Strategic Honeypot Deployment in Ultra-Dense Beyond 5G Networks: A Reinforcement Learning Approach

Abstract

The progression of Software Defined Networking (SDN) and the virtualisation technologies lead to the beyond 5G era, providing multiple benefits in the smart economies. However, despite the advantages, security issues still remain. In particular, SDN/NFV and cloud/edge computing are related to various security issues. Moreover, due to the wireless nature of the entities, they are prone to a wide range of cyberthreats. Therefore, the presence of appropriate intrusion detection mechanisms is critical. Although both Machine Learning (ML) and Deep Learning (DL) have optimised the typical rule-based detection systems, the use of ML and DL requires labelled pre-existing datasets. However, this kind of data varies based on the nature of the respective environment. Another smart solution for detecting intrusions is to use honeypots. A honeypot acts as a decoy with the goal to mislead the cyberatatcker and protect the real assets. In this paper, we focus on Wireless Honeypots (WHs) in ultradense networks. In particular, we introduce a strategic honeypot deployment method, using two Reinforcement Learning (RL) techniques: (a) e − Greedy and (b) Q − Learning. Both methods aim to identify the optimal number of honeypots that can be deployed for protecting the actual entities. The experimental results demonstrate the efficacy of both methods.

Protecting IEC 60870-5-104 ICS/SCADA Systems with Honeypots

“…In [8], the authors introduce the use of wireless honeypots in ultra-dense Beyond 5G (B5G) networks. Moreover, the authors model and discuss the strategic deployment of honeypots in ultra-dense B5G networks.…”

Novel Ransomware Detection Exploiting Uncertainty and Calibration Quality Measures Using Deep Learning

“…Deep learning models have gained significant attention in the context of cybersecurity using the various models being developed and tested for misuse and anomaly detection and to prevent or deter different types of intrusions [19]. The rise of deep learning has improved the conventional signature and specification-based detection solutions, making these crucial tools for protecting systems and networks against cyberattacks [20]. Deep learning is recognized for its ability to rapidly analyze data streams as a basis to actively deploy and adjust countermeasures for improved detection of malware attacks [21,22].…”

Encountering social engineering activities with a novel honeypot mechanism

“…It operates by presenting itself on the internet as a potential target for attackers, usually a server or other highvalue asset, then collecting data and alerting defenders when an unauthorized person attempts to access the honeypot. Honeypots can be considered as a distinct from and alternative methods of defense that are frequently employed in the production environment as a preventative measure [10]- [13]. Honeypots operation consist of: computer (acts as s server full of vulnerabilities like opened ports), application (acts as common services like web, dynamic host configuration protocol) and data (spoofed high value assets and unencrypted data).…”