An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Energy Management System in the Vehicles using Three Level Neuro Fuzzy Logic
Volume-3 | Issue-3
Cloud Load Estimation with Deep Logarithmic Network for Workload and Time Series Optimization
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Population Based Meta Heuristics Algorithm for Performance Improvement of Feed Forward Neural Network
Volume-2 | Issue-1
Comparative Analysis of an Efficient Image Denoising Method for Wireless Multimedia Sensor Network Images in Transform Domain
Volume-3 | Issue-3
A Comprehensive Review on Power Efficient Fault Tolerance Models in High Performance Computation Systems
Volume-3 | Issue-3
An Integrated Approach for Crop Production Analysis from Geographic Information System Data using SqueezeNet
Volume-3 | Issue-4
An Accurate Bitcoin Price Prediction using logistic regression with LSTM Machine Learning model
Volume-3 | Issue-3
Design of Distribution Transformer Health Management System using IoT Sensors
Volume-3 | Issue-3
Design of a Food Recommendation System using ADNet algorithm on a Hybrid Data Mining Process
Volume-3 | Issue-4
Automatic Diagnosis of Alzheimer’s disease using Hybrid Model and CNN
Volume-3 | Issue-4
Effective Prediction of Online Reviews for Improvement of Customer Recommendation Services by Hybrid Classification Approach
Volume-3 | Issue-4
Acoustic Features Based Emotional Speech Signal Categorization by Advanced Linear Discriminator Analysis
Volume-3 | Issue-4
Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction
Volume-3 | Issue-4
Identification of Electricity Threat and Performance Analysis using LSTM and RUSBoost Methodology
Volume-3 | Issue-4
Review on Data Securing Techniques for Internet of Medical Things
Volume-3 | Issue-3
Volume - 5 | Issue - 3 | september 2023
Published
21 August, 2023
Social engineering attacks continue to pose significant threats to information security by exploiting human psychology and manipulating individuals into divulging sensitive information or performing actions that compromise organizational systems. Traditional defense mechanisms often struggle to detect and mitigate such attacks due to their dynamic and deceptive nature. In response, the integration of hybrid soft computing techniques has developed as a promising method to enhance the accuracy and effectiveness of social engineering detection systems. This study provides an in-depth exploration of the various hybrid soft computing methodologies applied to the detection of social engineering attacks. It discusses the synergistic combination of different soft computing techniques, such as genetic algorithms, neural networks, swarm intelligence and fuzzy logic along with their integration with other security measures. The study presents a comprehensive survey of recent research advancements, methodologies, datasets, performance metrics, and challenges in the domain of hybrid soft computing for social engineering detection. Furthermore, it offers insights into potential future directions and applications for advancing the field.
KeywordsSocial Engineering Soft Computing Hybrid Techniques Neural Networks Fuzzy Logic Genetic Algorithms Swarm Intelligence Detection Information Security
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