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Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response

In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging op...

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Autores principales: Ilca, Lucian Florin, Lucian, Ogruţan Petre, Balan, Titus Constantin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422617/
https://www.ncbi.nlm.nih.gov/pubmed/37571540
http://dx.doi.org/10.3390/s23156757
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author Ilca, Lucian Florin
Lucian, Ogruţan Petre
Balan, Titus Constantin
author_facet Ilca, Lucian Florin
Lucian, Ogruţan Petre
Balan, Titus Constantin
author_sort Ilca, Lucian Florin
collection PubMed
description In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs’ cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system’s effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities.
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spelling pubmed-104226172023-08-13 Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response Ilca, Lucian Florin Lucian, Ogruţan Petre Balan, Titus Constantin Sensors (Basel) Article In this study, the methodology of cyber-resilience in small and medium-sized organizations (SMEs) is investigated, and a comprehensive solution utilizing prescriptive malware analysis, detection and response using open-source solutions is proposed for detecting new emerging threats. By leveraging open-source solutions and software, a system specifically designed for SMEs with up to 250 employees is developed, focusing on the detection of new threats. Through extensive testing and validation, as well as efficient algorithms and techniques for anomaly detection, safety, and security, the effectiveness of the approach in enhancing SMEs’ cyber-defense capabilities and bolstering their overall cyber-resilience is demonstrated. The findings highlight the practicality and scalability of utilizing open-source resources to address the unique cybersecurity challenges faced by SMEs. The proposed system combines advanced malware analysis techniques with real-time threat intelligence feeds to identify and analyze malicious activities within SME networks. By employing machine-learning algorithms and behavior-based analysis, the system can effectively detect and classify sophisticated malware strains, including those previously unseen. To evaluate the system’s effectiveness, extensive testing and validation were conducted using real-world datasets and scenarios. The results demonstrate significant improvements in malware detection rates, with the system successfully identifying emerging threats that traditional security measures often miss. The proposed system represents a practical and scalable solution using containerized applications that can be readily deployed by SMEs seeking to enhance their cyber-defense capabilities. MDPI 2023-07-28 /pmc/articles/PMC10422617/ /pubmed/37571540 http://dx.doi.org/10.3390/s23156757 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Ilca, Lucian Florin
Lucian, Ogruţan Petre
Balan, Titus Constantin
Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response
title Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response
title_full Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response
title_fullStr Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response
title_full_unstemmed Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response
title_short Enhancing Cyber-Resilience for Small and Medium-Sized Organizations with Prescriptive Malware Analysis, Detection and Response
title_sort enhancing cyber-resilience for small and medium-sized organizations with prescriptive malware analysis, detection and response
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10422617/
https://www.ncbi.nlm.nih.gov/pubmed/37571540
http://dx.doi.org/10.3390/s23156757
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