A survey on the role of artificial intelligence, machine learning and deep learning for cybersecurity attack detection

A Salih, ST Zeebaree, S Ameen… - … & Innovation amid …, 2021 - ieeexplore.ieee.org
With the growing internet services, cybersecurity becomes one of the major research
problems of the modern digital era. Cybersecurity involves techniques to protect and control …

ML-based IDPS enhancement with complementary features for home IoT networks

P Illy, G Kaddoum, K Kaur… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The Internet of Things (IoT) networks are obstructed by security vulnerabilities that hackers
can leverage to operate intrusions in many environments, such as smart homes, smart …

RPL rank attack detection using Deep Learning

W Choukri, H Lamaazi… - … Conference on Innovation …, 2020 - ieeexplore.ieee.org
Internet of Things (IoT) is a network of interconnected smart devices. It provides a set of
services in different domains to improve the quality of human daily life. However, protecting …

Artificial intelligence algorithms for cyberspace security applications: a technological and status review

J Chen, D Wu, R Xie - Frontiers of Information Technology & Electronic …, 2023 - Springer
Three technical problems should be solved urgently in cyberspace security: the timeliness
and accuracy of network attack detection, the credibility assessment and prediction of the …

Cybersecurity Alert Prioritization in a Critical High Power Grid With Latent Spaces

JR Feijoo-Martínez, A Guerrero-Curieses… - IEEE …, 2023 - ieeexplore.ieee.org
High-Power electric grid networks require extreme security in their associated
telecommunication network to ensure protection and control throughout power transmission …

Effective Cyber Attack Detection in an IoMT-Smart System using Deep Convolutional Neural Networks and Machine Learning Algorithms

N Ram, D Kumar - 2022 Second International Conference on …, 2022 - ieeexplore.ieee.org
The widespread availability of digital technology has reshaped the computing environment.
being just one of them. pear phishing, impersonating, distributed denial of service stolen …

[HTML][HTML] AI-driven solutions for safeguarding IoT environments: an intrusion detection and prevention study

P Illy - 2024 - espace.etsmtl.ca
Les progrès réalisés ces dernières années dans le domaine des technologies de
l'information et de la communication (TIC) ont donné naissances à des nouveaux concepts …

A Deep Learning Approach for Sustainable Ad Hoc Vehicular Network

SS Thorat, DV Rojatkar, PR Deshmukh - International Conference on …, 2024 - Springer
In the era of autonomous vehicles and Google cars, Vehicular Ad Hoc networks are slowly
and steadily becoming a reality. V2V and V2I are two prominent and important variants of …

[PDF][PDF] A robust Gradient boosting model based on SMOTE and NEAR MISS methods for intrusion detection in imbalanced data sets

AO ARIK - 2022 - researchgate.net
Novel technologies cause many security vulnerabilities and zero-day attack risks. Intrusion
Detection Systems (IDS) are developed to protect computer networks from threats and …

Intrusion Detection System using Autoencoder based Deep Neural Network for SME Cybersecurity

KA Ubaidillah, SI Hisham, F Ernawan… - … on Informatics and …, 2021 - ieeexplore.ieee.org
This paper proposes an intermediate solution using artificial intelligence to monitor any
potential threat for SME, specifically in Malaysia. The proposed method uses Autoencoder …