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A Survey of Machine Learning Algorithms for Detecting Malware in IoT Firmware
This work explores the use of machine learning techniques on an Internet-of-Things firmware dataset to detect malicious attempts to infect edge devices or subsequently corrupt an entire network.
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification
Cyber security can be enhanced through application of machine learning by recasting network attack data into an image format, then applying supervised computer vision and other machine learning techniques to detect malicious specimens.
Virus-MNIST: Machine Learning Baseline Calculations for Image Classification
The Virus-MNIST data set is a collection of thumbnail images that is similar in style to the ubiquitous MNIST hand-written digits.
Predicting Solar Flares with Remote Sensing and Machine Learning
High energy solar flares and coronal mass ejections have the potential to destroy Earth's ground and satellite infrastructures, causing trillions of dollars in damage and mass human suffering.
A Survey of Machine Learning Algorithms for Detecting Ransomware Encryption Activity
A survey of machine learning techniques trained to detect ransomware is presented.
Overhead-MNIST: Machine Learning Baselines for Image Classification
Twenty-three machine learning algorithms were trained then scored to establish baseline comparison metrics and to select an image classification algorithm worthy of embedding into mission-critical satellite imaging systems.
