Phishing Detection Classifier with Scoring and Protection
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Solution Overview
Problem
Current methods fail to effectively detect phishing websites, which often mimic legitimate sites to obtain sensitive information, leading to potential identity theft, as they rely on blacklists that are difficult to maintain and update.
Innovation Solution
A system utilizing a classifier trained with specific training data and a whitelist to identify phishing attempts by analyzing website features and generating a score to determine the likelihood of a website being legitimate or fraudulent, with a protection component to block access to suspicious sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If blacklists are used to detect phishing websites, then detection capability is provided, but the system becomes difficult to maintain and update
Solution Approach 1:
The system automatically generates phishing detection rules by analyzing phishing website characteristics and training data, eliminating the need for manual blacklist maintenance. The rule generation component creates detection rules autonomously based on learned patterns from training data, allowing the system to self-update and adapt to new phishing techniques without human intervention.
2Measurement precision
If machine learning classification is implemented, then phishing detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system is divided into distinct functional components: training data management, rule generation, classification, scoring, and protection. Each component performs a specific task in the phishing detection pipeline, making the overall complex system manageable through modular design. The classifier component specifically handles the machine learning classification task separately from other system functions.
Solution Approach 2:
The rule generation component acts as an intermediary that translates complex machine learning training data into simplified detection rules that can be applied by the classifier. This intermediary layer bridges the gap between the complex training process and the simpler classification operation, managing system complexity by creating an intermediate representation of phishing characteristics.
3Object-affected harmful factors
If access to suspicious websites is blocked, then user protection is improved, but potential legitimate access may be denied
Solution Approach 1:
The system applies partial blocking by denying access only to websites that exceed the phishing probability threshold, while allowing access to websites below the threshold. This partial action approach provides protection against confirmed phishing sites while maintaining flexibility for potentially legitimate sites, avoiding excessive blocking that would deny all uncertain cases.
Solution Approach 2:
The scorer component provides feedback by generating a phishing probability score that informs the protection component's access decision. This feedback mechanism allows the system to adjust its blocking behavior based on the calculated risk level, enabling flexible response that balances protection with legitimate access needs.
Data Source
AI summary
A system and method for identifying a phishing website is disclosed. Content associated with a website that a user is attempting to access is retrieved and translated into a format that a classifier can process. The classifier is trained to identify phishing attempts for a particular website or family of websites. The classifier processes the website to determine if the website is a phishing website. A scorer can determine the likelihood that the classifier classified the website correctly. If the website is determined to be a phishing website a protection component can deny access to the website. Otherwise the user can be permitted to access the website.