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A Comprehensive Review on Malware Detection Approaches

IEEE Access, 2020
According to the recent studies, malicious software (malware) is increasing at an alarming rate, and some malware can hide in the system by using different obfuscation techniques.
Omer Aslan, Refik Samet
doaj   +3 more sources

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

Telematics and Informatics Reports
The ever-increasing growth of online services and smart connectivity of devices have posed the threat of malware to computer system, android-based smart phones, Internet of Things (IoT)-based systems.
Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar   +2 more
doaj   +3 more sources

A Survey on ML Techniques for Multi-Platform Malware Detection: Securing PC, Mobile Devices, IoT, and Cloud Environments [PDF]

Sensors
Malware has emerged as a significant threat to end-users, businesses, and governments, resulting in financial losses of billions of dollars. Cybercriminals have found malware to be a lucrative business because of its evolving capabilities and ability to ...
Jannatul Ferdous, Rafiqul Islam, Arash Mahboubi, Md Zahidul Islam   +3 more
doaj   +2 more sources

LEDA—Layered Event-Based Malware Detection Architecture [PDF]

Sensors
The rapid increase in new malware necessitates effective detection methods. While machine learning techniques have shown promise for malware detection, most research focuses on identifying malware through the content of executable files or full behavior ...
Radu Marian Portase, Raluca Laura Portase, Adrian Colesa, Gheorghe Sebestyen   +3 more
doaj   +2 more sources

Packed malware variants detection using deep belief networks [PDF]

MATEC Web of Conferences, 2020
Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years.
Zhang Zhigang, Chang Chaowen, Han Peisheng, Zhang Hongtao   +3 more
doaj   +1 more source

A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks

Information, 2023
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware.
Parvez Faruki, Rati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun, Rajendra Pamula   +5 more
doaj   +1 more source

An Adaptive Behavioral-Based Incremental Batch Learning Malware Variants Detection Model Using Concept Drift Detection and Sequential Deep Learning

IEEE Access, 2021
Malware variants are the major emerging threats that face cybersecurity due to the potential damage to computer systems. Many solutions have been proposed for detecting malware variants.
Abdulbasit A. Darem, Fuad A. Ghaleb, Asma A. Al-Hashmi, Jemal H. Abawajy, Sultan M. Alanazi, Afrah Y. Al-Rezami   +5 more
doaj   +1 more source

Malware Detection Using Memory Analysis Data in Big Data Environment

Applied Sciences, 2022
Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection
Murat Dener, Gökçe Ok, Abdullah Orman
doaj   +1 more source

The rise of obfuscated Android malware and impacts on detection methods [PDF]

PeerJ Computer Science, 2022
The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious
Wael F. Elsersy, Ali Feizollah, Nor Badrul Anuar   +2 more
doaj   +2 more sources

Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review

Applied Sciences, 2023
Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives.
Nana Kwame Gyamfi, Nikolaj Goranin, Dainius Ceponis, Habil Antanas Čenys   +3 more
doaj   +1 more source