A Hybrid Anomaly Based Intrusion Detection Methodology Using IWD for LSTM Classification
Authors:
- 14 December 2020
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Abstract
The Network Intrusion Detection based on Anomaly is one of the best ways to identify the spam users and activities in cyber security. In present era, the Intrusion Detection System resources are increased due to inappropriate features that effect the detection rate of systems. To ensure better detection rate, a feature selection approach is utilized for the elimination of dissimilar and unemployable features in Intrusion Detection Systems. In addition, the time-consuming for the detection process also needs to be augmented for the process of classification. The paper introduces a method that avails the IWD algorithm for the feature subset selection in conjunction with LSTM to predict the malicious activity on that network. KDD CUP’99 dataset is employed for the judgement of performance on the intrusion detection in comparison with extant techniques. The performance estimate of the proposed model with previous methodologies depicts that the intended model is prominent by means of Higher Detection Rate, Low False Alarm Rate, and time consumption.Keywords
This publication has 14 references indexed in Scilit:
- An Improved Intrusion Detection Algorithm Based on GA and SVMIEEE Access, 2018
- An IWD-based feature selection method for intrusion detection systemSoft Computing, 2017
- Anomaly detection approach using hybrid algorithm of data mining technique2017
- Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection2016
- A study on intrusion detection using neural networks trained with evolutionary algorithmsSoft Computing, 2015
- Analysis of network traffic features for anomaly detectionMachine Learning, 2014
- Intelligent Water Drops Algorithm for Rough Set Feature SelectionLecture Notes in Computer Science, 2013
- Neural-Intelligent Water Drops algorithm to select relevant textural features for developing precision irrigation system using machine visionComputers and Electronics in Agriculture, 2011
- Anomaly-based intrusion detection in software as a service2011
- Long Short-Term MemoryNeural Computation, 1997