AI-Driven Phishing Detection Systems: Protecting Users Against Sophisticated Social Engineering Attacks
Abstract
Phishing remains a prevalent form of cyberattack, posing significant risks to individuals and organizations. This study investigates an AI-based approach to detect phishing attempts in real time by analyzing email content, URLs, and sender behaviors. Using natural language processing and deep learning, our model classifies phishing emails with high accuracy, identifying subtle patterns often missed by traditional filters. Through extensive testing on recent phishing datasets, our results demonstrate that AI-driven systems can significantly reduce phishing attack success rates, thereby enhancing user security and trust in digital communication.
Downloads
References
Balantrapu, S. S. (2021). A Systematic Review Comparative Analysis of Machine Learning Algorithms for Malware Classification. International Scientific Journal for Research, 3(3), 1-29.
Balantrapu, S. S. (2020). AI-Driven Cybersecurity Solutions: Case Studies and Applications. International Journal of Creative Research In Computer Technology and Design, 2(2).
Balantrapu, S. S. (2022). Evaluating AI-Enhanced Cybersecurity Solutions Versus Traditional Methods: A Comparative Study. International Journal of Sustainable Development Through AI, ML and IoT, 1(1), 1-15.
Balantrapu, S. S. (2022). Ethical Considerations in AI-Powered Cybersecurity. International Machine learning journal and Computer Engineering, 5(5).
Balantrapu, S. S. (2021). The Impact of Machine Learning on Incident Response Strategies. International Journal of Management Education for Sustainable Development, 4(4), 1-17.
Balantrapu, S. S. (2019). Adversarial Machine Learning: Security Threats and Mitigations. International Journal of Sustainable Development in Computing Science, 1(3), 1-18.
Pillai, S. E. V. S., Polimetla, K., Avacharmal, R., & Perumal, A. P. (2022). Mental health in the tech industry: Insights from surveys and NLP analysis. JOURNAL OF RECENT TRENDS IN COMPUTER SCIENCE AND ENGINEERING (JRTCSE), 10(2), 22-33.
Deekshith, A. (2020). AI-Enhanced Data Science: Techniques for Improved Data Visualization and Interpretation. International Journal of Creative Research In Computer Technology and Design, 2(2).
Deekshith, A. (2019). Integrating AI and Data Engineering: Building Robust Pipelines for Real-Time Data Analytics. International Journal of Sustainable Development in Computing Science, 1(3), 1-35.
Boppiniti, S. T. (2022). Exploring the Synergy of AI, ML, and Data Analytics in Enhancing Customer Experience and Personalization. International Machine learning journal and Computer Engineering, 5(5).
Boppiniti, S. T. (2020). Big Data Meets Machine Learning: Strategies for Efficient Data Processing and Analysis in Large Datasets. International Journal of Creative Research In Computer Technology and Design, 2(2).
Boppiniti, S. T. (2021). Real-Time Data Analytics with AI: Leveraging Stream Processing for Dynamic Decision Support. International Journal of Management Education for Sustainable Development, 4(4).
Boppiniti, S. T. (2019). Machine Learning for Predictive Analytics: Enhancing Data-Driven Decision-Making Across Industries. International Journal of Sustainable Development in Computing Science, 1(3).
