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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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RansomShield: A Visualization Approach to Defending Mobile Systems Against Ransomware
The unprecedented growth in mobile systems has transformed the way we approach everyday computing. Unfortunately, the emergence of a sophisticated type of malware known as ransomware poses a great threat to consumers of this technology. Traditional research on mobile malware detection has focused on approaches that rely on analyzing bytecode for uncovering malicious apps. However, cybercriminals can bypass such methods by embedding malware directly in native machine code, making traditional methods inadequate. Another challenge that detection solutions face is scalability. The sheer number of malware variants released every year makes it difficult for solutions to efficiently scale their coverage. To address these concerns, this work presents RansomShield, an energy-efficient solution that leverages CNNs to detect ransomware. We evaluate CNN architectures that have been known to perform well on computer vision tasks and examine their suitability for ransomware detection. We show that systematically converting native instructions from Android apps into images using space-filling curve visualization techniques enable CNNs to reliably detect ransomware with high accuracy. We characterize the robustness of this approach across ARM and x86 architectures and demonstrate the effectiveness of this solution across heterogeneous platforms including smartphones and chromebooks. We evaluate the suitability of different models for mobile systems by comparing their energy demands using different platforms. In addition, we present a CNN introspection framework that determines the important features that are needed for ransomware detection. Finally, we evaluate the robustness of this solution against adversarial machine learning (AML) attacks using state-of-the-art Android malware dataset.
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- Award ID(s):
- 1947580
- PAR ID:
- 10413499
- Date Published:
- Journal Name:
- ACM Transactions on Privacy and Security
- Volume:
- 26
- Issue:
- 3
- ISSN:
- 2471-2566
- Page Range / eLocation ID:
- 1 to 30
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3579822
