In this thesis, we explore the idea of using binary visualization and image analysis to detect anomalous activity on an Industrial Internet of Things (IIoT) based network. The data is gathered into a pcap file and then fed into our encoder, which uses a space-filling curve to convert the 1-dimensional stream of data into pixels with a specific red, blue, and green gradient value. The pixels create an image which is then given to an image analysis system based on a Convolutional Neural Network, which classifies if the traffic supplied is malicious or not. The results show that using a Binary and Multiclass classifier approach to the image analysis both work well reaching an accuracy of 100% and 94% respectively. While the binary classifier is more accurate both succeed at separating Malicious from Benign traffic. The choice of space-filling curves in our binary visualization ended up having little to no impact on overall classification accuracy.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hh-47186 |
| Date | January 2022 |
| Creators | Hjerpe, Joar, Karlsson, Oliver |
| Publisher | Högskolan i Halmstad, Akademin för informationsteknologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0024 seconds