Published Paper Details:

In this work, we propose an ANN-based AI cyber security framework for detecting cyber attacks. By converting security incidents into single entities, the architecture takes advantage of advanced learning models including CNN and LSTM to improve the detection rate of the system. The AI-SIEM system decreases the occurrence of false alarms, thus enabling more efficient and faster response to changing cyber attacks. Experiments carried out on benchmark datasets, such as NSLKDD, CISIDS2017, results high values when compared to other machine learning techniques like SVM, k-NN and Decision Trees. The technique in question has been designed with an emphasis on practical scenarios, where issues such as data annotation and overfitting are expected. The results confirm that the elaborated framework is competent enough for intrusion detection, working with large volumes of information and responding to a changing threat landscape, thus providing strong defense in various spheres of cybersecurity.