With the escalating complexity and frequency of cyber threats in today's interconnected digital landscape, traditional security measures have proven insufficient in safeguarding sensitive data and systems. In response, there has been a significant paradigm shift towards leveraging advanced technologies such as machine learning (ML) and data analytics to fortify cyber security defences. The significant influence of machine learning (ML) and data analytics on enhancing cyber threat detection and prevention techniques is examined in this paper. Through the utilization of algorithms that can handle enormous volumes of data, companies can obtain priceless knowledge about new dangers and illicit actions. Because machine learning algorithms are so good at finding patterns and abnormalities in datasets, it is possible to identify suspicious activity and possible security breaches in real time. Network logs, endpoint devices, and user behaviour can all be combined and analysed by analysts to find hidden patterns that indicate to sophisticated cyber -attacks. While ML and data analytics offer immense potential in bolstering cyber security, several challenges remain. These include ensuring the integrity and quality of training data, addressing algorithm biases, and navigating regulatory compliance requirements. In summary, the combination of data analytics and machine learning offers a solution to the problems facing contemporary cyber security.
Manupreet Kaur (Tue,) studied this question.