Intrusion Detection Mechanism for Large Scale Networks using CNN-LSTM

In today's world, Network and System Security are of paramount importance in the digital communication environment. To avoid breaches, it is badly needed for a security administrator to detect the intruder and prevent him from entering into the network. Machine Learning techniques are used to solve these types of problems, but they are not highly able to generalize as they fail to obtain relation among the features. Several works have also been done in Deep Learning using Artificial Neural Networks, Deep Neural Networks, RNN, etc. are not computationally efficient. This paper suggests a new machine learning model for intrusion detection that uses LSTMs and CNNs. This work uses CNN to choose feature characteristics from the input data, and send these features to LSTM for sequence analysis, and to address the imbalanced data set problem, Based on the total number of training examples in each class, each example will have its weight calculated based on cost function method. The raw input data format is transformed into a matrix format(image) to further decrease the computation cost. To test the efficiency of the CNN-LSTM model this work uses a conventional NSL-KDD dataset. The computation time has been reduced to 1 10 th the time that a fully connected layer took to train. The experimental results show that the model achieves an accuracy of 99.6% and a Detection rate of 96.75% while training.