Cyber risk and cybersecurity: a systematic review of data availability

F Cremer, B Sheehan, M Fortmann… - The Geneva papers …, 2022 - pmc.ncbi.nlm.nih.gov
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …

Fuzzy logic-based DDoS attacks and network traffic anomaly detection methods: Classification, overview, and future perspectives

D Javaheri, S Gorgin, JA Lee, M Masdari - Information Sciences, 2023 - Elsevier
Nowadays, cybersecurity challenges and their ever-growing complexity are the main
concerns for various information technology-driven organizations and companies. Although …

Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: A systematic survey

N Talpur, SJ Abdulkadir, H Alhussian… - Artificial intelligence …, 2023 - Springer
Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

MP Novaes, LF Carvalho, J Lloret… - Future Generation …, 2021 - Elsevier
Over the last few years, Software Defined Networking (SDN) paradigm has become an
emerging architecture to design future networks and to meet new application demands. SDN …

[HTML][HTML] Dependable intrusion detection system using deep convolutional neural network: A novel framework and performance evaluation approach

V Hnamte, J Hussain - Telematics and Informatics Reports, 2023 - Elsevier
Intrusion detection systems (IDS) play a critical role in safeguarding computer networks
against unauthorized access and malicious activities. However, traditional IDS approaches …

Optimization enabled deep learning‐based ddos attack detection in cloud computing

S Balasubramaniam, C Vijesh Joe… - … Journal of Intelligent …, 2023 - Wiley Online Library
Cloud computing is a vast revolution in information technology (IT) that inhibits scalable and
virtualized sources to end users with low infrastructure cost and maintenance. They also …

Cloud security based attack detection using transductive learning integrated with Hidden Markov Model

Y Aoudni, C Donald, A Farouk, KB Sahay… - Pattern recognition …, 2022 - Elsevier
In recent years, organizations and enterprises put huge attention on their network security.
The attackers were able to influence vulnerabilities for the configuration of the network …

A comprehensive review of deep neuro-fuzzy system architectures and their optimization methods

N Talpur, SJ Abdulkadir, H Alhussian… - Neural Computing and …, 2022 - Springer
Deep neuro-fuzzy systems (DNFSs) have been successfully applied to real-world problems
using the efficient learning process of deep neural networks (DNNs) and reasoning aptitude …

Distributed denial of service attacks in cloud: State-of-the-art of scientific and commercial solutions

A Bhardwaj, V Mangat, R Vig, S Halder… - Computer Science Review, 2021 - Elsevier
Cloud computing model provides on demand, elastic and fully managed computer system
resources and services to organizations. However, attacks on cloud components can cause …

Cloud network anomaly detection using machine and deep learning techniques-recent research advancements

A Abdallah, A Alkaabi, G Alameri, SH Rafique… - IEEE …, 2024 - ieeexplore.ieee.org
In the rapidly evolving landscape of computing and networking, the concepts of cloud
networks have gained significant prominence. Although the cloud network offers on-demand …