Lightweight deception systems using honeypot techniques

Traditional defences against cyber threats such as Intrusion detection system or firewall were found to be lacking in this age and time. These defences lack the means to detect advanced persistent threats, zero-day vulnerabilities and rapid emergence of new malware variants. Honeypots were used t...

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Bibliographic Details
Main Author: Lee, Timothy Kok Kiang
Other Authors: Lam Kwok Yan
Format: Final Year Project (FYP)
Language:English
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/10356/74358
Description
Summary:Traditional defences against cyber threats such as Intrusion detection system or firewall were found to be lacking in this age and time. These defences lack the means to detect advanced persistent threats, zero-day vulnerabilities and rapid emergence of new malware variants. Honeypots were used to supplement traditional defences as it is able to provide intelligence on an attacker’s Tactics Techniques and Procedures (TTP). However, the deployment of honeypot systems is usually complicated and costly causing it to be out of reach for smaller market players. This project aims to design a lightweight honeypot architecture and explain why a lightweight solution is desirable. Then, an evaluation of the proposed lightweight honeypot architecture is conducted based on its ability to handle the number of concurrent connections. Research is first conducted on different honeypot systems exploring several design factors before proposing a solution. Then, the proposed solution is implemented and tested for its performance. There are 2 core concepts in the proposed solution – cluster technology and container virtualization. Lightweight honeypot architecture showed much more flexibility compared to its traditional counterparts. By incorporating the 2 core concepts, the cost and complexity of deployment has been reduced making it a feasible solution for smaller market players. Further work could be done on hardening the security of the architecture or implementing a machine learning module to correlate Security Information and Events Management (SIEM) logs.