A unified deep learning anomaly detection and classification approach for smart grid environments
I Siniosoglou, P Radoglou-Grammatikis… - … on Network and …, 2021 - ieeexplore.ieee.org
The interconnected and heterogeneous nature of the next-generation Electrical Grid (EG),
widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also …
widely known as Smart Grid (SG), bring severe cybersecurity and privacy risks that can also …
Attack graph model for cyber-physical power systems using hybrid deep learning
Electrical power grids are vulnerable to cyber attacks, as seen in Ukraine in 2015 and 2016.
However, existing attack detection methods are limited. Most of them are based on power …
However, existing attack detection methods are limited. Most of them are based on power …
A comprehensive survey on low-rate and high-rate DDoS defense approaches in SDN: Taxonomy, research challenges, and opportunities
Abstract Software Defined Networking (SDN) expands the networking capabilities using
abstraction, open-source protocols, energy efficiency, and programmable features for …
abstraction, open-source protocols, energy efficiency, and programmable features for …
Machine-learning-enabled ddos attacks detection in p4 programmable networks
Abstract Distributed Denial of Service (DDoS) attacks represent a major concern in modern
Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in …
Software Defined Networking (SDN), as SDN controllers are sensitive points of failures in …
Deep learning for the security of software-defined networks: a review
As the scale and complexity of networks grow rapidly, management, maintenance, and
optimization of them are becoming increasingly challenging tasks for network administrators …
optimization of them are becoming increasingly challenging tasks for network administrators …
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 …
Federated deep reinforcement learning for traffic monitoring in SDN-based IoT networks
This paper proposes a novel traffic monitoring framework, namely, DeepMonitor, for SDN-
based IoT networks to provide fine-grained traffic analysis capability for different IoT traffic …
based IoT networks to provide fine-grained traffic analysis capability for different IoT traffic …
Performance and features: Mitigating the low-rate TCP-targeted DoS attack via SDN
D Tang, Y Yan, S Zhang, J Chen… - IEEE Journal on Selected …, 2021 - ieeexplore.ieee.org
Software-Defined Networking (SDN) is an emerging network architecture. The decoupled
data and control plane provides programmability for efficient network management …
data and control plane provides programmability for efficient network management …
Deep Reinforcement Learning for intrusion detection in Internet of Things: Best practices, lessons learnt, and open challenges
Abstract The Internet of Things (IoT) scenario places important challenges even for deep
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …
The DAG blockchain: A secure edge assisted honeypot for attack detection and multi-controller based load balancing in SDN 5G
IH Abdulqadder, D Zou, IT Aziz - Future Generation Computer Systems, 2023 - Elsevier
Software-defined networking (SDN) has increased the need for security due to the
participation of illegitimate packets resulting from poor processing times and inadequate …
participation of illegitimate packets resulting from poor processing times and inadequate …