Border security is of great importance to most countries. Turkey has been in conflict with terrorist groups since the 1980s. Up to now, more than 40,000 people have been killed, including Turkish soldiers and civilians. The porosity and openness of Turkey's Iraq border, combined with the rugged topography of the region, creates a passage for terrorist groups to move materiel and personnel. Technical capabilities of Unmanned Aerial Vehicles UAVs can be used to improve coverage along borders. However, their effectiveness is highly dependent on the characteristics of the region. In this study, 87 km of the Turkey-Iraq border is modeled in Map Aware Non Uniform Automata (MANA) to examine the potential impact of UAVs on detecting and classifying terrorists seeking passage from Northern Iraq into Turkey. The results from the 103,200 simulated terrorist incursions are analyzed using descriptive statistics, stepwise linear regression, lasso regression, regression trees, and random forests. The use of UAVs is found to be efficient in the detection and classification of terrorists in this region. The analysis techniques reveal that the most significant factors are the UAV's detection and classification performance, as well as the terrorists' counter detection capabilities. Thus, Turkey (and countries trying to secure similar terrain) should purchase (or build) and employ hard-to-detect UAVs with sophisticated sensors.