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Minerva: A Robust File-Based Ransomware Detector

Core Concepts
Minerva is a novel, robust approach to ransomware detection that constructs file-based behavioral profiles to identify malicious activity, and is designed to be resilient against evasion attacks.
The paper presents Minerva, a novel ransomware detection approach that leverages file-based behavioral profiling to identify malicious activity. Minerva is designed to be robust against evasion attacks, with architectural and feature selection choices informed by their resilience to adversarial manipulation. The key insights behind Minerva are: Ransomware must ultimately encrypt user files to achieve its objective, so monitoring file-level behavior can detect malicious activity regardless of how tasks are distributed across processes. Different aspects of file-based behavioral profiles are interconnected, so attempts to alter one aspect to evade detection will trigger detectable changes in others. Minerva employs a multi-tier architecture that monitors file activity across different time windows, using an ensemble of machine learning classifiers to detect malicious behavior. The paper conducts a comprehensive analysis of Minerva's performance against traditional, evasive multiprocess, and unseen ransomware, as well as adaptive ransomware specifically engineered to evade Minerva's detection. The results demonstrate Minerva's ability to accurately identify ransomware, generalize to unseen threats, and withstand evasion attacks, with remarkably low detection times.
"Minerva detects ransomware activity on average within 0.52 seconds of the onset of malicious activity." "Minerva achieves over 99% true positive rate and true negative rate against traditional and evasive multiprocess ransomware."
"Minerva is engineered to be robust by design against evasion attacks, with architectural and feature selection choices informed by their resilience to adversarial manipulation." "Minerva effectively guards against traditional ransomware, evasive multiprocess ransomware, and adaptive ransomware engineered specifically to evade Minerva's detection."

Key Insights Distilled From

by Dorjan Hitaj... at 04-17-2024
Minerva: A File-Based Ransomware Detector

Deeper Inquiries

How can Minerva's file-based behavioral profiling approach be extended to detect other types of malware beyond ransomware?

Minerva's file-based behavioral profiling approach can be extended to detect other types of malware by adapting the feature selection and training process to capture the unique behaviors exhibited by different malware variants. Here are some ways to extend Minerva's approach: Feature Engineering: Modify the features used in the behavioral profiling to encompass behaviors specific to different types of malware. For example, for malware that focuses on data exfiltration, features related to network activity and data transfer can be included. For malware that targets system files, features related to system calls and file modifications can be emphasized. Training on Diverse Datasets: Train the classifiers on diverse datasets containing various types of malware samples. By exposing the model to a wide range of behaviors, it can learn to differentiate between benign and malicious activities across different malware categories. Adversarial Training: Incorporate adversarial training techniques to expose the model to potential evasion tactics used by different malware variants. By training the model against adversarial examples, it can become more robust and resilient to evasion attempts. Dynamic Updating: Implement a system that continuously updates the behavioral profiles based on new malware samples and evolving behaviors. This adaptive approach ensures that the model stays effective against emerging threats. Collaborative Defense: Integrate Minerva's approach with other malware detection systems to create a comprehensive defense strategy. By combining file-based behavioral profiling with signature-based detection and anomaly detection, a more holistic view of the system's security can be achieved.

What are the potential limitations or drawbacks of Minerva's contrastive design approach, and how could they be addressed?

Minerva's contrastive design approach offers robustness against evasion attacks, but it may have some limitations that need to be considered: Overfitting to Specific Evasion Techniques: There is a risk that the model may become overly specialized in detecting specific evasion tactics, making it less effective against new or unknown strategies. To address this, regular updates and diversification of training data can help the model adapt to evolving threats. Complexity and Interpretability: The contrastive design approach may introduce complexity to the model, making it harder to interpret and explain the decision-making process. To mitigate this, techniques such as feature importance analysis and model explainability tools can be employed to enhance transparency. Resource Intensive: Implementing a contrastive design approach may require more computational resources and training time compared to simpler detection methods. Optimizing the model architecture and training process can help mitigate these resource constraints. Adversarial Transferability: Adversaries may attempt to transfer knowledge gained from evading one model to evade Minerva's contrastive design. Regularly updating the model and incorporating ensemble learning techniques can help counter this threat.

How could Minerva's techniques be integrated with other security measures, such as data backup and recovery systems, to provide a comprehensive defense against ransomware attacks?

Integrating Minerva's techniques with other security measures can enhance the overall defense against ransomware attacks. Here are some ways to achieve this comprehensive defense: Early Detection and Isolation: Use Minerva for early detection of ransomware activity, triggering immediate isolation of affected systems to prevent further spread. Simultaneously, backup systems can be activated to restore encrypted data from a clean state. Behavioral Analysis for Recovery: After ransomware containment, leverage Minerva's behavioral profiling to analyze the extent of the attack and identify any residual malicious activity. This information can guide the restoration process and ensure that all traces of ransomware are eliminated. Incident Response Automation: Integrate Minerva with incident response automation tools to streamline the response to ransomware incidents. Automated actions can be triggered based on Minerva's detection alerts, such as isolating infected systems, blocking malicious processes, and initiating recovery procedures. Continuous Monitoring and Updates: Implement continuous monitoring of system behavior using Minerva to detect any resurgence of ransomware activity. Regular updates to the model and behavioral profiles can enhance detection capabilities and adapt to new ransomware variants. User Awareness and Training: Educate users on ransomware threats and best practices for data protection. Combine Minerva's detection capabilities with user training to create a proactive defense strategy that involves both technology and human vigilance. By integrating Minerva's file-based behavioral profiling approach with data backup and recovery systems, incident response automation, continuous monitoring, and user training, organizations can establish a robust defense mechanism against ransomware attacks.