Data Science Techniques for Cybersecurity Threat Detection

Abstract

Due to the explosive development of digital technologies and the Internet of Things (IoT), there has been an enormous influx of data, and the number of advanced cyber threats has also increased. Conventional cybersecurity controls are mostly not able to identify sophisticated and dynamic attacks as they happen. This essay will discuss how data science methods, such as, but not limited to, machine learning (ML), deep learning (DL), natural language processing (NLP), and big data analytics, are being used to detect, classify, and prevent cybersecurity threats. The study, based on the review conducted through analytical research, illustrates the beneficial impact of data-driven models on the intrusion detection, malware classification, phishing detection, and anomaly analysis. The combination of predictive analytics and cybersecurity systems can ensure the ability to monitor threats and assess risks in real time and respond automatically. Other issues that are considered in the paper include data imbalance, adversarial attacks, and model explainability. The results indicate that data science can increase the resilience of cybersecurity through the provision of proactive defense measures, which, in the end, will contribute to more secure and smarter digital ecosystems.