Analysis of Visible Light Communication using Integrated Avalanche Photodiode
Volume-4 | Issue-2
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
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
ANALYSIS OF ROUTING PROTOCOLS IN FLYING WIRELESS NETWORKS
Volume-1 | Issue-3
Dual Edge-Fed Left Hand and Right Hand Circularly Polarized Rectangular Micro-Strip Patch Antenna for Wireless Communication Applications
Volume-2 | Issue-3
Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection
Volume-3 | Issue-2
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
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Analysis of Solar Power Generation Performance Improvement Techniques
Volume-4 | 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 - 4 | december 2021
Published
16 May, 2022
Deep learning algorithms are very effective in the application of classification and prediction over the traditional estimators. The proposed work employs a bottleneck layer algorithm on CICIDS-2017 dataset to prove its efficacy on the prediction of cyber-attacks. The performance of the bottleneck model architecture is incorporated with Artificial Neural Network (ANN) and Deep Neural Network (DNN) models and compared over the traditional ANN, DNN and Support Vector Machines (SVM) models. The experimental work reaches a maximum accuracy of 92.35% in the DNN and 90.98% in ANN algorithm respectively.
KeywordsIntrusion detection system bottleneck layer cyber security model anomaly prediction
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