An In-Depth Analysis of Cyber Attacks in Secured Platforms

TL;DR

This paper provides a comprehensive survey and comparative analysis of machine learning techniques for detecting malicious threats, specifically ransomware, on Android devices, highlighting current challenges and performance metrics.

Contribution

It offers a detailed review of existing methods and introduces a dataset for evaluating the accuracy of machine learning approaches against Android malware.

Findings

Machine learning techniques vary in detection accuracy.

Developing robust anti-malware systems remains challenging due to data requirements.

The study presents a new Android Applications dataset for benchmarking.

Abstract

There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue in mobile communication, disrupting user experiences and posing significant privacy threats. This study surveys commonly used machine learning techniques for detecting malicious threats in phones and examines their performance. The majority of past research focuses on customer feedback and reviews, with concerns that people might create false reviews to promote or devalue products and services for personal gain. Hence, the development of techniques for detecting malicious threats using machine learning has been a key focus. This paper presents a comprehensive comparative study of current research on the issue of malicious threats and methods for tackling these challenges. Nevertheless, a huge amount of information is required by these methods, presenting a challenge for developing robust, specialized automated anti-malware systems. This research describes the Android Applications dataset, and the accuracy of the techniques is measured using the accuracy levels of the metrics employed in this study.