# Project Description: Dynamic Analysis of Malware Using Artificial Neural Networks
Overview
The increasing sophistication of cyber threats necessitates advanced methodologies for malware detection and analysis. This project aims to leverage artificial neural networks (ANNs) to perform dynamic analysis of malware, focusing particularly on identifying malicious behavior through the analysis of parent process hierarchy. By understanding how processes interact and the relationships that develop in a potentially infected environment, we can significantly enhance our ability to detect and respond to malware effectively.
Objectives
1. To develop an artificial neural network model that can learn to recognize patterns of behavior characteristic of malicious software through dynamic analysis.
2. To establish a robust feature extraction process that captures the parent process hierarchy and other relevant attributes during the execution of a program in a controlled environment.
3. To evaluate the effectiveness of machine learning classifiers in detecting malware by comparing them with traditional signature-based methods.
4. To create a comprehensive dataset of benign and malicious processes, annotated according to their behavior and hierarchy, to train and test the ANN models.
5. To provide insights into the relationship between parent processes and child processes, enhancing understanding of malware propagation and behavior during its lifecycle.
Methodology
1. Data Collection
– Malware Sample Acquisition: Gather a diverse set of malware samples from reputable repositories while ensuring legal and ethical compliance.
– Benign Sample Acquisition: Collect a set of benign applications representing typical software installations to serve as a control group for analysis.
– Dynamic Analysis Environment: Set up a controlled virtual environment (e.g., using Sandbox technologies) to execute the applications for dynamic analysis without risking the host system.
2. Feature Extraction
– Process Hierarchy Analysis: During execution, capture the parent-child relationship of processes. This includes information such as:
– Process IDs (PIDs)
– Parent Process IDs (PPIDs)
– Creation times
– Resource usage patterns (e.g., CPU, memory)
– Behavioral Metrics: Register additional metrics such as file access patterns, registry modifications, network activity, and API calls to provide a complete view of the malware’s behavior.
3. Model Development
– Artificial Neural Network Design: Create an ANN architecture suitable for classification tasks. This may include:
– LSTM (Long Short-Term Memory) networks for sequential dependencies
– Convolutional Neural Networks (CNNs) adapted for hierarchical data representation
– Feature Selection: Use techniques like PCA (Principal Component Analysis) or autoencoders to reduce dimensionality while preserving critical information.
4. Training and Testing
– Dataset Splitting: Divide the dataset into training, validation, and testing sets. Utilize cross-validation techniques to ensure the model is well-generalized.
– Model Training: Employ various optimization algorithms (like Adam or SGD) to train the ANN on the feature set obtained from the dynamic analysis.
– Performance Evaluation: Use metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to assess the performance of the model against known malicious and benign samples.
5. Analysis and Interpretation
– Model Interpretation: Use techniques such as SHAP (SHapley Additive exPlanations) to interpret the model’s predictions and understand which features are most influential in classifying processes as benign or malicious.
– Behavioral Insights: Analyze the identified behaviors of malicious processes to provide actionable insights for improving existing detection methodologies.
Expected Outcomes
– A functional ANN model capable of accurately identifying malicious behavior based on dynamic analysis of process hierarchy.
– A validated dataset of both benign and malicious samples, categorized by behavior and structure.
– Comprehensive documentation of the research findings, methodologies, and the potential for further research into anomaly-based detection strategies.
Conclusion
This project stands at the intersection of cybersecurity and machine learning, aiming to create a novel approach to malware detection through the understanding of process relationships. By harnessing the power of ANNs, we strive to offer a more proactive, intelligent defense mechanism against an ever-evolving threat landscape. Our advanced dynamic analysis model can provide significantly improved detection rates and insights into malware behavior, ultimately contributing to safer digital environments.
