wtospit Goto Github PK

followers: 12.0 following: 12.0 repos: 86.0 gists: 7.0

Name: Witaya Tospitakkul

Type: User

Company: Odd-e (Thailand)

Bio: ODDS| Someone who loves coding....

Witaya Tospitakkul's Projects

hacktoberfest-2k17

Official Repository for Hacktoberfest 2k17 NITK Edition meetup conducted at NITK Surathkal in collaboration with Team Engineer. Credits to @jenkoian for the Hacktoberfest Checker @rohitvarkey for the PDF for Tuesday's talk!

hacktoberfest-census

A census of those participating in Hacktoberfest (and an easy PR!)

hacktoberfest-firstpr

Making your first contribution

hacktoberfest-sign-in

Beginner friendly. Easy Pull Request! .

hacktoberfest2018

Welcome! I need some help making my website... Learn how in the README

ids-deep-learning

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.