Analyzing and Predicting of Global Terrorism Attacks Based on Machine Learning

Terrorism poses a complex and pervasive threat to global security and sustainable development. Over 200,000 terrorist attacks have been documented since 1970, resulting in significant human casualties, extensive property damage, and widespread social unrest. These incidents not only disrupt daily life but also impede economic progress, underscoring the necessity of robust counter-terrorism strategies within the framework of global security governance. Although patterns in terrorist activities may appear random, they are often intentional and systematically organized, reflecting identifiable characteristics that can inform targeted preventive measures. Leveraging open-source datasets such as the Global Terrorism Database (GTD), researchers have constructed analytical and predictive models employing machine learning, clustering, and classification techniques. Various studies have introduced innovative predictive frameworks, including hybrid classifiers, risk assessment models, and deep learning architectures. These advancements contribute to more effective early warning systems and enhanced operational efficiency in counter-terrorism initiatives, with the goal of minimizing human losses and strengthening global security.