MalAware: Effective and Efficient Run-Time Mobile Malware Detector
| Autor: | Alberto Ferrante, Jelena Milosevic, Miroslaw Malek |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Linear complexity
Computer science business.industry Detector Feature extraction 0102 computer and information sciences 02 engineering and technology Computer security computer.software_genre 01 natural sciences Mobile malware Statistical classification 010201 computation theory & mathematics Embedded system 0202 electrical engineering electronic engineering information engineering Malware 020201 artificial intelligence & image processing Mobile telephony Android (operating system) business computer |
| Zdroj: | DASC/PiCom/DataCom/CyberSciTech |
| DOI: | 10.1109/dasc-picom-datacom-cyberscitec.2016.65 |
| Popis: | Effective detection of malware is of paramount importance for securing the next generation of smart devices. Static detection, the preferred technique used so far, is not sufficiently powerful to defeat state-of-the-art malware, and will be even less effective in the near future. Dynamic malware detection guarantees better protection since it operates at run-time and can identify also unknown malware, however, the computational resources required are usually not affordable for battery operated devices. We propose MalAware, an effective, fast, and lightweight dynamic detection method. We identify malware by applying linear complexity classification algorithms to seven discriminating features and we improve the reliability of our detection using an efficient sliding windows mechanism. Our results, based on testing of about 2000 Android applications, demonstrate the timeliness and the effectiveness of detection in our approach. In fact, malware is detected within the first three minutes of execution with an F-measure of 0.85. |
| Databáze: | OpenAIRE |
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