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A survey of machine and deep learning methods for internet of things (IoT) security
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 …
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
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 …
malware software can catch it. This novelty and the lack of existing mitigation strategies …
Survey of machine learning techniques for malware analysis
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 …
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
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 …
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
Over the past decades, researchers have been proposing different Intrusion Detection
approaches to deal with the increasing number and complexity of threats for computer …
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
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 …
wireless communications than ever before. For this reason, techniques such as spectrum …
Malware classification and composition analysis: A survey of recent developments
Malware detection and classification are becoming more and more challenging, given the
complexity of malware design and the recent advancement of communication and …
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
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 …
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
The advancement of the Internet of Things (IoT) has allowed for unprecedented data
collection, automation, and remote sensing and actuation, transforming autonomous …
collection, automation, and remote sensing and actuation, transforming autonomous …
AMAL: high-fidelity, behavior-based automated malware analysis and classification
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 …
labeling system that addresses shortcomings of the existing systems. AMAL consists of two …