DroidMat: Android Malware Detection through Manifest and API Calls Tracing
By Dong-Jie Wu, Ching-Hao Mao, Te-En Wei, Hahn-Ming Lee, Kuo-Ping Wu. 2012 Seventh Asia Joint Conference on Information Security, 2012
682 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Software, Signal Processing
View Paper: https://www.semanticscholar.org/paper/7b1996d4446f7682fa0ae36527f3cbc5e46dad58
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
- Lorenzo Cavallaro
- Jun Zhang
- Yang Xiang
- Surya Nepal
- Xiuzhen Cheng
- Konrad Rieck
- Sheng Wen
- Malte Hubner
In Software
- Lorenzo Cavallaro
- Yang Xiang
- Jun Zhang
- Surya Nepal
- Konrad Rieck
- Sheng Wen
- Malte Hubner
- Rosli Bin Salleh
In Signal Processing
- Lorenzo Cavallaro
- Yang Xiang
- Jun Zhang
- Surya Nepal
- Konrad Rieck
- Sheng Wen
- Malte Hubner
- Rosli Bin Salleh
Citations in the corpus
- A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “DroidMat [8] uses the API call traces, intent message passing, and information of components (activity, service, and receiver) to detect malware.”
- DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, methodology): “Similarly, the methods Crowdroid [4], DroidMat [36], Adagio [20], MAST [5], and DroidAPIMiner [1] analyze features statically extracted from Android applications using machine learning techniques.”
- MARVIN: Efficient and Comprehensive Mobile App Classification through Static and Dynamic Analysis (2015, background): “The remainder of related work on classifying Android malware based on static features evaluates their approaches on non-representative data sets: The data set of DroidMat [37] only contains 238 malware samples, PUMA [38] on the other hand, in addition to only learning from requested permissions, is ”
- The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “Permissions such as SEND_SMS are an important feature for analysis as most actions (e.g., a series of APIs) require particular permissions in order to be invoked [Wu et al. 2012].”
- Edge Computing Security: State of the Art and Challenges (2019, background): “[132] independently proposed the static analysis methods to identify possible malicious uses of dangerous Android APIs.”
- Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection (2018, background): “based classifiers to categorize applications as either malicious or benign [1], [2], [21], [25], [32].”