A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications
Volume-3 | Issue-3
TOWARDS GHZ METALLIC ACCESS NETWORKS
Volume-1 | Issue-1
Analysis of Visible Light Communication using Integrated Avalanche Photodiode
Volume-4 | Issue-2
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Process Control Ladder Logic Trouble Shooting Techniques Fundamentals
Volume-1 | Issue-4
TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
COMPUTATIONAL OFFLOADING FOR PERFORMANCE IMPROVEMENT AND ENERGY SAVING IN MOBILE DEVICES
Volume-1 | Issue-4
Dual Edge-Fed Left Hand and Right Hand Circularly Polarized Rectangular Micro-Strip Patch Antenna for Wireless Communication Applications
Volume-2 | Issue-3
ANALYSIS OF ROUTING PROTOCOLS IN FLYING WIRELESS NETWORKS
Volume-1 | Issue-3
5G Systems with Low Density Parity Check based Chanel Coding for Enhanced Mobile Broadband Scheme
Volume-2 | Issue-1
TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application
Volume-3 | Issue-3
Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles
Volume-3 | Issue-3
Cyber-attack and Measuring its Risk
Volume-3 | Issue-4
Analysis of Solar Power Generation Performance Improvement Techniques
Volume-4 | Issue-3
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Pollination Inspired Clustering Model for Wireless Sensor Network Optimization
Volume-3 | Issue-3
Design of Low Power Cam Memory Cell for the Next Generation Network Processors
Volume-3 | Issue-4
A STUDY OF RESEARCH NOTIONS IN WIRELESS BODY SENSOR NETWORK (WBSN)
Volume-1 | Issue-2
Computation of Constant Gain and NF Circles for 60 GHz Ultra-low noise Amplifiers
Volume-3 | Issue-3
Volume - 3 | Issue - 2 | june 2021
Published
14 June, 2021
The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.
KeywordsMachine learning Support vector machine Intrusion detection Wireless sensor networks Grey wolf optimization algorithm
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