Using Data Mining as a Tool to Enhance Threat Detection and Response
DOI:
https://doi.org/10.63282/3050-9246/ICRTCSIT-121Keywords:
Data Mining, Threat Detection, Threat Response, Cybersecurity, Anomaly Detection, Intrusion Detection, Machine Learning, Pattern Recognition, Data Analysis, Security Analytics, Incident Response, Predictive Analytics, Big Data, Network Security, Risk ManagementAbstract
As the nature and scope of cyber threats are growing, signature-based protection mechanisms can no longer protect online resources. Having data mining investigate large volumes of data to uncover hidden patterns has been an essential aspect in the enhancement of threat detection and response. In this paper, we shall examine how data mining strategies can be applied to identify anomalies that can be utilized to predict attacks and even automatically defend the systems. According to the recent findings in machine learning and predictive analytics, information-driven models are more effective in identifying, responding faster, and helping in preemptive cybersecurity procedures. The other issues associated with the deployment of data mining solutions in cybersecurity that have been discussed in the study are data quality, complexity of computation, and privacy
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