Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Blockchain-Enabled Federated Learning on Kubernetes for Air Quality Prediction Applications
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Hybrid Parallel Image Processing Algorithm for Binary Images with Image Thinning Technique
Volume-3 | Issue-3
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
QoS-aware Virtual Machine (VM) for Optimal Resource Utilization and Energy Conservation
Volume-3 | Issue-3
Probabilistic Neural Network based Managing Algorithm for Building Automation System
Volume-3 | Issue-4
Fusion based Feature Extraction Analysis of ECG Signal Interpretation - A Systematic Approach
Volume-3 | Issue-1
Multi-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Volume-3 | Issue-3
Real Time Anomaly Detection Techniques Using PySpark Frame Work
Volume-2 | Issue-1
Deniable Authentication Encryption for Privacy Protection using Blockchain
Volume-3 | Issue-3
Smart Fashion: A Review of AI Applications in Virtual Try-On & Fashion Synthesis
Volume-3 | Issue-4
Sentiment Analysis of Nepali COVID19 Tweets Using NB, SVM AND LSTM
Volume-3 | Issue-3
Audio Tagging Using CNN Based Audio Neural Networks for Massive Data Processing
Volume-3 | Issue-4
Frontiers of AI beyond 2030: Novel Perspectives
Volume-4 | Issue-4
Smart Medical Nursing Care Unit based on Internet of Things for Emergency Healthcare
Volume-3 | Issue-4
Early Stage Detection of Crack in Glasses by Hybrid CNN Transformation Approach
Volume-3 | Issue-4
ARTIFICIAL INTELLIGENCE APPLICATION IN SMART WAREHOUSING ENVIRONMENT FOR AUTOMATED LOGISTICS
Volume-1 | Issue-2
Deep Convolution Neural Network Model for Credit-Card Fraud Detection and Alert
Volume-3 | Issue-2
Volume - 6 | Issue - 1 | march 2024
Published
26 April, 2024
This work introduces a novel approach aimed at strengthening the effectiveness of phishing detection systems in the face of evolving cyber threats. Leveraging the power of machine learning-based anomaly detection techniques, this proposed mechanism seeks to significantly enhance both the accuracy and adaptability of current detection methods to effectively combat emerging phishing attacks. Central to this methodology is the utilization of ensemble model mechanisms, which intelligently integrate predictions from a diverse array of machine learning models. Through cautious analysis of URLs utilizing distinct datasets, this system systematically compares and contrasts results with established approaches, thereby enriching the overall detection process. This approach showcases notable improvements in performance metrics, boasting higher success rates that substantially exceed conventional heuristic analysis and blacklist-based detection methodologies. By transcending the limitations inherent in traditional detection strategies, this innovative framework represents a promising leap forward in the ongoing battle against phishing exploits, offering enhanced resilience in safeguarding sensitive user information from malicious cyber threats.
KeywordsPhishing Detection Machine Learning Anomaly Detection Ensemble Models Cyber Threats URLs Datasets Performance Metrics Heuristic Analysis Blacklist-Based Detection Emerging Threats Cyber Exploits User Information Malicious Threats
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