A Comprehensive Web-Based Platform for Phishing Message Detection

Thepersistent threat of phishing attacks looms large over the digital sphere, posing indiscriminate risks to users and organizations alike, leading to substantial financial losses and data breaches. In response to this growing menace, this paper introduces an innovative web platform engineered specifically to combat the escalating sophistication of phishing messages. At the heart of its design lies the fusion of advanced machine learning algorithms, natural language processing techniques, and behavioural analysis, facilitating the real-time identification and mitigation of phishing attempts. Distinguishing itself from prior solutions, our platform embraces a multifaceted strategy, amalgamating URL analysis, email content scrutiny, and user behaviour tracking to pinpoint malicious messages accurately, thereby fortifying cybersecurity resilience. Through meticulous testing and validation, our platform demonstrates exceptional effectiveness in thwarting phishing assaults, surpassing prevailing methodologies by a significant margin. Featuring an intuitive user interface, the platform equips both individuals and enterprises with the tools to swiftly assess the legitimacy of incoming communications, thereby reducing susceptibility to phishing scams. Our methodology represents a notable departure from traditional approaches, presenting a comprehensive solution adept at countering the evolving tactics employed by cyber adversaries. In sum, this web platform emerges as a pivotal advancement in the battle against phishing threats, fortifying cybersecurity defences in an increasingly precarious online environment.

Authors

Publication Details

Journal
International Innovative Research Journal of Engineering and Technology
Published
2024-09-30
DOI
https://doi.org/10.32595/iirjet.org/v10i1.2024.196
Primary Topic
Spam and Phishing Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

A Comprehensive Web-Based Platform for Phishing Message Detection

Suhaib Abdullah Hamed Albishri, Tasnim Hamed Fadhl Alkhaziri, Malath Salim Khalfan AlYahmadi
International Innovative Research Journal of Engineering and Technology
Spam and Phishing Detection
article

A Comprehensive Web-Based Platform for Phishing Message Detection

Suhaib Abdullah Hamed Albishri, Tasnim Hamed Fadhl Alkhaziri, Malath Salim Khalfan AlYahmadi
article en

Abstract

Thepersistent threat of phishing attacks looms large over the digital sphere, posing indiscriminate risks to users and organizations alike, leading to substantial financial losses and data breaches. In response to this growing menace, this paper introduces an innovative web platform engineered specifically to combat the escalating sophistication of phishing messages. At the heart of its design lies the fusion of advanced machine learning algorithms, natural language processing techniques, and behavioural analysis, facilitating the real-time identification and mitigation of phishing attempts. Distinguishing itself from prior solutions, our platform embraces a multifaceted strategy, amalgamating URL analysis, email content scrutiny, and user behaviour tracking to pinpoint malicious messages accurately, thereby fortifying cybersecurity resilience. Through meticulous testing and validation, our platform demonstrates exceptional effectiveness in thwarting phishing assaults, surpassing prevailing methodologies by a significant margin. Featuring an intuitive user interface, the platform equips both individuals and enterprises with the tools to swiftly assess the legitimacy of incoming communications, thereby reducing susceptibility to phishing scams. Our methodology represents a notable departure from traditional approaches, presenting a comprehensive solution adept at countering the evolving tactics employed by cyber adversaries. In sum, this web platform emerges as a pivotal advancement in the battle against phishing threats, fortifying cybersecurity defences in an increasingly precarious online environment.

International Innovative Research Journal of Engineering and Technology
Openalex Percentile: Top 64%
Spam and Phishing Detection
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