Lightweight machine learning framework for efficient DDoS attack detection in IoT networks. [PDF]
Nawaz M +4 more
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Hyperparameter optimization of XGBoost and hybrid CnnSVM for cyber threat detection using modified Harris hawks algorithm. [PDF]
Elwahsh H +7 more
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Deep memory for deep threats: A novel architecture combining GRUs and deep learning models for IDS. [PDF]
Alqhatani A +5 more
europepmc +1 more source
A privacy preserving intrusion detection framework for IIoT in 6G networks using homomorphic encryption and graph neural networks. [PDF]
Hua B, Xi H.
europepmc +1 more source
NIDD-enabled lightweight intrusion detection for effective DDoS mitigation in 5G and beyond. [PDF]
Javid I +6 more
europepmc +1 more source
An intelligent deep representation learning with enhanced feature selection approach for cyberattack detection in internet of things enabled cloud environment. [PDF]
Alamro H +7 more
europepmc +1 more source
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Detection of DDoS attacks with feed forward based deep neural network model
Expert Systems With Applications, 2021As a result of the increase in the services provided over the internet, it is seen that the network infrastructure is more exposed to cyber attacks.
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A taxonomy of DDoS attack and DDoS defense mechanisms
ACM SIGCOMM Computer Communication Review, 2004Distributed denial-of-service (DDoS) is a rapidly growing problem. The multitude and variety of both the attacks and the defense approaches is overwhelming. This paper presents two taxonomies for classifying attacks and defenses, and thus provides researchers with a better understanding of the problem and the current solution space.
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A comprehensive study of DDoS attacks over IoT network and their countermeasures
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Nondeterminism complicates the development and management of distributed systems, and arises from two main sources: the local behavior of each individual node as well as the behavior of the network connecting them. Taming nondeterminism effectively requires dealing with both sources.
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