Visible to the public Agent Based Cybersecurity Model for Business Entity Risk Assessment

TitleAgent Based Cybersecurity Model for Business Entity Risk Assessment
Publication TypeConference Paper
Year of Publication2020
AuthorsAshiku, L., Dagli, C.
Conference Name2020 IEEE International Symposium on Systems Engineering (ISSE)
Date PublishedNov. 2020
ISBN Number978-1-7281-8602-3
Keywordsagent based cybersecurity, agent-based modelling, agent-based system modelling, attack-state, Business, business data processing, business entity risk assessment, complex enterprise systems, computer network security, computer networks, critical infrastructure, cyber-attacks, cybersecurity, data access, defense mechanism, defense strategies, dynamic state changes, external attacks, Human Behavior, human factors, human interactions, information exchange, innovative information technology, internal attacks, Mathematical model, multi-agent systems, network agents, network setting, network threats, network traffic features, network transactions, neural nets, Neural Network, Neural networks, Organizations, Predator prey systems, pubcrawl, risk assessment, risk management, risk-based approach, security, security solutions, self-organizing behavior, telecommunication traffic, third-party users, time-state chart, Workstations

Computer networks and surging advancements of innovative information technology construct a critical infrastructure for network transactions of business entities. Information exchange and data access though such infrastructure is scrutinized by adversaries for vulnerabilities that lead to cyber-attacks. This paper presents an agent-based system modelling to conceptualize and extract explicit and latent structure of the complex enterprise systems as well as human interactions within the system to determine common vulnerabilities of the entity. The model captures emergent behavior resulting from interactions of multiple network agents including the number of workstations, regular, administrator and third-party users, external and internal attacks, defense mechanisms for the network setting, and many other parameters. A risk-based approach to modelling cybersecurity of a business entity is utilized to derive the rate of attacks. A neural network model will generalize the type of attack based on network traffic features allowing dynamic state changes. Rules of engagement to generate self-organizing behavior will be leveraged to appoint a defense mechanism suitable for the attack-state of the model. The effectiveness of the model will be depicted by time-state chart that shows the number of affected assets for the different types of attacks triggered by the entity risk and the time it takes to revert into normal state. The model will also associate a relevant cost per incident occurrence that derives the need for enhancement of security solutions.

Citation Keyashiku_agent_2020