Ilca Lucian Florin, Lucian Ogruţan Petre, Balan Titus Constantin
Faculty of Electrical Engineering and Computer Science, "Transilvania" University of Brasov, 500036 Brasov, Romania.
Sensors (Basel). 2023 Jul 28;23(15):6757. doi: 10.3390/s23156757.
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.
在本研究中，对中小型组织（SMEs）的网络弹性方法进行了调查，并提出了一种利用开源解决方案进行规范性恶意软件分析、检测和响应的综合解决方案，用于检测新出现的威胁。通过利用开源解决方案和软件，开发了一个专门为员工人数多达250人的中小企业设计的系统，重点是检测新威胁。通过广泛的测试和验证，以及用于异常检测、安全和保障的高效算法和技术，证明了该方法在增强中小企业网络防御能力和提升其整体网络弹性方面的有效性。研究结果突出了利用开源资源应对中小企业所面临的独特网络安全挑战的实用性和可扩展性。所提出的系统将先进的恶意软件分析技术与实时威胁情报源相结合，以识别和分析中小企业网络内的恶意活动。通过采用机器学习算法和基于行为的分析，该系统可以有效地检测和分类复杂的恶意软件菌株，包括那些以前未见过的菌株。为了评估该系统的有效性，使用真实世界的数据集和场景进行了广泛的测试和验证。结果表明，恶意软件检测率有显著提高，该系统成功识别出传统安全措施经常遗漏的新出现威胁。所提出的系统代表了一种实用且可扩展的解决方案，使用容器化应用程序，可供寻求增强其网络防御能力的中小企业轻松部署。
