Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
Volume-2 | Issue-4
Augmented Reality in Education
Volume-2 | Issue-4
A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
Volume-2 | Issue-2
IoT based Biotelemetry for Smart Health Care Monitoring System
Volume-2 | Issue-3
Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
Volume-2 | Issue-4
GUI based Industrial Monitoring and Control System
Volume-3 | Issue-2
AUTOMATION USING IOT IN GREENHOUSE ENVIRONMENT
Volume-1 | Issue-1
Principle of 6G Wireless Networks: Vision, Challenges and Applications
Volume-3 | Issue-4
Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
Volume-3 | Issue-2
Light Weight CNN based Robust Image Watermarking Scheme for Security
Volume-3 | Issue-2
VIRTUAL REALITY GAMING TECHNOLOGY FOR MENTAL STIMULATION AND THERAPY
Volume-1 | Issue-1
Design of Digital Image Watermarking Technique with Two Stage Vector Extraction in Transform Domain
Volume-3 | Issue-3
Analysis of Natural Language Processing in the FinTech Models of Mid-21st Century
Volume-4 | Issue-3
PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
Volume-3 | Issue-3
Image Augmentation based on GAN deep learning approach with Textual Content Descriptors
Volume-3 | Issue-3
Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 5 | Issue - 4 | december 2023
Published
30 November, 2023
With the escalating threats in the digital landscape of cybersecurity, the rapid and widespread of masked and phishing URLs poses a significant threat to online users. Detecting these malicious URLs is a paramount concern to safeguard sensitive information and prevent unauthorized access. This study delves into the application of machine learning algorithms for the accurate identification of masked and phishing URLs. Specifically, Decision Tree, Random Forest, and XGBoost algorithms are employed to create predictive models capable of distinguishing between legitimate and malicious URLs. The research involves the collection of a comprehensive dataset comprising both legitimate and various forms of malicious URLs. Feature engineering techniques are applied to extract relevant information from the URLs, transforming them into numerical representations suitable for machine learning. The three selected algorithms are individually trained and finetuned using the dataset, exploiting their unique capabilities to distinguish patterns indicative of phishing attempts and masked URLs. The performance of each model is evaluated using metrics such as accuracy, precision, recall, and web traffic. This study examines the application of machine learning algorithms to identify masked and phishing URLs. By comparing the results of these algorithms, a predictive model capable of distinguishing between legitimate and malicious URLs is created. Experimental results showed promising accuracy rates and potential to contribute to online security efforts. The implications of this research extend to advanced cybersecurity systems, offering enhanced protection against evolving threats in the digital domain.
KeywordsMasking URL Phishing Cybersecurity Social Engineering Machine Learning
Full Article PDF