Detection of malicious domains with concept drift using ensemble learning
PH Chiang, SC Tsai - IEEE Transactions on Network and …, 2024 - ieeexplore.ieee.org
In the current landscape of network technology, it is indisputable that the Domain Name
System (DNS) plays a vital role but also encounters significant security challenges. Despite …
System (DNS) plays a vital role but also encounters significant security challenges. Despite …
Hoeffding adaptive trees for multi-label classification on data streams
Data stream learning is a very relevant paradigm because of the increasing real-world
scenarios generating data at high velocities and in unbounded sequences. Stream learning …
scenarios generating data at high velocities and in unbounded sequences. Stream learning …
Online Drift Detection with Maximum Concept Discrepancy
Continuous learning from an immense volume of data streams becomes exceptionally
critical in the internet era. However, data streams often do not conform to the same …
critical in the internet era. However, data streams often do not conform to the same …
[PDF][PDF] Modyn: Data-Centric Machine Learning Pipeline Orchestration
In real-world machine learning (ML) pipelines, datasets are continuously growing. Models
must incorporate this new training data to improve generalization and adapt to potential …
must incorporate this new training data to improve generalization and adapt to potential …
Research on Concept Drift Handling in Sample Stream Data Based on Deep Neural Networks
H Liu, X Chen, Q Guo, J Yu, X Liu - 2024 5th International …, 2024 - ieeexplore.ieee.org
Stream data, as an important form of big data, is widely used. Due to the continuous
changes in data distribution in stream data, concept drift is prone to occur, which may lead to …
changes in data distribution in stream data, concept drift is prone to occur, which may lead to …