Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

DIVYA SHARMA; JASVIR SINGH

doi:10.36948/ijfmr.2024.v06i03.20891

Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

Author(s)	DIVYA SHARMA, JASVIR SINGH
Country	India
Abstract	Mobile devices have end up integral elements of our each day lives, storing great quantities of private and sensitive data. However, this convenience comes with inherent dangers, as cybercriminals increasingly more goal cellular devices for malicious sports consisting of stealing personal statistics, disrupting operations, and compromising the working machine. Various sorts of cellular malware, together with Remote Access Tools (RATs), Bank Trojans, Ransomware, Cryptomining Malware, and Advertising Click Fraud, pose sizable threats to users' privateness and protection. Detecting and mitigating cell malware is essential in safeguarding customers' gadgets and statistics. This paper systematically examines and surveys cellular malware detection strategies, specializing in traditional and superior strategies. Traditional detection methods encompass signature-based detection, conduct-primarily based detection, and permission analysis, while superior techniques embody gadget studying-based detection and anomaly detection. Each approach has its strengths and obstacles, emphasizing the significance of using a mixture of strategies for complete safety. The paper reviews relevant literature to research the effectiveness of different detection techniques and their packages in actual-global situations. It discusses the evolution of malware detection methodologies, highlighting advancements which include mobile botnet type, dynamic anomaly-based totally detection, and characteristic-based adverse attacks on device getting to know classifiers. Additionally, the paper explores the demanding situations confronted via cutting-edge detection techniques and proposes avenues for future research to address those obstacles. By presenting a comprehensive evaluation of cell malware detection strategies, this thesis contributes to the advancement of studies in cybersecurity and aids in the improvement of greater strong and green detection mechanisms to combat evolving threats in the cellular surroundings.
Keywords	Mobile Malware, Signature-Based Detection, Behavior Based Detection, Machine Learning Based Detection, Anomaly Based Detection.
Field	Engineering
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-05-22
Cite This	Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques - DIVYA SHARMA, JASVIR SINGH - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20891
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.20891
Short DOI	https://doi.org/gtwmr6

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Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

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