Machine Learning-Based Information Systems Security Management

Keywords:
Darktrace, Security risks, Protection Mechanisms, Machine Learning

Abstract:
Modern companies' operations depend on the Internet and web services, so security concerns are critical. This paper discusses current security risks and responses, including security mechanisms. Risks specific to modern ages, especially financial institutions, can be categorized into ones due to increased use of mobile applications, break-ins of third-party organizations, and cryptocurrency usage risks. Protection mechanisms are used to protect corporate processes and data and must meet desired critical points. Further, this study presents specific operations of Darktrace, a suite of AI-powered software tools designed to protect corporate assets from cyberattacks. Darktrace uses both supervised and unsupervised machine learning algorithms to maximize threat detection performance, supporting the conclusion that artificial intelligence promises a lot in the realm of threat detection and intrusion detection.

CITATION:

IEEE format

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S. Anđelić, N. Dedić, V. Dedić, “Machine Learning-Based Information Systems Security Management,” in Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science, Belgrade, Singidunum University, Serbia, 2024, pp. 156-161. doi:10.15308/Sinteza-2024-156-161

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Anđelić, S., Dedić, N., Dedić, V. (2024). Machine Learning-Based Information Systems Security Management. Paper presented at Sinteza 2024 - International Scientific Conference on Information Technology, Computer Science, and Data Science. doi:10.15308/Sinteza-2024-156-161

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