|Title||Dissecting Android Malware: Characterization and Evolution [link]|
|Author||Yajin Zhou and Xuxian Jiang from CS in NSCU||yajin email@example.com|
|Publishing||SP ’12 Proceedings of the 2012 IEEE Symposium on Security and Privacy||Year||2012|
|Abstract||The popularity and adoption of smartphones has greatly stimulated the spread of mobile malware, especially on the popular platforms such as Android. In light of their rapid growth, there is a pressing need to develop effective solutions. However, our defense capability is largely constrained by the limited understanding of these emerging mobile malware and the lack of timely access to related samples. In this paper, we focus on the Android platform and aim to systematize or characterize existing Android malware. Particularly, with more than one year effort, we have managed to collect more than 1,200 malware samples that cover the majority of existing Android malware families, ranging from their debut in August 2010 to recent ones in October 2011. In addition, we systematically characterize them from various aspects, including their installation methods, activation mechanisms as well as the nature of carried malicious payloads. The characterization and a subsequent evolution-based study of representative families reveal that they are evolving rapidly to circumvent the detection from existing mobile anti-virus software. Based on the evaluation with four representative mobile security software, our experiments show that the best case detects 79.6% of them while the worst case detects only 20.2% in our dataset. These results clearly call for the need to better develop next-generation anti-mobile-malware solutions.|
1. Goals and contributions
2. Malware Characterization
(1) Malware Installation:
(2) Activation: BOOT_COMLETED, SMS_RECEIVED, ACTION_MAIN
(3) Malicious Payloads
(4) Permission Uses
3. Malware Detection
(1) Anti-Virus Products
(2) signature base: 20.2% – 79.6%
This paper illustrates Android malware features in common, analyzing a large collection (over 1,200) in a chronological order. It raised a need of systematic Android malware analysis, which is not presence today despite of rapid growing in number. The authors collected 1,200 malware samples and classified them into 49 families (categories). A variety of findings has been shown in terms of characterization including malware installation, activation, malicious payloads, and permission use, which provides useful insight to identify Android malware in the near future when deciding if an application is suspicious.
Although the authors mentioned that existing mobile anti-virus application poorly detected malware, the biggest reason might be due to lack of samples as anti-virus detection normally depends on the signatures. It would be great the study could be carried out in a regular basis, making a comparison in changes.