Conference TEHI, November 07-08-Samarkhand, Uzbekistan
Cybersecurity & Privacy
Project Title:
AI-Driven Cybersecurity, Threat Intelligence, and Digital Infrastructure Protection
Overview:
This project develops intelligent cybersecurity systems using machine learning to detect, prevent, and mitigate cyber threats across aviation systems, IoT devices, healthcare networks, and enterprise platforms.
Key Research Focus Areas:
-
Phishing and social engineering detection using ML
-
Aviation cybersecurity (ADS-B spoofing and injection detection)
-
IoT security using IEEE-compliant AI frameworks
-
Privacy-aware and explainable cyber-defense systems
Current Outcomes:
-
Real-time ADS-B attack detection models
-
High-accuracy phishing detection systems
-
AI frameworks for securing IoT and medical devices
Funding & Collaboration Relevance:
Strong alignment with DoD, DHS, FAA, DARPA, and critical infrastructure security programs.
Published papers:
|
Paper Name |
Keywords |
|
Artificial Intelligence for Cybersecurity: A State of the Art |
cybersecurity, AI, machine learning, threat detection, adversarial attacks, XAI, quantum computing |
|
Optimizing Intrusion Detection with Hybrid Deep Learning Models and Data Balancing Techniques |
intrusion detection, deep learning, SMOTE, data balancing, IDS, network security, hybrid model |
|
Dynamic Secret Sharing for Enhanced Cloud Security: Tackling Eavesdropping and Threshold Attacks |
secret sharing, cloud security, eavesdropping, threshold attacks, cryptography, distributed systems |
|
Automating Malware Detection and Response via Real-Time Threat Feed Integration with Wazuh SIEM |
malware detection, SIEM, Wazuh, threat intelligence, real-time response, cybersecurity automation |
|
AI-Driven Secure Semantic Communication with Dynamic Encryption for Real-Time Data Integrity |
semantic communication, dynamic encryption, AI security, data integrity, real-time, secure communication |