Attack Path Modelling and Lateral Movement Analysis

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  Network Attack Analyzer dashboard showing interactive graph-based network visualization for modeling and analyzing potential attack paths.

Inspiration

Modern cyber attacks are no longer single-step exploits; they involve multi-stage lateral movement inside networks. While learning cybersecurity and working on platforms like TryHackMe, we observed that attackers often move through systems by exploiting trust relationships and privilege escalation. This inspired us to build a system that can simulate how an attacker navigates a network and identify vulnerable paths before exploitation.

What it does

Our project is a web-based platform that models enterprise networks as graphs and analyzes potential attack paths. It identifies how an attacker can move from an entry point to critical assets (like domain controllers) using shortest path algorithms. The system highlights the most dangerous attack path and incorporates privilege-based analysis to simulate realistic attacker behavior.

How we built it

We designed the system using a graph-based approach where nodes represent network devices and edges represent connections. Implemented Dijkstra’s Algorithm to compute shortest attack paths Built an interactive network visualization interface using JavaScript and graph libraries Developed a network customization module to create real-world scenarios Added privilege-based evaluation to select the most critical path Created a report generation module for analysis output

Challenges we ran into

Modeling real-world networks in a simplified graph structure Handling multiple shortest paths and selecting the most relevant one Designing an intuitive UI for network visualization Integrating algorithm logic with real-time visualization

What we learned

Practical implementation of graph algorithms in cybersecurity Understanding of lateral movement and attack techniques Frontend visualization using graph libraries System design and modular architecture

What’s next

Integrating real vulnerability data (CVE-based weighted graphs) Adding AI-based risk prediction Deploying on cloud for scalability Building automated security recommendations Impact This project helps organizations think like attackers and proactively identify weak points in their network, improving overall security posture.

Built With

• css3
• d3.js-(or-vis.js)
• dijkstra?s-algorithm
• git
• github
• graph-data-structures
• html5
• javascript
• localhost