Anti-Phishing Adaptive AI Systems: Efficiently Countering Social Engineering Attacks by Real-Time Analysis of Email Content

• Published in: 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES) pp. 1 - 6
• Main Authors: Joseph, Arokia Suresh Kumar, Srinivasan, Sathiyandrakumar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 24.04.2025
• Subjects: Accuracy; Adaptation models; Adaptive systems; Artificial intelligence; Blockchains; Electronic mail; Email Content Analysis; Natural language processing; Natural Language Processing (NLP); Phishing; Phishing Attack Prevention; Phishing Detection Algorithms; Phishing Email Classification; Real-Time Phishing Detection; Real-time systems; Social Engineering Attacks; Vectors
• DOI: 10.1109/ICCIES63851.2025.11032758

Attacks using phishing, the most common kind of social engineering, are getting smarter all the time; they target human weaknesses to steal personal data. The most common vector for these types of assaults is phishing emails that appear to come from trusted sources in an effort to deceive recipients into giving up sensitive information or installing harmful programs. Because they are built on static rule-based detection systems and signature-based methodologies, traditional anti-phishing solutions can't keep up with the ever-changing tactics used by attackers. In order to effectively combat phishing assaults, this study presents adaptive AI systems that use real-time email content analysis. These AI systems are able to detect tiny indications of harmful behaviour by seamlessly analysing the context, content, and intent of email messages using advanced machine learning (ML) algorithms and natural language processing (NLP) techniques. Metadata, links, attachments, and the message's semantic structure are all parts of structured and unstructured email content that the system examines for phishing signs. Adaptive AI systems may continually enhance their detection skills over time, unlike traditional models that depend on predetermined criteria. This is because they can learn from evolving phishing techniques. In addition, these systems can detect zero-day phishing assaults and other types of phishing that haven't been seen before, making them a more effective and agile defence. Improving detection accuracy while lowering false positives, which generally plague anti-phishing efforts, is the subject of this research, which further explores the use of real-time analysis. Finding a happy medium between system openness and user privacy and incorporating AI systems into preexisting email infrastructure are other issues covered in the research. Improved detection of complex phishing attempts and subsequent faster and more effective incident responses are achieved by the suggested adaptive AI systems through a mix of supervised and unsupervised learning models. In its last section, the article delves into the possible future applications of anti-phishing technologies powered by artificial intelligence and how they could improve cybersecurity for a wider variety of online communication channels.