The catastrophic events of September 11, 2001 and the beheading of James Foley on August 19, 2014 were the tipping points in the United States’ infamous war on terrorism. The countless tragedies and years of war have demonstrated the unprecedented brutality of the terrorist organizations Al-Qaeda and the Islamic State, who have become household names for Americans. However, despite their notoriety, these two groups may not be the most lethal nor the most strategically capable of extremist organizations. Rather, hidden by mountainous Afghanistan, the Haqqani Network has established itself as a competent enemy for five decades now, all while evading the general public’s eye. Despite its persistent presence in Afghanistan since the Soviet-Afghan War and the extensive qualitative literature discussing the breadth of its capabilities and reach, the Haqqani Network’s potential lethality does not appear to be reflected in its respective quantitative data. This study explores the potential underestimations of the Haqqani Network’s breadth of attacks in Afghanistan within the Global Terrorism Database through association rules mining and two Naive Bayes classifiers. Since the extremist group’s creation, the Global Terrorism Database has only attributed 84 attacks within Afghanistan to the Haqqani Network. This study will therefore explore whether or not there is reason to suspect that the GTD has severely undercounted the Haqqani Network’s capabilities in Afghanistan through machine learning.
