Deep Learning Enabled Cybersecurity Risk Assessment for Microservice Architectures

Kernekoncepter

Proposing the CyberWise Predictor framework for predicting and assessing cybersecurity risks in microservice architectures using deep learning-based natural language processing models.

Resumé

The article introduces the CyberWise Predictor framework, focusing on cybersecurity risk assessment in microservice architectures. It addresses challenges in security risk assessment and proposes a solution using deep learning-based models. The content is structured as follows:

Introduction to Microservice Architectures
Challenges of Microservices in Cybersecurity
Proposed Solution: CyberWise Predictor Framework
Taxonomy of Microservice Vulnerabilities
Architecture of CyberWise Predictor
Data Imputation with Deep Learning Models
Experimental Results and Model Performance Evaluation

Statistik

Our framework achieves an average accuracy of 92% in predicting vulnerability metrics.
A total of 2395 vulnerabilities were detected in the Sock Shop benchmark application.
RoBERTa model demonstrated high accuracy rates ranging from 0.896 to 0.944 across different CVSS metrics.

Citater

Vigtigste indsigter udtrukket fra

Towards Deep Learning Enabled Cybersecurity Risk Assessment for Microservice Architectures

by Majid Abduls... kl. arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15169.pdf

Dybere Forespørgsler

How can the CyberWise Predictor framework be adapted for other cybersecurity domains?

The CyberWise Predictor framework can be adapted for other cybersecurity domains by following a similar approach of utilizing deep learning-based natural language processing models to analyze vulnerability descriptions and predict security risks. The key steps would involve:

Data Collection: Gather relevant data specific to the cybersecurity domain of interest, including vulnerability descriptions and associated metrics.
Model Selection: Choose an appropriate deep learning model based on the nature of the data and task at hand. Fine-tune the model using domain-specific datasets.
Training and Evaluation: Train the model on the collected data, validate its performance through testing, and adjust parameters as needed.
Risk Assessment: Implement a risk assessment methodology that incorporates both predicted metrics from the model and ground truth data to calculate overall risk scores.

How can insights from this research be applied to enhance overall cybersecurity strategies beyond microservice architectures?

Insights from this research can be applied in broader cybersecurity strategies by:

Improving Vulnerability Prediction: Utilize deep learning models to predict vulnerabilities across different systems or applications, enhancing proactive threat identification.
Enhancing Risk Assessment: Incorporate predictive modeling techniques into existing risk assessment frameworks to fill gaps in missing data and improve accuracy in evaluating security risks.
Automating Security Analysis: Develop automated tools that leverage NLP models for analyzing security-related text data, enabling faster detection of potential threats.
Optimizing Incident Response: Use predictive analytics to anticipate potential cyber incidents based on historical patterns, allowing organizations to proactively strengthen their defenses.

What are the potential limitations or biases introduced by using deep learning models for cybersecurity risk assessment?

Potential limitations or biases introduced by using deep learning models for cybersecurity risk assessment include:

Data Bias: Models may learn biased patterns present in training data, leading to skewed predictions or reinforcing existing prejudices within security assessments.
Overfitting: Deep learning models may overfit on training data if not properly regularized, resulting in poor generalization capabilities when exposed to new unseen instances.
Interpretability: Complex deep learning architectures like transformers may lack interpretability, making it challenging for stakeholders to understand how decisions are made by these models.
4 .Adversarial Attacks: Deep learning models are susceptible to adversarial attacks where malicious inputs are crafted specifically to deceive them into making incorrect predictions.