Performing Dynamic Malware Analysis in Software Defined Network using LSTM Technique
Keywords:
Control Plane, Data Plane, POX, Controller, accuracy, LSTM, sigmoid, reluAbstract
Methods for analyzing harmful or benign packets are crucial for enhancing security systems. Those packets can only be detected to a limited extent by current security methods. A malware analysis architecture by incorporating Control Plane and Data plane with the Software-Defined Networking (SDN). This article uses a deep learning model to classify malware in order to perform accurately and effectively. The Long Short-Term Memory (LSTM) model is trained using the system suggested in this work, which extracts a number of properties. We present a Secure SDN Simulation in this research that is controlled by a POX Controller and we propose an improved Long Short-Term Memory (LSTM), to achieve improved accuracy using a Confusion Matrix. To achieve this, we train and test an LSTM model using TensorFlow and Keras package. Long short-term memory (LSTM), which analyses all potential data points that can handle big datasets. The results showed that the sigmoid function performed better than other activation functions and “relu”(Rectified Linear Unit) activation layer that gives a better result with a 97.7% accuracy rate. This work can aid in the detection of malware and enhance security measures.
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