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CYBER SECURITY DETECTION USING ANN

One of the major challenges in cyber security is the provision of an automated and effective cyber-threats
detection technique. We present an AI technique for cyber-threats detection, based on artificial neural
networks. The proposed technique converts multitude of collected security events to individual event profiles
and use a deep learning-based detection method for enhanced cyberthreat detection. For this work, we
developed an AI-SIEM system based on a combination of event profiling for data preprocessing and different
artificial neural network methods, including FCNN, CNN, and LSTM. The system focuses on discriminating
between true positive and false positive alerts, thus helping security analysts to rapidly respond to cyber threats.
All experiments in this study are performed by authors using two benchmark datasets (NSLKDD and
CICIDS2017) and two datasets collected in the real world. To evaluate the performance comparison with
existing methods, we conducted experiments using the five conventional machine-learning methods (SVM, k-
NN, RF, NB, and DT). Consequently, the experimental results of this study ensure that our proposed methods
are capable of being employed as learning-based models for network intrusion-detection, and show that
although it is employed in the real world, the performance outperforms the conventional machine-learning
methods.