Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach
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Light Weight CNN based Robust Image Watermarking Scheme for Security
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Principle of 6G Wireless Networks: Vision, Challenges and Applications
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PROGRESS AND PRECLUSION OF KNEE OSTEOARTHRITIS: A STUDY
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Is Internet becoming a Major Contributor for Global warming - The Online Carbon Footprint
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Augmented Reality in Education
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A Study on Various Task-Work Allocation Algorithms in Swarm Robotics
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IoT based Biotelemetry for Smart Health Care Monitoring System
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Tungsten DiSulphide FBG Sensor for Temperature Monitoring in Float Glass Manufacturing
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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
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Comparative Analysis for Personality Prediction by Digital Footprints in Social Media
Volume-3 | Issue-2
Volume - 3 | Issue - 1 | march 2021
Published
18 May, 2021
Recently, the internet is becoming as the most effective tool to interact with many foreign societies especially during COVID-19 pandemic. Moreover, the digital platform is increasing in many developing countries and at the same time, the chance of fraudulence is also increasing day by day. In the digital world, phishing assaults are emerging as the most common type of social engineering attack. Currently, many websites are targeting to acquire the confidential data, which is stored in websites. Recently, the classification techniques are employed to detect the phishing websites. Many tools are used for anti-phishing purposes; they are blacklist and antivirus software. The confidential data in a fake surrounding has intended the category of leaked data due to the action of attackers. In this scenario, machine learning method is observed as a very effective to classify the phishing and non-phishing web (Uniform Resource Locator) URLs. This classification struggles in classifying the leaked data content-based challenge. Therefore, the proposed algorithm is associated with the content-based classification method along with the rule-based generator algorithm. This research article integrates the content-based classification with a rule-based generator algorithm to improve the overall performance of the system. The updated public online repository called Mendeley dataset is used in the proposed research work. The proposed algorithm is used in 7k phishing and real websites content data for performing feature extraction. The extracted feature is then analyzed with our proposed algorithm to provide better prediction accuracy. Also, the proposed work has concluded that, the associate algorithm has achieved better accuracy, when compared to other existing methods.
KeywordsDeep learning malicious website
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