PHASE 1
Analysis
Uncovering the Hidden Threats: Analyzing Malware for a Safer Digital World
- Behavioral Analysis
- Static and Dynamic Insights
- Threat Identification
- Reverse Engineering
- Enhanced Defense Strategies
PHASE 2
Ontology
Structuring the Semantics: Organizing Malware Knowledge for Smarter Security
- Systematic Classification
- Relationship Mapping
- Standardized Terminology
- Enhanced Analysis Efficiency
- Integration with AI Models
PHASE 3
Evaluation
Measuring Intelligence: Ensuring AI Delivers Reliable Cybersecurity Solutions
- Performance Assessment
- Robustness Testing
- Scalability Analysis
- Bias Detection
- Continuous Improvement
PHASE 2
Visulisation
Tracking Threats Over Time: Visualizing Malware Evolution for Informed Decisions
- Trend Analysis
- Dynamic Insights
- Visual Clarity
- Predictive Capabilities:
- Enhanced Decision-Making
QUICK ANSWERS
Frequently asked questions
This project aims to enhance cybersecurity by analyzing malware, developing a structured ontology, and using AI and longitudinal visualization to understand and predict the evolution of cyber threats.
Cybersecurity professionals, organizations, and researchers looking for advanced tools to analyze threats, predict attacks, and strengthen defenses against evolving malware can greatly benefit from this project.
A malware ontology is a structured classification of malware types, characteristics, and relationships. It provides a consistent framework for understanding and analyzing threats, enabling better collaboration and decision-making
AI is employed to evaluate, predict, and identify malware behaviors with high accuracy. The models are continuously refined through rigorous evaluation to ensure reliability and scalability
Longitudinal visualization tracks and illustrates malware trends over time, helping organizations identify evolving threats and anticipate future attacks.
