Privacy-preserving machine learning based data analytics on edge devices
Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely
used in today's applications and services. However, with social awareness of privacy and …
used in today's applications and services. However, with social awareness of privacy and …
Network intrusion detection using word embeddings
Word embeddings, learning syntactic and semantic relationships between words from the
raw text, are known to achieve superior performance in many prediction and classification …
raw text, are known to achieve superior performance in many prediction and classification …
A Time Series Clustering Method for Network Big Data
In the era of big data, network data increase rapidly in a distributed manner, giving birth to
the network big data. Network big data with the extra features such as distributed and …
the network big data. Network big data with the extra features such as distributed and …
[PDF][PDF] A Fine-Grained Hardware Security Approach for Runtime Code Integrity in Embedded Systems.
Embedded systems are subjected to various adversaries including software attacks,
physical attacks, and side channel attacks. Most of these malicious attacks can lead to the …
physical attacks, and side channel attacks. Most of these malicious attacks can lead to the …
A survey of big data and computational intelligence in networking
Networking has become an indispensable part of the modern world, providing convenient
access to the Internet, remote communication, and information exchange. Big data also has …
access to the Internet, remote communication, and information exchange. Big data also has …
User-centric composable services: A new generation of personal data analytics
Machine Learning (ML) techniques, such as Neural Network, are widely used in today's
applications. However, there is still a big gap between the current ML systems and users' …
applications. However, there is still a big gap between the current ML systems and users' …