This project is a network intrusiondetector that uses machine learning algorithms to distinguish between bad (intrusions/attacks) and good (normal) connections. The UI is built using Flask framework and the KDD Cup 1999 dataset is used for training.
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This document is a project report for an IntrusionDetectionSystem. It includes an introduction describing intrusiondetectionsystems and their components. The architecture section includes a data flow diagram and flowchart. It also lists the required hardware and software. Screenshots from the system are provided.
Kang et al. [23] proposed an intrusiondetectionsystem based on the deep neural network for vehicular networks. The attack scenario was performed on malicious data packets, which are injected into an in-vehicle controller area network
It will again be activated if another intrusion occurs but for a limited duration, thus making our system more efficient. Figure 1 and Figure 2 represent the block diagram and the flowchart of our proposed system.
With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusiondetectionsystems (NIDS) to protect against various threats.
The article proposes a taxonomy of intrusiondetectionsystems based on the system deployment, data source, timeliness and detection strategy. Some future challenges for intrusiondetectionsystems have also been presented.
The paper proposes a novel architecture to combat intrusiondetection that has a Convolutional Neural Network (CNN) module, along with a Long Short Term Memory(LSTM) module and with a Support Vector Machine (SVM) classification function.
