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Basavaraju, Shashank (2024) Developing an Advanced and Adaptive Framework of Honeypots for Efficient Deception for ZigBee IOT Environments. Masters thesis, Dublin, National College of Ireland.
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Abstract
Cyber threats continue to be one of the most persistent security concerns in modern digital settings. This study deals with these issues using honeypots, integrated with an intrusion detection system that uses deep learning. Utilizing them to entice potential attackers into the open to demonstrate their methods, captured traffic is analysed using a GRU-based deep learning model to find intrusion attempts. The hardware setup comprises ESP32 microcontrollers connected with DHT11 sensors to achieve the environmental data, XBee modules for communication, OLED displays for data visualization, and a CC2531 ZigBee sniffer for packet analysis. In action, the honeypot captures and preserves the traffic within a controlled environment, where the attackers lead themselves unknowingly to reveal their strategies. Tools like hping3 and Hydra are used to test their attacks to understand the attacker procedure. This framework is coming with lots of advantage on the front of the identification and mitigation of the threat on cyberspace through being proactive. Since the model integrates CNN for feature extraction and GRU for sequence analysis, it is feature-rich to detect any sophisticated pattern of attacks. In addition to that, the tricking nature of the honeypot will help in the early detection of the threats to be attacked and will further enhance the overall cybersecurity defence against the dynamic threat landscapes. The CNN-GRU model is trained using custom datasets created by Wireshark achieved an amazing level of 94% testing accuracy for distinguishing malicious traffic.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Prior, Michael UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks > Internet of things |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 29 Jul 2025 10:53 |
| Last Modified: | 29 Jul 2025 10:53 |
| URI: | https://norma.ncirl.ie/id/eprint/8295 |
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