A holistic and proactive approach to forecasting cyber threats

Area of Science:

• Cybersecurity
• Machine Learning
• Data Science

Background:

• Traditional cyber-attack detection is reactive, relying on signature-based methods and human expertise.
• Existing machine learning models primarily focus on short-term detection, not long-term prediction.
• Long-term cyber-attack forecasting is crucial for proactive defense strategy development.

Purpose of the Study:

• To develop a novel machine learning (ML)-based framework for forecasting cyber-attack trends.
• To enable automated, large-scale, and long-term prediction of cyber threats.
• To provide defenders with extended lead time for developing and disseminating countermeasures.

Main Methods:

• Utilized a monthly dataset of major cyber incidents across 36 countries over 11 years.
• Extracted features from unstructured big data sources: scientific literature, news, blogs, and tweets.
• Developed a framework leveraging ML for automated trend identification and threat cycle generation.

Main Results:

• Successfully forecasted cyber-attack trends years in advance using big data.
• Identified a five-phase threat cycle detailing the lifecycle of 42 known cyber threats.
• Demonstrated an automated approach to cyber-attack trend prediction.

Conclusions:

• The proposed ML framework offers a significant advancement in proactive cybersecurity.
• Automated long-term cyber-attack prediction is feasible using diverse big data sources.
• Understanding the threat cycle enhances the ability to anticipate and mitigate future attacks.