Leveraging Big Data Analytics and Data Mining for Detecting Sophisticated Cyber Threats

The traditional cybersecurity approach, which depends on signature detection methods, cannot find complex and new dangers such advanced persistent threats (APTs). It proposes a solution for threat classification and anomaly detection combining big data technologies such as data mining with Machine Learning (ML) techniques. Through ML, the method increases detection efficiency and lowers false positives when spotting developing cyberthreats. Using enormous volumes of data from network traffic, records, and endpoints in real time helps to increase the precision, distribution, and speed of threat detection. The results show that the proposed method’s accuracy is 92% greater than the generally between 85 and 87% accurate current systems. It also noted more unusual values and lowered false positives and false negatives. It also lowers CPU, memory, and bandwidth usage and enhances resource economy. It enables the development of a security system more flexible and proactive in the future.