Stay ahead of the curve with research-driven insights into AI, blockchain, quantum security, and cutting-edge cybersecurity innovations.

Post-Quantum Cryptography Lab Overview Quantum computing is set to transform the cybersecurity landscape by rendering many current encryption methods obsolete. This project introduces students to post-quantum cryptography (PQC), including hands-on labs exploring key exchange mechanisms, digital signatures, and practical implementation challenges using leading PQC algorithms. What You Will Learn How quantum computers threaten RSA, ECC, and other classical encryption methods Key concepts in post-quantum cryptography, including lattice-based and hash-based algorithms Hands-on experience with NIST PQC candidates such as Kyber and Dilithium Implementation of secure key exchange and digital signatures Benchmarking and comparing PQC algorithms Preparing for a quantum-resistant future in cybersecurity Hands-On Learning Students will gain experience using:

AI & ML for Threat Detection and SOC Automation Overview Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing how security teams detect, analyze, and respond to threats. This project introduces students to the fundamentals of AI and ML in cybersecurity, with a specific focus on anomaly detection and automation within Security Operations Centers (SOCs). What You Will Learn The role of AI and ML in modern cybersecurity operations How supervised and unsupervised ML techniques apply to threat detection Creating and training simple models for anomaly detection in log data Evaluating the performance of ML-based threat detection vs rule-based detection How AI supports SOC automation, alert prioritization, and triage Hands-On Learning Students will gain experience using:

ETR-3: Building Zero Trust Architecture with Open Tools Overview Zero Trust Architecture (ZTA) is a modern security model that assumes no implicit trust—whether inside or outside the network perimeter. This project guides students through the principles of Zero Trust and helps them simulate a secure, identity-driven environment using open tools like pfSense, Docker, and OpenVPN. What You Will Learn Core principles of Zero Trust (least privilege, explicit verification, segmentation) Design patterns for ZTA and common implementation models Hands-on setup of segmented networks and identity-aware policies Integration of access control, monitoring, and secure tunneling in a Zero Trust lab Hands-On Learning Students will gain experience using:

Blockchain Security & Smart Contract Exploitation Overview Blockchain technology introduces new models for decentralized trust, integrity, and data verification—but it also comes with unique vulnerabilities. This project explores blockchain’s role in cybersecurity, with a focus on smart contract security, decentralized identity, and real-world exploits. What You Will Learn How blockchain enhances security through decentralization and immutability Common blockchain attack vectors (e.g., reentrancy, front-running, 51% attacks) Hands-on deployment and testing of smart contracts Identifying and patching vulnerabilities in smart contract code Use cases for blockchain in threat mitigation, identity, and secure logging Hands-On Learning Students will gain experience using:

Implementing Passwordless Authentication with NIST 800-63-4 Overview The new NIST 800-63-4 Digital Identity Guidelines introduce a modern approach to authentication that prioritizes usability, resilience, and assurance. Organizations are encouraged to use longer passphrases, verify passwords against breached lists, and adopt passwordless methods based on WebAuthn and FIDO2. This project provides a hands-on experience implementing passwordless authentication using Keycloak, mapping the setup to NIST’s Authentication Assurance Levels (AAL2 and AAL3). Students will learn how modern identity systems support Zero Trust environments and reduce reliance on passwords entirely.

Simulating an Agentic Cyberattack (Reasoning-Only Lab) Overview Agentic cyberattacks represent the next evolution of adversarial activity, where AI systems no longer assist attackers but act on their own. These systems can plan intrusion paths, adapt to defenses, generate highly targeted phishing content, and optimize their attack strategies in real time. This project transforms the agentic attack concept into a safe, reasoning-only simulation. Students will guide an AI model through a realistic, multi-phase intrusion scenario against a fictional organization. The AI never executes real attacks. Instead, it explains how an autonomous adversary would think, plan, and adjust each stage of the operation.