.2.4 [Software Engineering]: Software/Program Verification— Validation; D.4.6 [Operating Systems]: Security and Protection— Invasive software”