A Novel Intrusion Detection Model for Detecting Known and Novel Cyber-attacks Using Convolutional Neural Network

MCA

- Today's cyber world faces a serious security problem with the use of new breach control techniques. a wide variety of tactics Learning approaches for developing a new intrusion prevention system have been developed. Therefore, we developed machine learning techniques to prevent infiltration. Developing a New Investigation Methodology. By using the approach, we may detect infiltration and identify the attacker's specifics. IDS are divided into two categories: host-specific and network-based. A host-based intrusion detection system (HIDS) monitors certain hosts or networks and alerts the user if suspicious activity is found. such as altering or deleting an internal document, making a sudden flurry of system calls, or making unforeseen changes to the settings. Network equipment like firewalls and bridges frequently have a network-based security mechanism (NIDS) linked to it to detect and prevent network threats. The KDD cup IDS dataset from the current research was obtained from an information source. The pre-processing techniques must then be used. The next step is to use a variety of deep learning and machine learning approaches, such as logistic regression (LR) and traditional neural networks (CNN). The query data indicates the accuracy of the aforementioned algorithms. After that, FLASK may be used to deploy the project as an internet application.

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