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A Novel Knowledge Search Structure for Android Malware Detection
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AI
DOI：10.1109/TSC.2024.3496333.png)
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While the Android platform is gaining explosive popularity, the number of malicious software (malware) is also increasing sharply. Thus, numerous malware detection schemes based on deep learning have been proposed. However, they are usually suffering from the cumbersome models with complex architectures and tremendous parameters. They usually require heavy computation power support, which seriously limit their deployment on actual application environments with limited resources (e.g., mobile edge devices). To surmount this challenge, we propose a novel Knowledge Distillation (KD) structure-Knowledge Search (KS). KS exploits Neural Architecture Search (NAS) to adaptively bridge the capability gap between teacher and student networks in KD by introducing a parallelized student-wise search approach. In addition, we carefully analyze the characteristics of malware and locate three cost-effective types of features closely related to malicious attacks, namely, Application Programming Interfaces (APIs), permissions and vulnerable components, to characterize Android Applications (Apps). Therefore, based on typical samples collected in recent years, we refine features while exploiting the natural relationship between them, and construct corresponding datasets. Massive experiments are conducted to investigate the effectiveness and sustainability of KS on these datasets. Our experimental results show that the proposed method yields an accuracy of 97.89% to detect Android malware, which performs better than state-of-the-art solutions.
Keyword:
Malware
Feature extraction
Operating systems
Static analysis
Smart phones
Computational modeling
Deep learning
Vectors
Security
Radio frequency
Android
malware detection
knowledge distillation
neural architecture search
multi-layer perceptron
期刊
IF:
5.8
论文数:
2.2K
被引数:
6.5K
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On the Impact of Sample Duplication in Machine-Learning-Based Android Malware Detection基于机器学习的Android恶意软件检测中样本复制的影响
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