Sowinski-Mydlarz, Viktor, Vassilev, Vassil, Ouazzane, Karim and Phipps, Anthony (2022) Security analytics framework validation based on threat intelligence. In: International Conference on Computational Science and Computational Intelligence, December 14-16, 2022, Las Vegas, USA. (In Press)
This is the latest version of this item.
|
Text
VV-32-Security Analytics Framework Validation based on Threat Intelligence.pdf - Accepted Version Download (2MB) | Preview |
Abstract / Description
Logical analysis of the ontology of digital security in banking helps us to identify the possible entry points for illegal access. The threats described in the ontology are detected by Machine Learning engines. The theoretical analysis is validated by verifying the framework and Machine Learning algorithms. Intelligence Graphs (original term) which are adding the actions to knowledge graphs to form workflows, are a base for validation of the framework through simulated execution of the scenarios specified in them.
The output is a method for analysing live network traffic data (machine learning algorithm) combined with semantic model to give a hybrid framework for threat intelligence in digital banking, leading to a complete threat detection platform. To prove our concept, we first devised an analytical validation scheme based on scenarios, which proves the viability of the premise, and then we implemented some scenarios, which demonstrate it in practice. The model is validated using operation workflows, namely 12 scenarios of banking “journeys” under the duress of various threats.
In this work we are presenting the validation of the framework by simulation of the banking operations and transactions stemming from the Ontology of Digital Banking used as a model of the banking infrastructure (assets, vulnerabilities, and threats included). This model has been approved by the members of Lloyds Bank Cyber Security Division.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Uncontrolled Keywords: | Digital security; Digital banking; Machine Learning; Threat intelligence |
Subjects: | 000 Computer science, information & general works |
Department: | School of Computing and Digital Media |
Depositing User: | Vassil Vassilev |
Date Deposited: | 31 Oct 2022 09:22 |
Last Modified: | 31 Oct 2022 09:22 |
URI: | https://repository.londonmet.ac.uk/id/eprint/8001 |
Available Versions of this Item
-
Security Analytics Framework Validation based on Threat Intelligence. (deposited UNSPECIFIED)
- Security analytics framework validation based on threat intelligence. (deposited 31 Oct 2022 09:22) [Currently Displayed]
Downloads
Downloads per month over past year
Downloads each year
Actions (login required)
![]() |
View Item |