anomaly detection in emerging crimes with deep autoencoder architecture

Crimes nowadays pose unique issues to security and legal institutions and requires smart approaches to different types of peculiar behavior within. this paper proposes a deep learning autoencodes framework to analyze and recognize unusual activities in the fbi’s crime dataset. utilizing the autoencoder model’s architecture consisting of input, compression, and output layers, the adam optimizer is used with a mean squared error loss function for training, validating with twenty percent of the data. a reconstruction error is calculated and subsequently, a threshold of the 95th percentile of the average mse is set to flag anomalies. findings prove that the model outperforms all comparative methodologies, achieving 98% accuracy and a 97% precision f1 score. in addition, the model was shown to have an auc on roc curve of 98.2% which confirms the model’s ability to accurately classify normal and abnormal samples. this study illustrates the capability of multi-dimensional autoencoders to analyze and process complex crime data which can greatly aid security agencies in premeditative and reactive responses to crime. further research will focus on attention-based hybrid models along with system for real-time responsive tracing of volatile hyperdynamics.