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Paper ID:IJSREM62173

Red-Team Attack Simulation Dashboard

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM62173

Volume:

10 |

4 |

April 2026

Pages:

1-27

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM62173

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

RB

Rucha Bhalerao

Department of Computer Science (Specialisation in Cyber Security and Forensics)

MIT ADT

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AG

Akshada Gundal

Department of Computer Science (Specialisation in Cyber Security and Forensics)

MIT ADT

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Mentors

AG

Arun Ghandat

Department of Computer Science (Specialisation in Cyber Security and Forensics)

MIT ADT

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Abstract

Modern cyber-attacks are becoming highly sophisticated, multi-staged, and automated, making it difficult for instructors and learners to clearly understand how attacks evolve across different phases of the cyber kill chain. Traditional attack tools such as Nmap, SQLMap, MySQLMap, Metasploit etc. produce fragmented, command-line based outputs which require deep technical expertise to interpret. Because of this, students and analysts struggle to visualize how reconnaissance leads to exploitation, and how payload delivery eventually results in data exfiltration. This gap highlights the urgent need for a unified simulation environment that can visually demonstrate each step of the attack process in a structured and simplified manner. To address this challenge, this research proposes a Red Team Simulation Dashboard, a centralised platform that graphically represents the entire attack flow from initial scanning to final data extraction. The dashboard integrates outputs from commonly used offensive tools and maps them into a visual kill-chain model. This allows users to easily understand which exploit succeeded, which stage failed, and how the attack lifecycle progresses. The system also supports real-time visualization, live log monitoring, and accurate stage tracking, enabling both students and security practitioners to see the immediate outcomes of every step. By combining multiple offensive tool results into one unified interface, the Red Team Simulation Dashboard improves practical learning, reduces complexity, and supports faster interpretation of attack data. It enhances understanding for college-level teaching, cyber-labs, and training scenarios, while also serving as a practical framework for demonstrating real-world red team methodologies. This makes the proposed dashboard a powerful educational and operational tool for modern cybersecurity environments.

KEYWORDS

Keywords: Red Team Simulation Cyber Kill Chain Attack Visualization Offensive Security Tools Real-Time Monitoring

Cite This Article

Rucha Bhalerao, Akshada Gundal, Arun Ghandat, "Red-Team Attack Simulation Dashboard," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 4, pp. 1-27, April 2026. https://doi.org/10.55041/IJSREM62173

Full Article/Conclusion

The proposed Red-Team Attack Simulation Dashboard delivers a modern, automated, and centralized approach for visualizing cyber attack chains in a structured, easy-to-understand manner. By integrating multiple penetration testing tools such as Nmap, SQLMap, Metasploit etc. into a single unified panel, the system enables students, beginners, and professionals to clearly observe how an attacker progresses through reconnaissance, exploitation, privilege escalation, and data exfiltration. Real-time visualization, logs, and stage-wise representation allow learners to connect every attack phase logically instead of seeing raw outputs separately. This makes learning faster and decision-making smarter. The dashboard supports easy report generation, safe simulation environment, and controlled execution of attacks, thus reducing risks and making red teaming more systematic and accessible.

References

[1] R. S. M. G. de Lima et al., "Red Team Cutting Edge: Centralised UI for Red Team Tools" — introduces RTK, all of which combines Feroxbuster, N map, SQLMap, and Nmap into an individual desktop application to streamline Red Team execution for novices as well as experienced users. [2] “Visual Assessments for Cyber Red Teaming” by J. Hernandez, B. Turnbull, and J. Yuen — highlights an automated red team technology that creates visual evidence of infrastructure while participating multi-stage assault scenarios. [3] CyberNX's article, "Top Red Team Tools, Commercial and Open-Source in 2025," examines and talks about the features, benefits, drawbacks, along with capabilities of several red-teaming applications the fact that will be widely accessible in 2025. [4] "A Case Presentation of the Red Team: From Sticky Note to Full Cloud Compromise" by Filip Dimitrov —reveals the need for more thorough research by showing the means by which a minor earliest vulnerability transformed into a full cloud hacking attempt. [5] The article "Unique Attack Vectors and Red Team Operations" focusses on typical TTPs that the red teams are using to comprehend malicious user actions, including phishing, infection with spyware uploading, particularly cyberspace breaching. [6] D. Caivano et al., "Red Team Knife (RTK): Unified Offensive Tool Interface" — Describes how RTK streamlines instruction, storytelling, and execution by combining the various offensive features into a single user experience. [7] J. Yuen et al., "Visual Analytics and Optimisation for Cyber Red Teaming" — Talks concerning how to improve situational awareness of red working teams by using dashboards with graphics and graph-first attack attack chain perspectives. [9] Vladimir Dimitrov, "Red Team Case Study: Sticky Note to Cloud Collapse" — Shows how simple flaws which have been missed might result in a full-scale system breach in intricate environment. [10] "Red Team Strategy and Key Attack Vectors" — focusses on establishing the main strike routes along with describes how hostile visualisation aids with reaction strategy planning for corporate entities.

Editorial & Review Transparency

Publication Date

5/1/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

The authors declare no conflict of interest related to this research work.

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