Artificial Intelligence (AI) breakthroughs in the last few years have accelerated dramatically as a result of the industry's vast technological use. Neural Networks (NN) is one of the most vital areas of AI, as they allow for commercial use of features that were previously not accessible via the use of computers. The Intrusion Detection System (IDS) is one of the areas in which Neural Networks are being extensively investigated to provide comprehensive computer network security and data confidentiality. During the realization of this work Artificial Neural Network (ANN) were used to shape the proposed model using a realistic CICIDS2017 dataset retrieved from the Canadian Institute for Cyber-Security (CIC) website. Following implementation and testing, it was discovered that the new model performs exceptionally well, with an average. In addition, the receiver operator characteristic curve (ROC) has a 9.999 % area under the Receiver Operator Characteristic Curve (AUC). Finally, it was discovered that the new model is exceptional and has a high level of accuracy. The new model will aid in an improved knowledge of various orders in which IDS research has been conducted. It will be useful for those working on AI-based solutions in IDS and similar domains. It is possible to enhance the new model's detection capabilities to incorporate all other lingering forms of incidents in this actual datasets, which contains all real-time and existing incidents.