An Approach to Android Ransomware Detection Using Deep Learning
DOI:
https://doi.org/10.31185/wjps.325Keywords:
Ransomware, Android devices, Deep learning, Malicious threatsAbstract
As mobile devices continue to grow in popularity, the threat of ransomware attacks on Android devices has escalated, leading to serious privacy and financial risks for users. Traditional methods of ransomware detection have become less effective due to the evolving nature of these attacks. Consequently, the application of deep learning techniques offers promising potential for the detection and prevention of Android ransomware. This paper proposes an approach that utilizes deep learning algorithms to analyze and identify ransomware behavior on Android devices, aiming to enhance the security measures against these malicious threats. The model we used is a feedforward neural network model using the Keras Sequential. The model consists of three layers of densely connected neurons. The first layer has 64 units, the second layer also has 64 units, and the third and final layer has 2 units. The proposed model gave accuracy 98.9%.
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Copyright (c) 2024 Rawaa Ismael Farhan
This work is licensed under a Creative Commons Attribution 4.0 International License.
