Cybersecurity & NLP
Word-embedding approach for unknown attributes in access control model
With the rapid advancements in computing and information technologies, access control models have become increasingly essential as the first line of defense. However, many traditional methods require significant human intervention. While these rule-based approaches, crafted by experienced system engineers, are highly reliable, they are also time-consuming and dependent on human resources that may not always be available. As an alternative, the attribute-based access control model provides greater flexibility in addressing the authorization needs of complex and dynamic systems. Nevertheless, many existing approaches fail to capture the contextual meaning of attribute values, as those values are typically typically treated as categorical data. This paper proposes modifying our Token2Vec to handle newly added tokens without additional training. Our experiments, conducted on real-world datasets, demonstrate the effectiveness of our approach by comparing it against State-of-the-Art models and evaluating its performance in evolving system scenarios. Our new approach has proved to be effective with over 93% accuracy in all scenarios where all tokens are known, and retains its performance in scenarios where a portion of the data is not presented in the training process.
Executive Impact at a Glance
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Problem Statement
Traditional access control models rely heavily on human intervention and struggle with the dynamic, contextual nature of attribute values. This leads to inefficiencies and security vulnerabilities, especially in complex enterprise environments. The challenge is to move beyond rigid, rule-based systems to intelligent, adaptive authorization.
- High human intervention in traditional ACL, RBAC, ABAC.
- Difficulty in interpreting contextual meaning of attribute values.
- Inefficiencies and errors in large organizations.
- Lack of flexibility for dynamic systems (APIs, IoT, Cloud).
Word Embeddings for Access Control
Word embeddings, a technique from Natural Language Processing (NLP), convert language tokens into dense numerical vectors that capture semantic relationships. This approach allows systems to understand the 'meaning' of user roles, resource descriptions, and access requests, enabling adaptive, context-aware access control systems. It moves beyond rigid rules to interpret intent and context.
- Transforms categorical data into semantically meaningful numerical vectors.
- Captures contextual meaning, not just exact matches.
- Enables adaptive, context-aware authorization.
- Successfully applied in cybersecurity for threat detection, phishing, intrusion.
FastText for Unknown Attributes
FastText, an extension of Word2Vec, is chosen for its ability to generate embeddings for previously unseen tokens. It does this by breaking words into n-grams and averaging their vectors. This is critical for evolving access control systems where new attributes are constantly introduced without requiring full model retraining. This maintains confidentiality and reduces resource demands.
- Handles unknown tokens by leveraging n-grams.
- Avoids retraining the entire embedding layer for new attributes.
- Preserves training data confidentiality.
- Minimizes resource demands and maintains optimized performance.
Gradient Boosting Tree (XGBoost)
For classification, Gradient Boosting Tree (specifically XGBoost) is employed. This ensemble learning technique sequentially builds weaker decision trees to correct errors, often outperforming Random Forest. Despite increased training complexity and time, XGBoost is a robust and versatile model, making it a reliable choice for predicting and classifying tabular datasets derived from access control attributes.
- Ensemble learning technique, builds trees sequentially.
- Corrects errors of previous iterations, robust and versatile.
- Outperforms Random Forest in many scenarios.
- Reliable for classifying tabular datasets from access control attributes.
95.27%
Achieved F1-score with FastText on 128k dataset (balanced)
Access Control Model with Token Embedding
Our proposed methodology for building an adaptive access control system using token embeddings.
User Request with Attributes
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Token Embedding (FastText)
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Numerical Vector Representation
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Classification Model (XGBoost)
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Authorization Decision
| Metric | DLBAC | Token2Vec | FastText Approach |
|---|---|---|---|
| Accuracy (%) | 82.09 | 93.28 | 95.27 |
| Precision (%) | 82.63 | 93.37 | 95.30 |
| Recall (%) | 82.05 | 93.27 | 95.26 |
| F1-score (%) | 82.00 | 93.28 | 95.27 |
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95.38%
Achieved Accuracy with FastText on 32k dataset (skewed)
Case Study: Handling Unknown Tokens in an Evolving System
Scenario: In a cloud system with a central node and multiple task nodes, new attributes (tokens) are constantly introduced as the system evolves. The challenge is to maintain robust access control without retraining the central node's embedding layer for every new token.
Solution: FastText's n-gram based embedding allows it to generate approximate vectors for unknown tokens without additional training. This capability ensures that task nodes can process novel access requests, maintaining high accuracy and system fluidity. The classification model (Gradient Boosting Tree) is then trained on these embeddings.
Outcome: Simulations demonstrate that FastText retains over 93% accuracy even when new tokens are not present in the initial training data, significantly outperforming Token2Vec in handling novel inputs. This reduces administrative overhead and enhances security in dynamic environments.
| Method | Accuracy (%) | Precision (%) | Recall (%) | F1-score (%) |
|---|---|---|---|---|
| Token2Vec Group 0 | 92.09 | 92.21 | 91.98 | 92.06 |
| FastText Group 0 | 94.46 | 94.52 | 94.39 | 94.44 |
| Token2Vec Group 1 | 90.74 | 90.90 | 90.51 | 90.65 |
| FastText Group 1 | 94.15 | 94.31 | 93.97 | 94.10 |
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Your AI Implementation Roadmap
A phased approach to integrate word embedding for access control into your enterprise.
Phase 1: Discovery & Strategy
Engage with your team to understand current access control challenges, data structures, and security objectives. Define project scope, identify key attributes for embedding, and establish success metrics. Validate data readiness and identify potential biases.
Phase 2: Data Preparation & Embedding Training
Collect and preprocess historical access logs. Train the FastText embedding layer on your central node data. Generate initial vector representations for known attributes, focusing on optimal vector dimensions and n-gram sizes. Establish protocols for handling unknown tokens.
Phase 3: Model Development & Integration
Develop and train the Gradient Boosting Tree classification model using the generated embeddings. Integrate the model into a proof-of-concept access control system. Implement mechanisms for new attribute value integration without retraining the central embedding layer.
Phase 4: Testing, Validation & Refinement
Conduct rigorous testing across balanced and imbalanced datasets, including scenarios with newly introduced attributes. Evaluate performance against State-of-the-Art models using accuracy, precision, recall, and F1-score. Refine model parameters and embedding strategies based on evaluation results.
Phase 5: Deployment & Monitoring
Deploy the enhanced access control system. Implement continuous monitoring for performance, bias detection, and new token identification. Establish a feedback loop for periodic model updates and human oversight, ensuring adaptive and secure authorization in evolving enterprise environments.
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