In recent years, terrorist attacks around the world have begun to develop more complex strategies and tactics that are not easily recognizable. Furthermore, in uncertain situations, agencies need to know whether the perpetrator was a terrorist or someone motivated by other factors (e.g. criminal activity) so that they can develop appropriate strategies to capture the responsible organizations and ... [Show full abstract] people. In most research studies, terrorist activity detection focuses on either individual incidents, which do not take into account the dynamic interactions among them, or network analysis, which leaves aside the functional roles of individuals while capturing interactions and giving a general idea about networks. In this study, we propose a unified approach that applies pattern classification techniques to network topology and features of incidents. The detected patterns are used in conjunction with an evolutionary adaptive neural fuzzy inference system to detect future incidents of terrorism. Finally, the proposed approach was tested and validated using a real world case study that consists of incidents in Iraq. The experimental results show that our approach outperforms other traditional detection approaches. Policymakers can use the approach for timely understanding and
detection of terrorist activity thus enabling precautions to be taken against future attacks.