ENTERPRISE AI ANALYSIS
Detecting Malicious .NET Executables Using Extracted Methods Names
This analysis explores a novel framework for detecting malicious .NET executables by statically extracting method names. It leverages machine learning models to classify malware with high accuracy, addressing the gap in .NET-specific malware detection.
Executive Impact Summary
Our advanced AI framework provides a significant leap in cybersecurity, offering precise and efficient detection of .NET malware.
0
Detection Accuracy
0
F1-Score Confidence
0
Reduction in False Positives
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Our innovative framework statically extracts .NET method names, preprocesses them, and applies machine learning for robust malware detection.
Enterprise Process Flow
Collect PE Samples
→
Extract .NET Samples
→
Extract Standard .NET Methods
→
Feature Selection
→
Dataset Creation
→
ML Training
→
Results Evaluation
| Feature | Static Analysis | Dynamic Analysis |
|---|---|---|
| Execution | No execution required, analyzes code without running it. | Requires executing the malware in a controlled environment. |
| Resource Use | Generally low computational overhead and faster. | High resource consumption, time-consuming for setup and monitoring. |
| Obfuscation Handling | Challenging; obfuscation can hide true functionality. | Effective; can reveal behavior of obfuscated code during runtime. |
| .NET Specificity | Tailored approach possible by analyzing CIL and metadata. | Often relies on generic Windows API calls, less .NET-specific. |
The evaluation demonstrated XGBoost as the top-performing model, achieving high accuracy and F1-scores, indicating the reliability of method names as features.
96.16%
Peak Detection Accuracy Achieved by XGBoost
| Model | Accuracy | F1-Score |
|---|---|---|
| XGBoost | 96.16% | 96.15% |
| Random Forest | 95.36% | 95.28% |
| SVM | 95.16% | 95.08% |
| Logistic Regression | 95.30% | 95.27% |
| KNN | 90.73% | 90.71% |
| Naïve Bayes | 88.66% | 88.24% |
While effective, the current model struggles with obfuscated samples. Future work will focus on multi-class classification and deep learning architectures.
Addressing Evolving Threats
Our current framework, relying on static analysis, faces challenges with highly obfuscated or packed .NET malware. These techniques are designed to bypass static detectors by concealing malicious functionality. Future work will expand into multi-class malware classification, leveraging deep learning architectures like RNNs and LSTMs. We also plan to explore advanced tokenization methods such as Word2Vec to further enhance detection capabilities and adapt to sophisticated evasion tactics.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating advanced AI solutions for threat detection.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A clear path to integrating advanced AI for .NET malware detection into your enterprise infrastructure.
Phase 1: Discovery & Assessment
Conduct a comprehensive audit of existing security infrastructure, identify .NET application landscape, and define specific detection goals. This includes analyzing current malware detection efficacy and identifying integration points.
Phase 2: Data Engineering & Model Customization
Develop tailored data pipelines for .NET executable analysis and method name extraction. Customize machine learning models with specific feature selection and training on enterprise-specific data to optimize detection for unique environments.
Phase 3: Integration & Deployment
Seamlessly integrate the AI detection framework into your existing security operations center (SOC) tools and workflows. Deploy the model in a test environment for rigorous validation before full-scale production rollout.
Phase 4: Monitoring, Optimization & Training
Continuously monitor model performance, update datasets with emerging threats, and fine-tune algorithms for adaptive defense. Provide training for security teams to leverage the new AI capabilities effectively.
Ready to Enhance Your Cybersecurity?
Leverage cutting-edge AI to protect your .NET applications from advanced threats. Schedule a consultation to discuss a tailored strategy for your enterprise.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat