Machine Learning and AI in Phishing Detection

By: KUKUTLA TEJONATH REDDY, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan,


Phishing attacks, with their ever-increasing deceptiveness and scope, pose a serious threat to individuals and organizations in the digital age [1]. Traditional detection methods are struggling to keep up with the growing strategies of cybercriminals and the implementation will continue. This abstract explores how state-of-the-art technologies, especially a combination of machine learning (ML) and artificial intelligence (AI) methods, use ML algorithms such as Decision Trees and Support Vector Machines to develop phishing detection methods [1][5]. Convolutional Neural Networks (CNNs) intensity) and Regenerative Neurons (RNNs).). of protection by detecting deviations from behavioural norms It adds another dimension [1][4]. This abstract explains how the combination of ML and AI not only increases the accuracy of phishing detection but also provides a dynamic shield against incoming threats, ensuring a secure digital environment for carriers to deal with all types of organizations [1][2][3][6][7].


Phishing attacks have become increasingly sophisticated, targeting individuals and organizations. As the digital landscape evolves, so do the techniques used by cybercriminals. Traditional phishing detection methods are struggling to keep up with these improvements. But the combination of machine learning (ML) and artificial intelligence (AI) techniques has paved the way for innovative and effective solutions [1]. In this article, we explore the areas of machine learning algorithms, deep learning techniques, natural language processing (NLP) will be used. strategies, and behavioural analysis, in order to combat the constant threat of phishing attacks [1][3].

Using Machine Learning Systems to Detect Phishing:

Machine learning algorithms, capable of recognizing patterns from data, have shown incredible promise in trying to catch detected abuse [1][5]. Algorithms such as decision trees, random forests, and support vector machines search for features extracted from email websites. These attributes include URLs, IP addresses, and message content [1]. Using supervised learning, these algorithms can identify legitimate transactions from phishing attempts. The continuous learning process allows these algorithms to adapt to new phishing techniques, increasing their accuracy over time.

Natural Language Processing Techniques for Phishing Content Analysis:

The language used in phishing email websites is often subtle enough to convey their malicious intent. Natural language processing (NLP) techniques including sentiment analysis, named object recognition, and speech modelling, can reveal these clues [1][4]. Sentiment analysis measures the emotional tone of a message, and detects inconsistencies that could indicate a predation attempt [1]. The recognized named company identifies the specific companies mentioned in the communication, and helps distinguish legitimate companies from suspect companies [1]. Language modelling techniques such as N-grams and hidden passwords provide analysis of language structures to help identify phishing content

Behaviour analysis and anomaly detection using AI:

Phishing attacks generally use human behaviour patterns [1]. AI-powered behavioural analytics monitor user activity to establish a baseline of appropriate behaviour. Deviations from this baseline, flagged as anomalies, could indicate a possible phishing attempt. In addition to machine learning algorithms, especially unsupervised learning techniques such as clustering and autoencoders, which analyse these anomalies to identify suspicious activities [1][7], AI algorithms are able to identify patterns in real time, enabling responses faster on emerging phishing threats.


In the ever-changing cybersecurity threat landscape, it is important to stay one step ahead of cybercriminals. Machine learning and artificial intelligence provide powerful weapons in the fight against phishing attacks. By leveraging this technology, organizations can strengthen their security, protect sensitive information, and ensure a secure digital environment. As this technology advances, the future of phishing detection holds promising possibilities, promising a more secure online experience for individuals and businesses.


  1. Agarwal, S., Agarwal, S., Shukla, S., & Tyagi, V. (2017). Phishing Websites Detection Using Machine Learning Techniques: A Comprehensive Review. In 2017 International Conference on Intelligent Sustainable Systems (ICISS)
  2. Saridakis, A. I., & Gritzalis, P. P. (2016). PhishAri: Automatic Real-Time Phishing Detection on Twitter. In 2016 IEEE Security and Privacy Workshops (SPW).
  3. Vishal, N., Rautaray, S. S., & Gupta, B. (2015). Combating Phishing Attacks: A Survey. In 2015 15th International Conference on Computational Science and Its Applications (ICCSA).
  4. Zhang, Y., Huang, L., Wang, J., Zhang, P., & Cao, T. (2019). Deep Learning for Phishing Detection and Malicious URL Classification: A Survey. IEEE Access, 7, 30614-30628.
  5. Singh, S., Gupta, S. K., & Bharti, P. K. (2017). Phishing Detection Using Machine Learning Algorithms. In 2017 International Conference on Computing, Communication and Automation (ICCCA).
  6. Selamat, A., Zainal Abidin, H. S., & Shamsuddin, A. (2010). A Machine Learning-Based Phishing Detection and Classification Approach. In 2010 International Conference on Information Retrieval & Knowledge Management (CAMP).
  7. Goh, L. H., Rizal, A. F. R. M., Rautaray, S. S., & Wong, E. K. (2015). Detecting Phishing Websites in Real-Time Through an Automated Learning System. In 2015 IEEE 17th International Conference on High-Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (HPCC/CSS).
  8. Alieyan, K., Almomani, A., Anbar, M., Alauthman, M., Abdullah, R., & Gupta, B. B. (2021). DNS rule-based schema to botnet detection. Enterprise Information Systems, 15(4), 545-564.
  9. Deveci, M., Pamucar, D., Gokasar, I., Köppen, M., & Gupta, B. B. (2022). Personal mobility in metaverse with autonomous vehicles using Q-rung orthopair fuzzy sets based OPA-RAFSI model. IEEE Transactions on Intelligent Transportation Systems.
  10. Chopra, M., Singh, S. K., Gupta, A., Aggarwal, K., Gupta, B. B., & Colace, F. (2022). Analysis & prognosis of sustainable development goals using big data-based approach during COVID-19 pandemic. Sustainable Technology and Entrepreneurship, 1(2), 100012.
  11. Zulkefly, N. A., Ghani, N. A., Hamid, S., Ahmad, M., & Gupta, B. B. (2021). Harness the global impact of big data in nurturing social entrepreneurship: A systematic literature review. Journal of Global Information Management (JGIM), 29(6), 1-19.
  12. Gupta, B. B., & Lytras, M. D. (2022). Fog-enabled secure and efficient fine-grained searchable data sharing and management scheme for IoT-based healthcare systems. IEEE Transactions on Engineering Management.

Cite As

REDDY K. T. (2023) Machine Learning and AI in Phishing Detection, Insights2Techinfo, pp.1

60130cookie-checkMachine Learning and AI in Phishing Detection
Share this:

Leave a Reply

Your email address will not be published.