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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

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

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

ICT 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, Huy-Trung Nguyen, Van-Hoang Le, Doan-Hieu Nguyen   +3 more
doaj   +3 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

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

PLoS 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, Pin Yang, Peng Jia, Ziheng He, Hairu Luo   +4 more
doaj   +2 more sources

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

IEEE 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, Ali Selamat, Lim Kok Cheng, Ondrej Krejcar   +3 more
doaj   +1 more source

Technique for IoT malware detection based on control flow graph analysis

Радіоелектронні і комп'ютерні системи, 2022
The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats.
Kira Bobrovnikova, Sergii Lysenko, Bohdan Savenko, Piotr Gaj, Oleg Savenko   +4 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