Whitepaper: AI-based simulation of phishing URLs and content

This whitepaper explores the growing threat of phishing and brand abuse in the digital landscape, where malicious domains and URLs are increasingly used to impersonate brands, steal user data, and compromise corporate security. To combat these evolving cyber threats, data scientists and the AI team at iZOOlogic have developed a sophisticated suite of models designed to identify and mitigate phishing attacks and brand abuse-related domains targeting our clients.

This article highlights the innovative models we have implemented, the methodology behind their development, and their effectiveness in enhancing cybersecurity. Our models leverage cutting-edge techniques, including machine learning, deep neural networks (DNN), logistic regression, and advanced customised computer vision models for logo identification, ensuring high accuracy in detecting and neutralising cyber threats.

 

I. Overview of the Model Suite

Our suite of models is designed to comprehensively detect phishing and brand abuse domains and URLs with exceptional precision. The models are trained in extensive in-house datasets (over 4 million of dataset), ensuring high relevance and accuracy when applied to the specific needs of the organization and customers.

The following are key components of this suite of models:

• Logic Regression: A foundational model used for binary classification tasks, including distinguishing between safe and potentially malicious URLs/domains. By analyzing features like URL length, character types, and domain age, logistic regression helps provide an initial risk score.
• Deep Neural Networks (DNN): We employ Customized DNNs embedding NLP techniques to detect intricate patterns in the structure and content of URLs. DNNs excel in capturing non-linear relationships in the data, making them particularly effective for identifying phishing attacks that might evade simpler models.
• Logo Identification: The Logo identification model is an Advanced Computer Vision Algorithm that detects logos and branding elements within images. This model is crucial for identifying domains and URLs that impersonate brands through visual content. By comparing logos against a database of trusted brands, the model helps identify fraudulent websites mimicking company assets.
• Safe/Unsafe Classification (3-Level): This model classifies URLs into three categories: safe, unsafe, and uncertain. The multi-level classification approach adds a layer of sophistication by distinguishing not just between malicious and non-malicious URLs but also by flagging those with ambiguous risk levels, which require further investigation.
• URL Prediction: This model specifically predicts whether a given URL is likely to lead to a phishing website. The URL prediction model analyzes URL components (e.g., subdomains, query parameters) and checks for patterns commonly associated with phishing URLs.
• Content analyzing (set of multiple models): Our Team have implemented five specialized models that analyze the contents of web page, images and patterns, to identify phishing indicators such as obfuscation techniques, script-based malicious behavior, and unusual coding practices. These models help provide another layer of security by examining the hidden behaviors within the website’s code.
• Supervised Learning: All models are trained using supervised learning techniques, ensuring that they learn to accurately differentiate between phishing and legitimate content based on labeled examples. This methodology provides the foundation for the accuracy and reliability of our predictions.

 

II. Training and Validation Methodology

The development of these models was carried out by our in house team of data scientists, AI engineers. The models were trained using an extensive and continually updated dataset that includes historical data on known phishing and brand abuse incidents, as well as legitimate domains and URLs. The training process involves fine-tuning each model on specific aspects of domain and URL structure, content analysis, and behavior
analysis.

Validation is performed using rigorous cross-validation techniques to ensure that the models generalize well to new, unseen data. This ensures that the models remain effective and accurate, even as attackers continuously evolve their tactics.

 

III. Performance and Accuracy

Our models have achieved an impressive accuracy rate of over 97%, indicating their high reliability in identifying phishing and brand abuse threats. This level of accuracy ensures that our models can predict and prevent a substantial number of potential attacks, providing robust protection to clients and safeguarding brand reputation.

The three-tiered approach—incorporating logistic regression, deep neural networks, and computer vision techniques ensures that we detect threats at multiple levels, from the basic characteristics of the URL to more advanced, visual, and behavioral cues.

 

IV. Impact and Benefits

The integration of these advanced models into our company’s security tools provides several key benefits:

• Enhanced Brand Protection: By accurately detecting impersonation attempts and brand abuse, our models help prevent damage to brand reputation and consumer trust.
• Robust Phishing Defense: The models help identify and block phishing attempts targeting clients, preventing data breaches and financial losses.
• Automated Threat Detection: With over 97% accuracy, the models enable automated detection, reducing the burden on security teams and improving response times.
• Continuous Adaptation: As attackers modify their strategies, our models are continuously updated and retrained with new data, ensuring they stay relevant and effective.