6g-enabled consumer electronics device intrusion detection with federated meta-learning and digital twins in a meta-verse environment
The widespread adoption of consumer electronics devices coupled with the emergence of
6G technology has led to the establishment of an extensive network of interconnected …
6G technology has led to the establishment of an extensive network of interconnected …
MalNet: A large-scale image database of malicious software
Computer vision is playing an increasingly important role in automated malware detection
with the rise of the image-based binary representation. These binary images are fast to …
with the rise of the image-based binary representation. These binary images are fast to …
Training unbiased diffusion models from biased dataset
With significant advancements in diffusion models, addressing the potential risks of dataset
bias becomes increasingly important. Since generated outputs directly suffer from dataset …
bias becomes increasingly important. Since generated outputs directly suffer from dataset …
AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Security
Security operation centers contend with a constant stream of security incidents, ranging from
straightforward to highly complex. To address this, we developed Copilot Guided Response …
straightforward to highly complex. To address this, we developed Copilot Guided Response …
Towards regression-free neural networks for diverse compute platforms
Our work tackles the emergent problem of reducing predictive inconsistencies arising as
negative flips: test samples that are correctly predicted by a less accurate model, but …
negative flips: test samples that are correctly predicted by a less accurate model, but …
Web Scale Graph Mining for Cyber Threat Intelligence
Defending against today's increasingly sophisticated and large-scale cyberattacks demands
accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate …
accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate …
An Efficient NAS-based Approach for Handling Imbalanced Datasets
Z Yao - arxiv preprint arxiv:2406.16972, 2024 - arxiv.org
Class imbalance is a common issue in real-world data distributions, negatively impacting the
training of accurate classifiers. Traditional approaches to mitigate this problem fall into three …
training of accurate classifiers. Traditional approaches to mitigate this problem fall into three …
IMB-NAS: Neural Architecture Search for Imbalanced Datasets
Class imbalance is a ubiquitous phenomenon occurring in real world data distributions. To
overcome its detrimental effect on training accurate classifiers, existing work follows three …
overcome its detrimental effect on training accurate classifiers, existing work follows three …
Building Visual Malware Dataset using VirusShare Data and Comparing Machine Learning Baseline Model to CoAtNet for Malware Classification
The present work takes inspiration from the work of Zihang Dai, Hanxiao Liu, Quoc V. Le,
Mingxing Tan at Google Research, Brain Team about CoAtNet. In that work it was showed …
Mingxing Tan at Google Research, Brain Team about CoAtNet. In that work it was showed …
Dual-Path Framework for Intra-Class Imbalance Medical Image Segmentation
X Lin, B Yang, Y Zhou, R Higashita… - 2023 IEEE 20th …, 2023 - ieeexplore.ieee.org
The intra-class imbalance usually occurs in medical images due to external influences, such
as noise interference and changes in camera angle. It leads to complex textures and varied …
as noise interference and changes in camera angle. It leads to complex textures and varied …