A Deep Neural Network Approach to Tracing Paths in Cybersecurity Investigations

Daniel, Clinton; Gill, T. Grandon; Hevner, Alan R.; Mullarkey, Matthew

doi:10.1109/icdmw51313.2020.00070

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…This is even more difficult in CPS, where attacks usually involve multistage and multiple components. Moreover, the analytic tasks conducted by security analysts rely heavily on a cognitive decision-making process that can differ between analysts, depending on their technical knowledge or level of experience [56]. This is why it is so important to have intelligent tools, as SMS-I, to support security analyst decisions.…”

Section: Sms-i Demonstrationmentioning

confidence: 99%

SMS-I: Intelligent Security for Cyber–Physical Systems

et al. 2022

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Critical infrastructures are an attractive target for attackers, mainly due to the catastrophic impact of these attacks on society. In addition, the cyber–physical nature of these infrastructures makes them more vulnerable to cyber–physical threats and makes the detection, investigation, and remediation of security attacks more difficult. Therefore, improving cyber–physical correlations, forensics investigations, and Incident response tasks is of paramount importance. This work describes the SMS-I tool that allows the improvement of these security aspects in critical infrastructures. Data from heterogeneous systems, over different time frames, are received and correlated. Both physical and logical security are unified and additional security details are analysed to find attack evidence. Different Artificial Intelligence (AI) methodologies are used to process and analyse the multi-dimensional data exploring the temporal correlation between cyber and physical Alerts and going beyond traditional techniques to detect unusual Events, and then find evidence of attacks. SMS-I’s Intelligent Dashboard supports decision makers in a deep analysis of how the breaches and the assets were explored and compromised. It assists and facilitates the security analysts using graphical dashboards and Alert classification suggestions. Therefore, they can more easily identify anomalous situations that can be related to possible Incident occurrences. Users can also explore information, with different levels of detail, including logical information and technical specifications. SMS-I also integrates with a scalable and open Security Incident Response Platform (TheHive) that enables the sharing of information about security Incidents and helps different organizations better understand threats and proactively defend their systems and networks.

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Section: Sms-i Demonstrationmentioning

confidence: 99%