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The increasingly sophisticated and growing number of threat actors along withthe sheer speed at which cyber attacks unfold, make timely identification ofattacks imperative to an organisations' security. Consequently, personsresponsible for security employ a large variety of information sourcesconcerning emerging attacks, attackers' course of actions or indicators ofcompromise. However, a vast amount of the needed security information isavailable in unstructured textual form, which complicates the automated andtimely extraction of attackers' Tactics, Techniques and Procedures (TTPs). Inorder to address this problem we systematically evaluate and compare differentNatural Language Processing (NLP) and machine learning techniques used forsecurity information extraction in research. Based on our investigations wepropose a data processing pipeline that automatically classifies unstructuredtext according to attackers' tactics and techniques derived from a knowledgebase of adversary tactics, techniques and procedures.

Towards Automated Classification of Attackers' TTPs by combining NLP with ML Techniques