215 skills found · Page 1 of 8
vinayakumarr / Network Intrusion DetectionNetwork Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
vaneenige / Unswitch🕹 A tiny event handler for Switch controllers!
alik604 / Cyber SecurityMachine Learning for Network Intrusion Detection & Misc Cyber Security Utilities
abhinav-bhardwaj / IoT Network Intrusion Detection System UNSW NB15Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
InvolutionHell / InvolutionhellA website of study resource, full-open-source. 内卷地狱网站仓库
SubrataMaji / IDS UNSW NB15Building an Intrusion Detection System on UNSW-NB15 Dataset Based on Machine Learning Algorithm
InitRoot / UNSW NB15Feature coded UNSW_NB15 intrusion detection data.
harshilpatel1799 / Iot Cyber Security With Machine Learning Research ProjectIoT networks have become an increasingly valuable target of malicious attacks due to the increased amount of valuable user data they contain. In response, network intrusion detection systems have been developed to detect suspicious network activity. UNSW-NB15 is an IoT-based network traffic data set with different categories for normal activities and malicious attack behaviors. UNSW-NB15 botnet datasets with IoT sensors' data are used to obtain results that show that the proposed features have the potential characteristics of identifying and classifying normal and malicious activity. Role of ML algorithms is for developing a network forensic system based on network flow identifiers and features that can track suspicious activities of botnets is possible. The ML model metrics using the UNSW-NB15 dataset revealed that ML techniques with flow identifiers can effectively and efficiently detect botnets’ attacks and their tracks.
FlamingJay / Time Related Intrusion Detection Model Based On Recurrent Neural NetworkHere, we use RNN to deal with the network intrusion problem. The UNSW-NB15 dataset is used.
devsoc-unsw / NotanglesThe best tool to plan your weekly UNSW timetable with friends. Super easy, customisable, drag and drop. Now with autotimetabling and custom event creation! Social timetabling coming soon.
DBAIWangGroup / COMP9318COMP9318 (Data Warehousing and Data Mining) @ UNSW
devsoc-unsw / CirclesThe open-source degree planner for UNSW students. Features an interactive drag-and-drop interface for easy term planning and automatic progression checking to help you stay on track for graduation.
harshilpatel1799 / IoT Network Intrusion Detection And Classification Using Explainable XAI Machine LearningThe 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.
YunqiuXu / UNSWCourse materials
zhmhbest / Python Nidsdata这是一个封装了KDDCup99、NSL-KDD、UNSW-NB15等入侵监测数据集的Python包。
TGyAlDeen / IDS UNSW NB15IoT intrusion Detection Model based on neural network and random forests
CatOn60Hz / Real Time Network Traffic Classifier IDSThis project develops and deploys a robust, multi-class Network Intrusion Detection System (IDS) capable of identifying various attack types and normal network traffic. Leveraging a 1D Convolutional Neural Network (CNN) architecture, the system is trained on the comprehensive UNSW-NB15 dataset, which features a wide range of modern attacks.
daya0576 / Unsw.coUNSW CSE Course Reviews
gakkistyle / Comp9021UNSW COMP9021 Principles of Programming
OltanS / UR5e Touch Haptic TeleoperationThis repository was created as a part of an mechatronic engineering honours thesis at the University of New South Wales (UNSW). This repository includes files that relate to teleoperating a Universal Robots E-series robot via a 3Dsystems Touch haptic device with haptic feedback for the purposes of improving remote ultrasounds.
