Abstract
This Epidemiology can be applied to cybersecurity as a novel approach for analysing and detecting cyber threats and their risks. It provides a systematic model for the analysis of likelihood, consequence, management, and prevention measures to examine malicious behaviours like disease. There are a few research studies in discrete cybersecurity risk factors; however, there is a significant research gap on the analysis of collective cyber risk factors and measuring their cyber risk impacts. Effective cybersecurity risk management requires the identification and estimation of the probability of infection, based on a comprehensive range of historical and environmental factors, including human behaviour and technology characteristics. This paper explores how an epidemiological principle can be applied to identify cybersecurity risk factors. These risk factors comprise both human and machine behaviours profiled as risk factors. This paper conducts a preliminary analysis of the relationships between these risk factors utilising Domain Name System (DNS) data sources. The experimental results indicated that the epidemiological principle can effectively examine and estimate cyber risk factors. The proposed principle has a great potential in enhancing new machine learning-enabled intrusion detection solutions by utilising this principle as a risk assessment module of the solutions.
Highlights
The cyber terrain continues to expand at a rapid pace
A botnet attack is one of the complex hacking techniques against Internet of Things (IoT) networks, which denotes a set of linked computers cooperating to implement suspicious and repetitive events to corrupt the resources of a victim such as Domain Name System (DNS) amplification attacks
This paper has discussed the applications of epidemiology to cybersecurity
Summary
This significantly expands the count of cyber-physical features and the number of entry points for potential exploitation. Artificial intelligence (AI) and machine learning (ML) provide revolutionary means to analyse and respond to behavioural patterns across complex Internet of Things (IoT) ecosystems, in computer speed. It can provide exceptional facilitation of big-data correlation and pattern recognition across many complex factors. The detection methods often fail at recognizing new variants of attacks and new botnet families. This paper explores how epidemiological principles can be applied to determine a range of factors These factors comprise both human and machine behaviours and characteristics profiled as risk factors.
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