yuhe-Ray's Stars
hunterakins/pyat
Provide Python functions for reading and writing the files required for the Acoustics Toolbox programs (KRAKEN, BELLHOP, SCOOTER). Also defines objects that represent the basic elements of an acoustic simulation.
bensherlock/nm3-python-driver
NM3 Acoustic Underwater Modem Python Driver, Logger, Network Simulator, and Example Code
antoineppm/uasn-simulation
Simulations of Underwater Acoustic Sensor Networks localization techniques
huawei-noah/trustworthyAI
Trustworthy AI related projects
nilsmorozs/uwa-channel-model
Underwater acoustic network modelling based on BELLHOP
jackfrued/Python-for-Freshmen-2023
harshilpatel1799/IoT-Network-Intrusion-Detection-and-Classification-using-Explainable-XAI-Machine-Learning
The continuing increase of Internet of Things (IoT) based networks have increased the need for Computer networks intrusion detection systems (IDSs). Over the last few years, IDSs for IoT networks have been increasing reliant on machine learning (ML) techniques, algorithms, and models as traditional cybersecurity approaches become less viable for IoT. IDSs that have developed and implemented using machine learning approaches are effective, and accurate in detecting networks attacks with high-performance capabilities. However, the acceptability and trust of these systems may have been hindered due to many of the ML implementations being ‘black boxes’ where human interpretability, transparency, explainability, and logic in prediction outputs is significantly unavailable. The UNSW-NB15 is an IoT-based network traffic data set with classifying normal activities and malicious attack behaviors. Using this dataset, three ML classifiers: Decision Trees, Multi-Layer Perceptrons, and XGBoost, were trained. The ML classifiers and corresponding algorithm for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets proved to be very high-performing based on model performance accuracies. Thereafter, established Explainable AI (XAI) techniques using Scikit-Learn, LIME, ELI5, and SHAP libraries allowed for visualizations of the decision-making frameworks for the three classifiers to increase explainability in classification prediction. The results determined XAI is both feasible and viable as cybersecurity experts and professionals have much to gain with the implementation of traditional ML systems paired with Explainable AI (XAI) techniques.