Security Boulevard

NDSS 2025 – Defending Against Backdoor Attacks On Graph Neural Networks Via Discrepancy Learning

Hao Yu (National University of Defense Technology), Chuan Ma (Chongqing University), Xinhang Wan (National University of Defense Technology), Jun Wang (National University of Defense Technology), Tao ...
Scientific Research Publishing

Kipf, T. and Welling, M. (2016) Semi-Supervised Classification with Graph Convolutional Networks. arXiv: 1609.02907.

ABSTRACT: Foot-and-Mouth Disease (FMD) remains a critical threat to global livestock industries, causing severe economic losses and trade restrictions. This paper proposes a novel application of ...
IEEE

FedSSL-NTC: A Robust Federated Self-Supervised Learning Framework for Network Traffic Classification Under Privacy Constraints

Abstract: Network traffic classification (NTC) plays an essential role in managing, securing, and optimizing networks. Supervised learning methods face challenges such as label scarcity. Given that ...
IEEE

Network Traffic Classification Using Self-Supervised Learning and Confident Learning

Abstract: Network traffic classification (NTC) is vital for efficient network management, security, and performance optimization, particularly with 5G/6G technologies. Traditional methods, such as ...
Frontiers

Self-supervised learning analysis of multi-FISH labeled cell-type map in thick brain slices

Accurate mapping of the spatial distribution of diverse cell types is essential for understanding the cellular organization of brain. However, the cellular heterogeneity and the substantial cost of ...
Scientific Research Publishing

Sen, P.C., Hajra, M. and Ghosh, M. (2019) Supervised Classification Algorithms in Machine Learning: A Survey and Review. In: Mandal, J.K. and Bhattacharya, D., Eds., Emerging ...

ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...
GitHub

classification-supervised-learning

A machine learning project to classify Iris flower species (Setosa, Versicolor, Virginica) using physical features. Implemented using Jupyter, Flask, Streamlit, and JavaScript. Part of CodSoft ...