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

open access: yesSensors
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   +3 more
doaj   +4 more sources

A Comprehensive Review on Malware Detection Approaches

open access: yesIEEE 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

A survey of IoT malware and detection methods based on static features

open access: yesICT Express, 2020
Due to a lack of security design as well as the specific characteristics of IoT devices such as the heterogeneity of processor architecture, IoT malware detection has to deal with very unique challenges, especially on detecting cross-architecture IoT ...
Quoc-Dung Ngo   +3 more
doaj   +3 more sources

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

open access: yesTelematics 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   +2 more
doaj   +3 more sources

Semantics-aware malware detection [PDF]

open access: yes2005 IEEE Symposium on Security and Privacy (S&P'05), 2005
A malware detector is a system that attempts to determine whether a program has malicious intent. In order to evade detection, malware writers (hackers) frequently use obfuscation to morph malware. Malware detectors that use a pattern-matching approach (such as commercial virus scanners) are susceptible to obfuscations used by hackers.
Mihai Christodorescu   +4 more
openaire   +2 more sources

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

open access: yesSensors
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   +3 more
doaj   +2 more sources

Packed malware variants detection using deep belief networks [PDF]

open access: yesMATEC 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   +3 more
doaj   +1 more source

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

open access: yesInformation, 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   +5 more
doaj   +1 more source

MalFuzz: Coverage-guided fuzzing on deep learning-based malware classification model.

open access: yesPLoS ONE, 2022
With the continuous development of deep learning, more and more domains use deep learning technique to solve key problems. The security issues of deep learning models have also received more and more attention.
Yuying Liu   +4 more
doaj   +2 more sources

Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions

open access: yesIEEE Access, 2023
Malware has emerged as a cyber security threat that continuously changes to target computer systems, smart devices, and extensive networks with the development of information technologies.
Nor Zakiah Gorment   +3 more
doaj   +1 more source

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