Machine learning-based botnet detection in software-defined network: A systematic review
In recent decades, the internet has grown and changed the world tremendously, and this, in
turn, has brought about many cyberattacks. Cybersecurity represents one of the most …
turn, has brought about many cyberattacks. Cybersecurity represents one of the most …
APT beaconing detection: A systematic review
Abstract Advanced Persistent Threat (APT) is a type of threat that has grabbed the attention
of researchers, particularly in the industrial security field. APTs are cyber intrusions carried …
of researchers, particularly in the industrial security field. APTs are cyber intrusions carried …
A two-fold machine learning approach to prevent and detect IoT botnet attacks
The botnet attack is a multi-stage and the most prevalent cyber-attack in the Internet of
Things (IoT) environment that initiates with scanning activity and ends at the distributed …
Things (IoT) environment that initiates with scanning activity and ends at the distributed …
Real-time botnet detection on large network bandwidths using machine learning
Botnets are one of the most harmful cyberthreats, that can perform many types of
cyberattacks and cause billionaire losses to the global economy. Nowadays, vast amounts …
cyberattacks and cause billionaire losses to the global economy. Nowadays, vast amounts …
A survey on botnets: Incentives, evolution, detection and current trends
SN Thanh Vu, M Stege, PI El-Habr, J Bang, N Dragoni - Future Internet, 2021 - mdpi.com
Botnets, groups of malware-infected hosts controlled by malicious actors, have gained
prominence in an era of pervasive computing and the Internet of Things. Botnets have …
prominence in an era of pervasive computing and the Internet of Things. Botnets have …
An aggregated mutual information based feature selection with machine learning methods for enhancing IoT botnet attack detection
Due to the wide availability and usage of connected devices in Internet of Things (IoT)
networks, the number of attacks on these networks is continually increasing. A particularly …
networks, the number of attacks on these networks is continually increasing. A particularly …
Botnet detection using negative selection algorithm, convolution neural network and classification methods
S Hosseini, AE Nezhad, H Seilani - Evolving Systems, 2022 - Springer
Botnet is a network and internet risk. It is necessary to detect botnet by analyzing and
monitoring in order to quickly prevent them. Most approaches are proposed to detect bots …
monitoring in order to quickly prevent them. Most approaches are proposed to detect bots …
AI-based botnet attack classification and detection in IoT devices
V Puri, A Kataria, VK Solanki… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
End-user Internet of Things (IoT) devices, including security cameras, smart appliances,
home monitors, and thermostats, are becoming more prevalent in households. Additionally …
home monitors, and thermostats, are becoming more prevalent in households. Additionally …
[PDF][PDF] Detection of autism spectrum disorder using a 1-dimensional convolutional neural network
Abstract Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease
that impairs speech, social interaction, and behavior. Machine learning is a field of artificial …
that impairs speech, social interaction, and behavior. Machine learning is a field of artificial …
DEMD-IoT: a deep ensemble model for IoT malware detection using CNNs and network traffic
Malware detection has recently emerged as a significant challenge on the Internet of Things
(IoT) security domain. Due to the increasing complexity and variety of malware, the demand …
(IoT) security domain. Due to the increasing complexity and variety of malware, the demand …