DDoS attack detection in smart grid network using reconstructive machine learning models.

ABSTRACT: Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new a

SUBMITTER: Naqvi SSA 

PROVIDER: S-EPMC10803083 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature