By: Dr. Farshad Badie, Dean of the Faculty of Computer Science and Informatics, Berlin School of Business and Innovation
This editorial explores the crucial role of machine learning (ML) in cyber-threat attribution (CTA) and emphasises the importance of interpretable models for effective attribution.
The Challenge of Cyber-Threat Attribution
Identifying the source of cyberattacks is a complex task due to the tactics employed by threat actors, including:
- Routing attacks through proxies: Attackers hide their identities by using intermediary servers.
- Planting false flags: Misleading information is used to divert investigators towards the wrong culprit.
- Adapting tactics: Threat actors constantly modify their methods to evade detection.
These challenges necessitate accurate and actionable attribution for:
- Enhanced cybersecurity defences: Understanding attacker strategies enables proactive defence mechanisms.
- Effective incident response: Swift attribution facilitates containment, damage minimisation, and speedy recovery.
- Establishing accountability: Identifying attackers deters malicious activities and upholds international norms.
Machine Learning to the Rescue
Traditional machine learning models have laid the foundation, but the evolving cyber threat landscape demands more sophisticated approaches. Deep learning and artificial neural networks hold promise for uncovering hidden patterns and anomalies. However, a key consideration is interpretability.
The Power of Interpretability
Effective attribution requires models that not only deliver precise results but also make them understandable to cybersecurity experts. Interpretability ensures:
- Transparency: Attribution decisions are not shrouded in complexity but are clear and actionable.
- Actionable intelligence: Experts can not only detect threats but also understand the “why” behind them.
- Improved defences: Insights gained from interpretable models inform future defence strategies.
Finding the Right Balance
The ideal model balances accuracy and interpretability. A highly accurate but opaque model hinders understanding, while a readily interpretable but less accurate model provides limited value. Selecting the appropriate model depends on the specific needs of each attribution case.
Interpretability Techniques
Several techniques enhance the interpretability of ML models for cyber-threat attribution:
- Feature Importance Analysis: Identifies the input data aspects most influential in the model’s decisions, allowing experts to prioritise investigations.
- Local Interpretability: Explains the model’s predictions for individual instances, revealing why a specific attribution was made.
- Rule-based Models: Provide clear guidelines for determining the source of cyber threats, promoting transparency and easy understanding.
Challenges and the Path Forward
The lack of transparency in complex ML models hinders their practical application. Explainable AI, a field dedicated to making models more transparent, holds the key to fostering trust and collaboration between human and machine learning. Researchers are continuously refining interpretability techniques, with the ultimate goal being a balance between model power and decision-making transparency.
