Learning to Learn Sequential Network Attacks Using Hidden Markov Models

Learning to Learn Sequential Network Attacks Using Hidden Markov Models

The global surge of cyber-attacks in the form of sequential network attacks has propelled the need for robust intrusion detection and prediction systems. Such attacks are difficult to reveal using current intrusion detection systems, since each individual attack phase may appear benign when examined...

Full description

Bibliographic Details
Main Authors: Timothy Chadza, Konstantinos G. Kyriakopoulos, Sangarapillai Lambotharan
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Transfer learning
hidden Markov model
Viterbi decoding
forward-backward
sequential network attacks
Online Access:https://ieeexplore.ieee.org/document/9146155/

Internet

https://ieeexplore.ieee.org/document/9146155/

Similar Items