Deep Learning-Based Attack Detection and Classification in Android Devices

Abstract

The increasing proliferation of Androidbased devices, which currently dominate the market with a staggering 72% global market share, has made them a prime target for attackers. Consequently, the detection of Android malware has emerged as a critical research area. Both academia and industry have explored various approaches to develop robust and efficient solutions for Android malware detection and classification, yet it remains an ongoing challenge. In this study, we present a supervised learning technique that demonstrates promising results in Android malware detection. The key to our approach lies in the creation of a comprehensive labeled dataset, comprising over 18,000 samples classified into five distinct categories: Adware, Banking, SMS, Riskware, and Benign applications. The effectiveness of our proposed model is validated using well-established datasets such as CICMalDroid2020, CICMalDroid2017, and CICAndMal2017. Comparing our results with state-of-the-art techniques in terms of precision, recall, efficiency, and other relevant factors, our approach outperforms other semi-supervised methods in specific parameters. However, we acknowledge that our model does not exhibit significant deviations when compared to alternative approaches concerning certain aspects. Overall, our research contributes to the ongoing efforts in the development of advanced techniques for Android malware detection and classification. We believe that our findings will inspire further investigations, leading to enhanced security measures and protection for Android devices in the face of evolving threats.

A Novel Neural Network Architecture Using Automated Correlated Feature Layer to Detect Android Malware Applications

“…Static analysis on the APK files of three datasets, namely CICMalDroid2020, CIC-InvestAndMal2019, and CICAndMal2017, was conducted [21]. From these files, API calls, intent, and permission-based features were extracted and 215 features were retained.…”

Interference Management Based on Meta-Heuristic Algorithms in 5G Device-to-Device Communications

“…D2D communication technology encounters numerous challenges, including interference mitigation, mode selection, resource allocation, and power control. Another critical issue is ensuring security, especially in the exchange of sensitive information [12,13]. The effective allocation of resources and adept power management are key to reducing interference and maximizing overall network throughput [14].…”

“…The field of D2D communication technology confronts several challenges, including interference mitigation, mode selection, resource allocation, and power control [15,16]. A significant concern is the aspect of security, especially when exchanging sensitive information [13,17]. Efficient resource allocation and effective power management lead to reduced interference and maximized overall throughput [14,18].…”