Correction to: Intrusion Detection Behavioral Model by Using ANN

An intrusion detection system (IDS) is the most necessary component of cybersecurity that detects intrusion after or before an attack occurs. Security issues are prevalent in environments where social media companies and educational institutions are producing large volumes of data. This paper implemented an intrusion detection behavioral model (IDBM) to study online behavioral harmful activity patterns and generate a signature alert for the system. Data augmentation approaches were applied to address this problem, and the restricted quantity of data points did not affect the conclusions. The IDBM can be used to contribute to more reliable and secure data. The ANN model achieved 89.1%, 0.7983%, 0.9385%, 0.31%, and 0.947% detection exactness, sensitivity, specificity, miss rate, and accuracy.