DDoS attack detection and mitigation using SDN: methods, practices, and solutions
Distributed denial-of-service (DDoS) attacks have become a weapon of choice for hackers,
cyber extortionists, and cyber terrorists. These attacks can swiftly incapacitate a victim …
cyber extortionists, and cyber terrorists. These attacks can swiftly incapacitate a victim …
Botnet in DDoS attacks: trends and challenges
N Hoque, DK Bhattacharyya… - … Surveys & Tutorials, 2015 - ieeexplore.ieee.org
Threats of distributed denial of service (DDoS) attacks have been increasing day-by-day due
to rapid development of computer networks and associated infrastructure, and millions of …
to rapid development of computer networks and associated infrastructure, and millions of …
Developing realistic distributed denial of service (DDoS) attack dataset and taxonomy
Distributed Denial of Service (DDoS) attack is a menace to network security that aims at
exhausting the target networks with malicious traffic. Although many statistical methods have …
exhausting the target networks with malicious traffic. Although many statistical methods have …
Machine learning approaches for combating distributed denial of service attacks in modern networking environments
A Aljuhani - IEEE Access, 2021 - ieeexplore.ieee.org
A distributed denial of service (DDoS) attack represents a major threat to service providers.
More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by …
More specifically, a DDoS attack aims to disrupt and deny services to legitimate users by …
A game-theoretical approach for mitigating edge DDoS attack
Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing
computing resources onto edge servers that are attached to base stations or access points …
computing resources onto edge servers that are attached to base stations or access points …
FLEAM: A federated learning empowered architecture to mitigate DDoS in industrial IoT
Due to resource constraints and working surroundings, many IIoT nodes are easily hacked
and turn into zombies from which to launch attacks. It is challenging to detect such …
and turn into zombies from which to launch attacks. It is challenging to detect such …
Multi-task network anomaly detection using federated learning
Because of the complexity of network traffic, there are various significant challenges in the
network anomaly detection fields. One of the major challenges is the lack of labeled training …
network anomaly detection fields. One of the major challenges is the lack of labeled training …
An efficient reinforcement learning-based Botnet detection approach
The use of bot malware and botnets as a tool to facilitate other malicious cyber activities (eg
distributed denial of service attacks, dissemination of malware and spam, and click fraud) …
distributed denial of service attacks, dissemination of malware and spam, and click fraud) …
A system for denial-of-service attack detection based on multivariate correlation analysis
Interconnected systems, such as Web servers, database servers, cloud computing servers
and so on, are now under threads from network attackers. As one of most common and …
and so on, are now under threads from network attackers. As one of most common and …
TSCRNN: A novel classification scheme of encrypted traffic based on flow spatiotemporal features for efficient management of IIoT
Abstract In the Industrial Internet of Things (IIoT) in the 5G era, the growth of smart devices
will generate a large amount of data traffic, bringing a huge challenge of network traffic …
will generate a large amount of data traffic, bringing a huge challenge of network traffic …