| Studiju veids |
maģistra akadēmiskās studijas |
| Studiju programmas nosaukums |
Biznesa informātika |
| Nosaukums |
Lietu interneta botu identificēšana, izmantojot reāllaika datu analītiku. |
| Nosaukums angļu valodā |
Internet of Things Bot Detection Using Real-Time Data Analysis |
| Struktūrvienība |
33000 Datorzinātnes, informācijas tehnoloģijas un enerģētikas fakultāte |
| Darba vadītājs |
Rūta Pirta-Dreimane |
| Recenzents |
Andrejs Zujevs |
| Anotācija |
The Internet of Things (IoT) has become a well known concept in the world of technology. The usage of IoT devices have increased within the recent years making it more susceptible to distributed malware and attacks. Securing IoT devices and systems have become a challenge due to difficulty in monitoring and intrusion detection in complex IoT networks. The behavior of common attacks made by botnets are analyzed. This paper proposes two solutions which are Machine learning approach and Rule based approach that analyzes traffic in an IoT network, detects suspicious traffic and alerts the user. For the ML approach the paper has four models trained and tested using a Ddos dataset.
This document will have a total on 73 pages, 18 tables, 34 figures and 5 pages of source of reference. |
| Atslēgas vārdi |
Keywords: Internet of Things (IoT), Network Security, Denial of Service (DoS) Attacks, Intrusion Detection Systems (IDS), Local Area Network (LAN), Transmission Control Protocol (TCP), Domain Name System (DNS), Transport Layer System (TLS). |
| Atslēgas vārdi angļu valodā |
Keywords: Internet of Things (IoT), Network Security, Denial of Service (DoS) Attacks, Intrusion Detection Systems (IDS), Local Area Network (LAN), Transmission Control Protocol (TCP), Domain Name System (DNS), Transport Layer System (TLS). |
| Valoda |
eng |
| Gads |
2024 |
| Darba augšupielādes datums un laiks |
24.05.2024 00:59:59 |
