Detection And Prevention of Malicious Activities In Vulnerable Network Security Using Deep Learning

In the digital age, network security is very important because bad people are always coming up with new ways to break into systems that are weak. Most of the time, standard security methods aren’t good enough to find and stop these complex threats. By using its ability to look at and learn from huge amounts of data, deep learning has become a useful tool for making networks safer. A thorough study of how deep learning techniques can be used to find and stop harmful actions in networks with weak security is presented in this paper. In this paper, a deep learning-based method that uses recurrent neural networks (RNNs), LSTM and convolutional neural networks (CNNs) to look at network traffic trends and spot strange behavior that could be a sign of hostile activity. The suggested system can accurately tell the difference between good and bad actions by training the model on a set of datasets that contain both normal and harmful network traffic. The paper discussed about setting up a real-time tracking system that checks network data all the time and lets managers know about possible threats. Several efficiency measures are used to judge how well the system works, showing that it can greatly improve network security by finding and stopping hostile actions in weak systems. The paper results show that methods based on deep learning are a hopeful way to deal with the problems of network security, offering a proactive way to protect against new threats. At the end, we talk about where future study should go and how important it is to include deep learning in current security systems to make sure they are strong against online risks

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