Reinforcement Learning Based Mobile Offloading for Cloud-Based Malware Detection
| Autor: | Yanda Li, Geyi Sheng, Xiaojiang Du, Xiaoyue Wan, Liang Xiao |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Software_OPERATINGSYSTEMS
Computer science business.industry 05 social sciences Real-time computing 050801 communication & media studies 020206 networking & telecommunications Cloud computing 02 engineering and technology computer.software_genre Convolutional neural network Mobile malware 0508 media and communications Server 0202 electrical engineering electronic engineering information engineering Reinforcement learning Malware Mobile telephony business Mobile device computer |
| Zdroj: | GLOBECOM |
| DOI: | 10.1109/glocom.2017.8254503 |
| Popis: | Cloud-based malware detection improves the detection performance for mobile devices that offload their malware detection tasks to security servers with much larger malware database and powerful computational resources. In this paper, we investigate the competition of the radio transmission bandwidths and the data sharing of the security server in the dynamic malware detection game, in which each mobile device chooses its offloading rate of the application traces to the security server. As the Q-learning technique has a slow learning rate in the game with high dimension, we have designed a mobile malware detection based on hotbooting-Q techniques, which initiates the quality values based on the malware detection experience. We propose an offloading strategy based on deep Q-network technique with a deep convolutional neural network to further improve the detection speed, the detection accuracy, and the utility. Preliminary simulation results verify the detection gain of the scheme compared with the Q- learning based strategy. |
| Databáze: | OpenAIRE |
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