Intrusion Detection System Using Enhanced Convolution Neural Network Python IEEE Machine Learning Final Year Project 2023.
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Title of the IIEEE Basic Paper: Intrusion Detection System Using Enhanced Convolutional Neural Network
Implementation code: Python.
Algorithm/model used: decision tree classification.
Web framework: bottle.
️Frontend: HTML, CSS, JavaScript.
Cost (in Indian Rupees): Rs.3000/.

IEEE Base Paper Summary:
Network intrusion detection technology plays an important role in maintaining network security. The main work is to continuously detect the current network status, through the detection of abnormal behavior in the network status, and timely warning to alert network administrators. The timeliness and accuracy of the intrusion detection system (IDS) is critical to the availability and reliability of the current network. In response to the problems of high false alarm rate, low detection efficiency and limited features common in IDS, this paper first explores the application of deep learning techniques in the field of network intrusion detection. With the ability of deep learning algorithms to automatically extract features from intrusion data and avoid the work of manually screening features, an intrusion detection method based on improved convolutional neural networks is then proposed. The method is improved by introducing the Inception module for optimal intrusion feature extraction based on the traditional convolutional neural network. The inception module uses a parallel convolution structure with several filters, using different sized convolution kernels on each convolution line for multiple layer-by-layer operations. The different characteristics of network intrusions in the dataset are identified and clustered using stacking.

REFERENCE:
Xue Ying Li; Rui Tang; Wei Song, “Intrusion Detection System with Enhanced Convolution Neural Network,” 2022 11th International Conference on Information and Communications Technology (ICTech)), 2022 IEEE Conference.

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