| Title | Reinforcement Learning-assisted Threshold Optimization for Dynamic Honeypot Adaptation to Enhance IoBT Networks Security |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Limouchi, Elnaz, Mahgoub, Imad |
| Conference Name | 2021 IEEE Symposium Series on Computational Intelligence (SSCI) |
| Keywords | Analytical models, data dissemination, deception technique, honeypot, human factors, Internet of Things, iobt, IoT, Logic gates, Network security, pubcrawl, reinforcement learning, resilience, Resiliency, Scalability, security, Switches, threshold optimization |
| Abstract | Internet of Battlefield Things (IoBT) is the application of Internet of Things (IoT) to a battlefield environment. IoBT networks operate in difficult conditions due to high mobility and unpredictable nature of battle fields and securing them is a challenge. There is increasing interest to use deception techniques to enhance the security of IoBT networks. A honeypot is a system installed on a network as a trap to attract the attention of an attacker and it does not store any valuable data. In this work, we introduce IoBT dual sensor gateways. We propose a Reinforcement Learning (RL)-assisted scheme, in which the IoBT dual sensor gateways intelligently switch between honeypot and real function based on a threshold. The optimal threshold is determined using reinforcement learning approach that adapts to nodes reputation. To focus on the impact of the mobile and uncertain behavior of IoBT networks on the proposed scheme, we consider the nodes as moving vehicles. We statistically analyze the results of our RL-based scheme obtained using ns-3 network simulation, and optimize value of the threshold. |
| DOI | 10.1109/SSCI50451.2021.9660066 |
| Citation Key | limouchi_reinforcement_2021 |
Reinforcement Learning-assisted Threshold Optimization for Dynamic Honeypot Adaptation to Enhance IoBT Networks Security