Algorithm-Driven Multi-User Platform for Decentralized Coordination in Self-Organizing UAV Swarms

Agit Atashyan — 2025-06-15

This paper introduces a comprehensive software platform designed to coordinate self-organizing UAV swarms through a secure and modular client-server system. Developed with multi-user collaboration in mind, the platform features an intuitive, cross-platform interface that allows users to define mission tasks, construct navigation graphs, and monitor swarm activity in real time.

At the heart of the system is a robust path-planning algorithm based on the rotor-router model with loop reversibility, which enables reliable and evenly distributed task coverage without relying on randomness. To enhance fault tolerance and ensure resilience in communication-limited environments, the platform employs a gossip-based broadcast algorithm. This allows swarm members to share information efficiently and maintain coordinated behaviour, even when some nodes experience failures or connectivity issues.

A built-in simulation module enables users to test and refine swarm coordination strategies before deployment, reducing operational risk and improving mission reliability. By simulating various environmental conditions and mission scenarios, users can evaluate system behaviour and optimize task execution. In parallel, the platform supports real-time 3D panorama generation from UAV-captured images, providing rich visual context and enabling more effective post-mission analysis.

Taken together, these features form a scalable, secure, and highly flexible system for managing decentralized drone swarms. The platform is well-suited for applications that demand coordination across multiple agents, including environmental monitoring, search and rescue, infrastructure inspection, and autonomous exploration. It bridges theoretical rigor with practical usability, offering a reliable toolset for both researchers and mission operators. Our work builds on earlier systems, introducing hybrid rotor-router initialization, algorithmic no-fly zone enforcement, and dual-toolchain image stitching.

Framework for Monitoring Malicious Channels in Phishing Campaigns: A Cyber Threat Intelligence Perspective

Ivo Ricardo Dias Rosa — 2025-06-15

Phishing campaigns are becoming more sophisticated, using encrypted communication platforms like telegram to coordinate their activities and store stolen information. This paper introduces a structured and scalable framework that supports cyber threat intelligence (CTI) efforts in monitoring malicious telegram channels linked to phishing activity.The framework is based on a combination of machine learning and human expertise, and it leverages a large dataset of telegram messages and their corresponding labels to train and evaluate the performance of the model. The methodology involves four interconnected stages: extracting hidden artifacts from phishing code, gaining unauthorized access to private telegram groups, analyzing collected data automatically, and implementing counterintelligence measures to prevent detection. Initial findings validate the framework's ability to detect indicators of compromise (IoCs) and visualize the attacker's infrastructure. Additional functionalities like integration with threat intelligence platforms and artificial intelligence-based data enrichment enhance the framework's versatility and usefulness. This method improves proactive communication monitoring, providing a secure and flexible solution for tracking adversarial communication channels.

International Journal of Electrical and Computer Engineering Research

Algorithm-Driven Multi-User Platform for Decentralized Coordination in Self-Organizing UAV Swarms

Framework for Monitoring Malicious Channels in Phishing Campaigns: A Cyber Threat Intelligence Perspective