Evaluating and Combating the Impact of Concept Drift on the Performance of Machine Learning-Based Phishing Detection Systems
arXiv:2606.11471v1 Announce Type: cross Abstract: The expansion of the digital domain has resulted in a substantial increase in digital communication, with email emerging as one of the most prominent channels. The proliferation of email communication is apparent in both professional and personal contexts, thereby creating numerous vulnerabilities for malicious actors to exploit. Spam emails, a form of unsolicited correspondence often bearing malicious intent towards recipients, have been an ongoing challenge for email users since the inception of email technology, and this problem has been exa
The continuous evolution of AI and the increasing sophistication of malicious actors drive the need for robust and adaptable security measures, making concept drift a critical contemporary challenge.
This research highlights the inherent vulnerability of machine learning security systems to concept drift, which could lead to significant cybersecurity breaches if left unaddressed, impacting data integrity and digital trust.
This research provides methods to evaluate and combat concept drift, potentially improving the resilience and longevity of AI-powered phishing detection systems, shifting focus towards adaptive security models.
- · Cybersecurity firms leveraging adaptive AI
- · Organizations with high digital communication volume
- · Researchers in adversarial AI and machine learning security
- · Organizations relying on static, non-adaptive ML security models
- · Malicious actors whose phishing techniques are countered more effectively
Improved phishing detection rates and reduced incidence of successful cyberattacks.
Increased investment in R&D for real-time adaptive AI security systems and threat intelligence platforms.
Enhanced trust in digital communication channels and potentially new regulatory demands for adaptive cybersecurity measures.
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