A survey of machine and deep learning methods for internet of things (IoT) security

MA Al-Garadi, A Mohamed, AK Al-Ali… - … surveys & tutorials, 2020 - ieeexplore.ieee.org
The Internet of Things (IoT) integrates billions of smart devices that can communicate with
one another with minimal human intervention. IoT is one of the fastest develo** fields in …

Deep learning for zero-day malware detection and classification: A survey

F Deldar, M Abadi - ACM Computing Surveys, 2023 - dl.acm.org
Zero-day malware is malware that has never been seen before or is so new that no anti-
malware software can catch it. This novelty and the lack of existing mitigation strategies …

Survey of machine learning techniques for malware analysis

D Ucci, L Aniello, R Baldoni - Computers & Security, 2019 - Elsevier
Co** with malware is getting more and more challenging, given their relentless growth in
complexity and volume. One of the most common approaches in literature is using machine …

Fog computing security: a review of current applications and security solutions

S Khan, S Parkinson, Y Qin - Journal of Cloud Computing, 2017 - Springer
Fog computing is a new paradigm that extends the Cloud platform model by providing
computing resources on the edges of a network. It can be described as a cloud-like platform …

A survey of random forest based methods for intrusion detection systems

PAA Resende, AC Drummond - ACM Computing Surveys (CSUR), 2018 - dl.acm.org
Over the past decades, researchers have been proposing different Intrusion Detection
approaches to deal with the increasing number and complexity of threats for computer …

Machine learning for wireless communications in the Internet of Things: A comprehensive survey

J Jagannath, N Polosky, A Jagannath, F Restuccia… - Ad Hoc Networks, 2019 - Elsevier
Abstract The Internet of Things (IoT) is expected to require more effective and efficient
wireless communications than ever before. For this reason, techniques such as spectrum …

Malware classification and composition analysis: A survey of recent developments

A Abusitta, MQ Li, BCM Fung - Journal of Information Security and …, 2021 - Elsevier
Malware detection and classification are becoming more and more challenging, given the
complexity of malware design and the recent advancement of communication and …

MalFCS: An effective malware classification framework with automated feature extraction based on deep convolutional neural networks

G **ao, J Li, Y Chen, K Li - Journal of Parallel and Distributed Computing, 2020 - Elsevier
Identifying the family of malware can determine their malicious intent and attack patterns,
which helps to efficiently analyze large numbers of malware variants. Methods based on …

Machine learning for security and the internet of things: the good, the bad, and the ugly

F Liang, WG Hatcher, W Liao, W Gao, W Yu - Ieee Access, 2019 - ieeexplore.ieee.org
The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …

AMAL: high-fidelity, behavior-based automated malware analysis and classification

A Mohaisen, O Alrawi, M Mohaisen - computers & security, 2015 - Elsevier
This paper introduces AMAL, an automated and behavior-based malware analysis and
labeling system that addresses shortcomings of the existing systems. AMAL consists of two …