A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

A Gaurav, BB Gupta, PK Panigrahi - Enterprise Information …, 2023 - Taylor & Francis
ABSTRACT The Internet of Things (IoT) is a relatively new technology that has piqued
academics' and business information systems' attention in recent years. The Internet of …

Malware detection issues, challenges, and future directions: A survey

FA Aboaoja, A Zainal, FA Ghaleb, BAS Al-Rimy… - Applied Sciences, 2022 - mdpi.com
The evolution of recent malicious software with the rising use of digital services has
increased the probability of corrupting data, stealing information, or other cybercrimes by …

Intelligent dynamic malware detection using machine learning in IP reputation for forensics data analytics

N Usman, S Usman, F Khan, MA Jan, A Sajid… - Future Generation …, 2021 - Elsevier
In the near future, objects have to connect with each other which can result in gathering
private sensitive data and cause various security threats and cyber crimes. To prevent cyber …

Dynamic analysis for IoT malware detection with convolution neural network model

J Jeon, JH Park, YS Jeong - Ieee Access, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected
society where all things are connected and exchange information through the Internet. IoT …

[HTML][HTML] Information security breaches due to ransomware attacks-a systematic literature review

TR Reshmi - International Journal of Information Management Data …, 2021 - Elsevier
Ransomware is the most predominant cyber threat in the digital infrastructure. The attackers
launching ransomware attacks use different techniques to hijack the users' or organizations' …

Tight arms race: Overview of current malware threats and trends in their detection

L Caviglione, M Choraś, I Corona, A Janicki… - IEEE …, 2020 - ieeexplore.ieee.org
Cyber attacks are currently blooming, as the attackers reap significant profits from them and
face a limited risk when compared to committing the “classical” crimes. One of the major …

Improved harris hawks optimization using elite opposition-based learning and novel search mechanism for feature selection

R Sihwail, K Omar, KAZ Ariffin, M Tubishat - Ieee Access, 2020 - ieeexplore.ieee.org
The rapid increase in data volume and features dimensionality have a negative influence on
machine learning and many other fields, such as decreasing classification accuracy and …

Malware detection using memory analysis data in big data environment

M Dener, G Ok, A Orman - Applied Sciences, 2022 - mdpi.com
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 …

Deceiving AI-based malware detection through polymorphic attacks

C Catalano, A Chezzi, M Angelelli, F Tommasi - Computers in Industry, 2022 - Elsevier
Malware detection is one of the most important tasks in cybersecurity. Recently, increasing
interest in Convolutional Neural Networks (CNN) and Machine Learning algorithms, which …

An adaptive behavioral-based incremental batch learning malware variants detection model using concept drift detection and sequential deep learning

AA Darem, FA Ghaleb, AA Al-Hashmi… - IEEE …, 2021 - ieeexplore.ieee.org
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 …