Android Malware Detection: A Machine Leaning Approach
By: Hasan Abdulla
Potential Business Impact:
Finds bad phone apps using smart computer programs.
This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and real-world applicability. Key findings show that ensemble methods demonstrate superior performance, but there are trade-offs between model interpretability, efficiency, and accuracy. Given its increasing threat, the insights guide future research and practical use of ML to combat Android malware.
Similar Papers
An In-Depth Analysis of Cyber Attacks in Secured Platforms
Cryptography and Security
Finds bad apps on phones using smart computer tricks.
Optimized Approaches to Malware Detection: A Study of Machine Learning and Deep Learning Techniques
Cryptography and Security
Finds computer viruses faster and more accurately.
DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection
Cryptography and Security
Stops bad apps from tricking phone security.