Machine learning techniques in emerging cloud computing integrated paradigms: A survey and taxonomy
Cloud computing offers Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and
Software as a Service (SaaS) to provide compute, network, and storage capabilities to the …
Software as a Service (SaaS) to provide compute, network, and storage capabilities to the …
[HTML][HTML] A comprehensive survey on knowledge-defined networking
PADSN Wijesekara, S Gunawardena - Telecom, 2023 - mdpi.com
Traditional networking is hardware-based, having the control plane coupled with the data
plane. Software-Defined Networking (SDN), which has a logically centralized control plane …
plane. Software-Defined Networking (SDN), which has a logically centralized control plane …
A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning
NM Yungaicela-Naula, C Vargas-Rosales… - Journal of network and …, 2022 - Elsevier
Abstract Distributed Denial-of-Service (DDoS) attacks are difficult to mitigate with existing
defense tools. Fortunately, it has been demonstrated that Software-Defined Networking …
defense tools. Fortunately, it has been demonstrated that Software-Defined Networking …
Towards security automation in software defined networks
NM Yungaicela-Naula, C Vargas-Rosales… - Computer …, 2022 - Elsevier
Abstract Software-Defined Networking (SDN) is a modern paradigm that provides a platform
for implementing reliable, centrally managed, and automated security solutions for …
for implementing reliable, centrally managed, and automated security solutions for …
A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Given the continually rising frequency of cyberattacks, the adoption of artificial intelligence
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
methods, particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement …
DRL-QOR: Deep reinforcement learning-based QoS/QoE-aware adaptive online orchestration in NFV-enabled networks
J Chen, J Chen, H Zhang - IEEE Transactions on Network and …, 2021 - ieeexplore.ieee.org
Faced with fluctuating network traffic and unknown underlying network traffic dynamics,
developing an effective orchestration model with low network cost is still a critical issue in …
developing an effective orchestration model with low network cost is still a critical issue in …
Chronos: Ddos attack detection using time-based autoencoder
MA Salahuddin, V Pourahmadi… - … on Network and …, 2021 - ieeexplore.ieee.org
Cognitive network management is becoming quintessential to realize autonomic networking.
However, the wide spread adoption of the Internet of Things (IoT) devices, increases the risk …
However, the wide spread adoption of the Internet of Things (IoT) devices, increases the risk …
DeepAir: Deep reinforcement learning for adaptive intrusion response in software-defined networks
TV Phan, T Bauschert - IEEE Transactions on Network and …, 2022 - ieeexplore.ieee.org
In this paper, we propose an adaptive intrusion response solution based on deep
reinforcement learning, namely DeepAir, to effectively defend against cyber-attacks in …
reinforcement learning, namely DeepAir, to effectively defend against cyber-attacks in …
Intrusion Detection System in Software-Defined Networks Using Machine Learning and Deep Learning Techniques--A Comprehensive Survey
At present, the Internet is facing numerous attacks of different kinds that put its data at risk.
The safety of information within the network is, therefore, a significant concern. To prevent …
The safety of information within the network is, therefore, a significant concern. To prevent …
[HTML][HTML] Multi-agent reinforcement learning framework in sdn-iot for transient load detection and prevention
The fast emergence of IoT devices and its accompanying big and complex data has
necessitated a shift from the traditional networking architecture to software-defined networks …
necessitated a shift from the traditional networking architecture to software-defined networks …