MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR CYBERSECURITY

Abstract : The detection and prevention of a network intrusion is a major concern. Machine Learning and Deep Learning methods detect network intrusions by predicting the risk with the help of training the data. Various machine learning and deep learning methods have been proposed over the years which are shown to be more accurate when compared to other network intrusion detecting systems. This survey paper gives a brief introduction about various machine learning and deep learning algorithms.
 EXISTING SYSTEM :
 ? Various machine learning and deep learning methods have been proposed over the years which are shown to be more accurate when compared to other network intrusion detecting systems. This survey paper gives a brief introduction about various machine learning and deep learning algorithms.
 DISADVANTAGE :
 ? Many problems exist that this conventional neural network cannot solve. The reason that RNN is a recurrent neural network is that the current output of a sequence is also related to the output before it. The concrete manifestation is that the network can remember the information of the previous moment and apply it to the calculation of the current output; that is, the nodes between the hidden layers become connected, and the input of the hidden layer includes both the output of the input layer and the last moment hidden layer output. Theoretically, any length of sequence data RNN can be processed. However, in practice, to reduce the complexity, it is often assumed that the current state is only related to the previous states
 PROPOSED SYSTEM :
 ? This includes even detection of already existing cybersecurity attacks. It requires as much effort to detect already existing type of attack so as to detect a new type of attack ? This is highly impossible task, since there are millions of cybersecurity attacks found among people all over the world. Hence, the detection of cybersecurity threats is of great concern. ? Therefore, the detection of these threats is very important. This can be done by using machine learning and deep learning algorithms in the field of cybersecurity.
 ADVANTAGE :
 ? KNN algorithm is a machine-learning algorithm used for classification, regression. KNN is a non-parametric, lazy learning algorithm. It predicts the classification of a new sample point by using a dataset in which the data points are separated into several classes. KNN is used for classification, the output is a class membership (predicts a class — a discrete value). ? An object is classified by maximum presence of its neighbors, with the object being assigned to the class having maximum number of k nearest neighbors. KNNis used for regression, the output is the value for the object (predicts continuous values). The value predicted is the average of the values of its k nearest neighbors
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