Drug abuse and addiction problems are one of the most serious health, social, and psychological problems facing the world. Many international studies indicate that the start of drug abuse occurs mostly in adolescence, which is the period that young people spend in schools, institutes, and universities. Drugs in the student community have become a scourge that raises increasing concern, whether among families or educators, over the fate of school children and educational attainment. Regarding their behaviors, an addicted student often exhibits abnormal behaviors such as permanent lethargy, anxiety, tremors, and aggressive behavior toward others. Moreover, to obtain drugs, the addicted student becomes compelled to resort to various means and ways, and they gradually become criminal addicts. To this endeavor, a detector of abnormal behaviors in schools has become a necessity. In this paper, we built an automatic system able to analyze and detect abnormal behaviors of addicted students and inform the educational staff and parents to know how to manage and treat them. On a technical level, we used deep learning and the recent computer vision techniques in the suggested solution due to their contributions to human behavior and emotion recognition fields. The best-recorded result (97.5%) is obtained with fused handcrafted features based on skeleton joints and deep features extracted with the MobileNet pretrained model and forwarded to a deep proposed network based on two TimeDistributed layers, one BiLSTM layer, and several Dense layers.