Predictive data mining
(also predictive analysis)
Predictive data mining definition
Predictive data mining uses statistical algorithms, machine learning techniques, and data analysis to help organizations find patterns in large datasets and predict future outcomes based on historical data. As a result, predictive data mining helps organizations make informed decisions, optimize resources and processes, and drive growth. Predictive data mining can also significantly improve an organization’s security posture. The most popular techniques in predictive data mining include regression analysis, classification, decision trees, neural networks, and ensemble methods.
See also: data mining, cyberattack
Predictive data mining use cases
- Anomaly detection. It helps organizations detect unusual patterns and identify security breaches, insider threats, or other malicious activities.
- Risk assessment. Users can identify and prioritize potential risks and the potential impact of these vulnerabilities. As a result, they can make better-informed decisions and implement strategies to mitigate these threats.
- Threat prediction. Predictive data mining analyzes patterns in historical data, such as network logs or threat intelligence feeds, to help organizations identify new threats.
- Incident response. It forecasts the potential scope and consequences of a security breach. Organizations can respond more effectively and minimize damage when they know what to expect.
- Phishing detection. It analyzes email content, sender information, and other attributes. This way, predictive data mining protects users from phishing email attacks.
- Malware defense. It examines file characteristics, behaviors, and other attributes. As a result, it warns organizations of both known and unknown malware types.
- Network security. Predictive data mining detects potential weak points or attack paths and recommends appropriate responses, helping organizations optimize their network security.