Measurement: Sensors (Apr 2024)
Automated malware detection using machine learning and deep learning approaches for android applications
Abstract
The popularity of mobile devices has aided in the development of the Android platform. Malware is one of the most frequent cyberattacks with its prevalence growing daily across the network. Due to these flaws, a hacker can simply access the mobile device's private data. To alleviate this issue, this paper proposes a novel Malware Attack Detection in android using deep belief NETwork (MAD-NET) which accurately detects and mitigates the malware attacks and enhances the security of the devices. Initially, the benign and malicious data's from the CICAndMal2017 datasets are given as a input to the feature extraction process. In feature extraction, the data's are classified into two types such as signature-based data and behaviour-based data. The extracted features are converted as a sequence data and fed into the classification phase. In the classification phase, the benign and malicious data's are classified by using DBN technique. After being classified, the detected malicious data's are converted into a detection report for further processing. The proposed MAD-NET approach is evaluated by using CICAndMal2017 datasets and it is simulated by using Network Simulator. The proposed MAD-NET is evaluated by using many performance metrics such as accuracy, precision, recall, and F1-score. The MAD-NET technique achieves an overall accuracy of 99.83 % for Deep Belief Network (DBN), than Artificial Neural Networks (ANN), Generative Adversarial Network (GAN) and Long Short-Term Memory Network (LSTM) technique of 93.11 %, 96.75 %, 94.42 % respectively to detect and mitigate the android devices from malware attacks.
