Intrusion Detection System - Deep Learning Perspective

Nowadays the large amount of sensitive data set is a traverse through different devices and communication channels and continuously new invention in computer network technology, security problem comes out increasingly, and it has become difficult to ignore because the day is day intruders discover new attacks so it is necessary to protect from those attacks and Intrusion Detection System play important role in network security. That's why to develop the advanced Intrusion Detection System (IDSs). Traditional Intrusion Detection Technologies faces a high false-positive rate, low accuracy, and many more problems. However, to detect the new attacks and analyze a large amount of data is one of the challenges in IDS. To improve the security of people now, adopt advanced technologies for protection. Deep learning is the rising area which deals with the large scale of data in a promising way. Also, various complex applications have been accomplishing by deep learning. Deep learning is a vast field of machine learning based on a hierarchical concept that teaches a computer to do what humans can do naturally: learn by observation. So, it will be composed of layers which might be multiple layers are called hidden layers which are neurons which is help to get the output using those hidden layers. Deep learning can govern broad-scale data and has shown efficient output in that field. This approach is helped with its mechanism to large data with efficient training time and provides good accuracy. However, there are some limitations to using deep learning in IDS. The proposed research work will discuss how deep-learning is used or which algorithm is used based on deep learning in the Intrusion Detection System to enhance the performance, its features, and also different approaches, limitations, and future implementations are discussed.