While a TTP-based attribution approach like CAPTAIN offers significant advantages, it also has limitations. One major limitation is the reliance on historical data, which may not fully capture the evolving tactics and techniques employed by threat actors. As adversaries adapt and innovate, the TTP database may become outdated, leading to potential misattributions. Additionally, TTPs can be shared or reused across different APT groups, complicating the attribution process and increasing the risk of false positives.
To address these limitations, CAPTAIN can be complemented by other techniques such as behavioral analysis, threat intelligence sharing, and machine learning models that focus on anomaly detection. Behavioral analysis can help identify deviations from established TTP patterns, providing insights into new or modified attack strategies. Threat intelligence sharing among organizations can enhance the richness of the TTP database, allowing for real-time updates and collaborative defense strategies. Furthermore, machine learning models can be trained on a broader set of features, including network traffic patterns, user behavior analytics, and even social engineering tactics, to create a more holistic view of threat actor behavior. By integrating these complementary techniques, CAPTAIN can achieve a more comprehensive and robust threat attribution framework.