Visible to the public Vigilant Dynamic Honeypot Assisted by Dynamic Fuzzy Rule Interpolation

TitleVigilant Dynamic Honeypot Assisted by Dynamic Fuzzy Rule Interpolation
Publication TypeConference Paper
Year of Publication2018
AuthorsNaik, N., Shang, C., Shen, Q., Jenkins, P.
Conference Name2018 IEEE Symposium Series on Computational Intelligence (SSCI)
Date Publishednov
ISBN Number978-1-5386-9276-9
KeywordsARP Spoofing, attack identification, Communication networks, composability, computer network security, D-FRI, Dynamic Fuzzy Rule Interpolation, Dynamic Networks and Security, Dynamical Systems, Fuzzy logic, fuzzy systems, interpolation, IP networks, IP spoofing, knowledge based systems, Metrics, Network security, primary security tool, pubcrawl, Resiliency, security, security attacks, security of data, spoofing attack, Tools, vigilant dynamic honeypot
Abstract

Dynamic Fuzzy Rule Interpolation (D-FRI) offers a dynamic rule base for fuzzy systems which is especially useful for systems with changing requirements and limited prior knowledge. This suggests a possible application of D-FRI in the area of network security due to the volatility of the traffic. A honeypot is a valuable tool in the field of network security for baiting attackers and collecting their information. However, typically designed with fewer resources they are not considered as a primary security tool for use in network security. Consequently, such honeypots can be vulnerable to many security attacks. One such attack is a spoofing attack which can cause severe damage to the honeypot, making it inefficient. This paper presents a vigilant dynamic honeypot based on the D-FRI approach for use in predicting and alerting of spoofing attacks on the honeypot. First, it proposes a technique for spoofing attack identification based on the analysis of simulated attack data. Then, the paper employs the identification technique to develop a D-FRI based vigilant dynamic honeypot, allowing the honeypot to predict and alert that a spoofing attack is taking place in the absence of matching rules. The resulting system is capable of learning and maintaining a dynamic rule base for more accurate identification of potential spoofing attacks with respect to the changing traffic conditions of the network.

URLhttps://ieeexplore.ieee.org/document/8628775
DOI10.1109/SSCI.2018.8628775
Citation Keynaik_vigilant_2018