Android, the most dominant Operating System (OS), experiences immense popularity for smart devices for the last few years. Due to its' popularity and open characteristics, Android OS is becoming the tempting target of malicious apps which can cause serious security threat to financial institutions, businesses, and individuals. Traditional anti-malware systems do not suffice to combat newly created sophisticated malware. Hence, there is an increasing need for automatic malware detection solutions to reduce the risks of malicious activities. In recent years, machine learning algorithms have been showing promising results in classifying malware where most of the methods are shallow learners like Logistic Regression (LR). In this paper, we propose a deep learning framework, called Droid-NNet, for malware classification. However, our proposed method Droid-NNet is a deep learner that outperforms existing cutting-edge machine learning methods. We performed all the experiments on two datasets (Malgenome-215 & Drebin-215) of Android apps to evaluate Droid-NNet. The experimental result shows the robustness and effectiveness of Droid-NNet.
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Belief-Space Planning for Automated Malware Defense
Malware detection and response is critical to ensuring information security across a wide range of devices. There have been few attempts, however, to develop security systems that exploit the benefits of different malware detection techniques. We formally introduce an automated malware defense framework and represent it as a belief-space planning problem that optimally reduces the impact on the performance of a system. Using the framework, we then provide an example automated malware defense system for email worm detection and response. Finally, we show in simulation that the system outperforms standard security techniques that have been used in practice. The result is a novel belief-space planning approach to auto- mated malware defense designed for robust, accurate, and efficient use in large networks of resource-constrained devices.
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- Award ID(s):
- 1813490
- PAR ID:
- 10097682
- Date Published:
- Journal Name:
- IJCAI-ECAI Workshop on AI for Internet of Things
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
