Research on adaptive 1DCNN network intrusion detection technology based on BSGM mixed sampling
W Ma, C Gou, Y Hou - Sensors, 2023 - mdpi.com
The development of internet technology has brought us benefits, but at the same time, there
has been a surge in network attack incidents, posing a serious threat to network security. In …
has been a surge in network attack incidents, posing a serious threat to network security. In …
[PDF][PDF] BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features.
H Huang, X Zhang, Y Lu, Z Li… - Computers, Materials & …, 2024 - cdn.techscience.cn
While encryption technology safeguards the security of network communications, malicious
traffic also uses encryption protocols to obscure its malicious behavior. To address the …
traffic also uses encryption protocols to obscure its malicious behavior. To address the …
Network Intrusion Detection Based on Machine Learning Classification Algorithms: A Review
AH Younis, AM Abdulazeez - JISA (Jurnal Informatika dan Sains), 2024 - trilogi.ac.id
The worldwide internet continues to spread, presenting numerous escalating hazards with
significant potential. Existing static detection systems necessitate frequent updates to …
significant potential. Existing static detection systems necessitate frequent updates to …
[HTML][HTML] A Network Intrusion Detection Method Based on Bagging Ensemble
Z Zhang, S Kong, T Xiao, A Yang - Symmetry, 2024 - mdpi.com
The problems of asymmetry in information features and redundant features in datasets, and
the asymmetry of network traffic distribution in the field of network intrusion detection, have …
the asymmetry of network traffic distribution in the field of network intrusion detection, have …