Optimization Accuracy of Intrusion Detection of Imbalanced Network using PCA and Conv1D-LSTM Technique
Amrita Singh, Vijay Bhandari, Ritu Srivastava · 2022 3rd International Conference for Emerging Technology (INCET) · 2022
Intrusion detection is the process of analyzing the network packets to identify if the packet is legitimate or anomalous. The major challenges involved in this domain includes the huge volume of data for training and the fast and streaming data that is to be provided for the prediction process. Further, the intrinsic data imbalance contained in the domain presents more challenges to the intrusion detection model. In this paper enhanced long short term memory (LSTM) classification accuracy and other parameters are compared with conventional deep learning technique and other machine learning techniques. This framework can be used not only to classify the tweets but also to analyses the sentiments of users towards higher education in India. The proposed framework is based on two algorithms: Enhancing LSTM using Evolutionary algorithm. Enhanced LSTM algorithm is used to improve its functionality with the help of Evolutionary algorithm as standard LSTM can choose values of parameters in a random manner.