Nowadays, terrorism has evolved into such a destructive threat to the whole world that it is calling for an increasing devotion of professional researches and explorations. Machine learning, as a powerful weapon to unveil the hidden knowledge, has been successfully applied into the anti-terrorism field. The aim of this paper is as follows: by implementing anomaly detection algorithm into a famous terrorism database-GTD, we aim to locate the anomalous records within and in doing so, we attempt to present a list of outliers which deviate from the rest of the data. These finding anomalous observations could carry great hidden information which is interesting from a terrorism researcher's perspective. Then empirical analysis and experimental evidence are provided to support the reliability and effectiveness of the outcome. We present some examples from the anomaly list and elaborate their abnormality. Besides, we also validate the irregularity of these finding anomalies from the respect of an improved classification precision, since these exceptions could be incurred by some human errors which turn them into noises and by removing these noise-like objects, we can achieve a higher classification precision. The classification section is extended with three sophisticated classifiers, Support Vector Machine (SVM), Naïve Bayes (NB) and Logistic Regression (LR).
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