News

IEEE18d
Comparison of Machine Learning Models for Identifying Malicious URLs
This study explores various machine learning techniques to accurately identify malicious URLs. Decision trees, random forests, K-nearest neighbors (KNN), and naive Bayes models are assessed using a ...
GitHub23d
Pull requests: BemuelTowett/MALICIOUS-URL-DETECTION-USING-MACHINE-LEARNING
Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.
Infosecurity-magazine.com24d
Malicious Machine Learning Model Attack Discovered on PyPI
A new campaign exploiting machine learning (ML) models via the Python Package Index (PyPI) has been observed by cybersecurity researchers. ReversingLabs said threat actors are using the Pickle ...
IEEE26d
Accuracy enhancement in Detection of Malicious Social Bots Using Reinforcement Learning Technique with URL Features in Twitter Network Through Convolutional Neural Network over ...
Abstract: This research aims to use Twitter's ... K-nearest neighbor (KNN) algorithm, to more effectively identify harmful social bots via reinforcement learning. A reinforcement learning method is ...
C&EN29d
Exposome-Scale Investigation of Cl-/Br-Containing Chemicals Using High-Resolution Mass Spectrometry, Multistage Machine Learning, and Cloud Computing
Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, British Columbia V6T 1Z1, Canada ...