Semester-wise syllabus for an M.Tech in Cyber security

Semester 1: Core Foundations 

Courses: 

1. Cryptography and Network Security 

   - Symmetric/asymmetric encryption (AES, RSA), hash functions, PKI, SSL/TLS, VPNs, and firewall configurations. 

2. Ethical Hacking and Penetration Testing 

   - Reconnaissance, vulnerability scanning, Metasploit, OWASP Top 10, and Kali Linux tools. 

3. Cyber security Fundamentals 

   - CIA triad, threat modeling, risk assessment, and security policies (NIST, ISO 27001). 

4. Operating System Security 

   - Hardening Windows/Linux systems, SELinux, auditd, and privilege escalation techniques. 

5. Research Methodology 

   - Technical writing, threat intelligence analysis, and ethical considerations. 

Labs: 

- Penetration Testing Lab (Kali Linux, Burp Suite, Nmap) 

- Network Security Lab (Wireshark, Snort IDS/IPS, firewall configuration) 

Semester 2: Advanced Topics & Electives 

Core Courses: 

1. Malware Analysis and Reverse Engineering 

   - Static/dynamic analysis, disassembly (IDA Pro, Ghidra), and sandboxing (Cuckoo Sandbox). 

2. Digital Forensics

   - Disk imaging, memory forensics (Volatility), chain of custody, and forensic tools (FTK, Autopsy). 

Electives (Examples): 

- Cloud Security (AWS/Azure/GCP security, CASB, SASE) 

- IoT and Embedded System Security 

- Blockchain and Cryptocurrency Security 

- Incident Response and Disaster Recovery 

- Cyber security Law and Compliance (GDPR, HIPAA, Cyber Law) 

Labs: 

- Malware Analysis Lab (Cuckoo Sandbox, REMnux) 

- Digital Forensics Lab (Autopsy, Sleuth Kit, Wireshark) 

Semester 3: Specialization & Project Work 

Electives (Examples):

- AI/ML in Cyber security (threat detection, anomaly detection) 

- Industrial Control System (ICS) Security (SCADA, Modbus) 

- Advanced Persistent Threats (APT) Analysis 

- Red Team/Blue Team Operations (adversary emulation, SOC workflows) 

- Quantum Cryptography (post-quantum algorithms, QKD) 

Project/Dissertation: 

- Phase 1: Topic selection (e.g., ransomware detection, zero-day exploit analysis, secure IoT framework), literature review, and proposal. 

- Seminars: Presentations on trends like AI-driven attacks, deep fake threats, or cyber warfare. 

Semester 4: Thesis/Project Completion 

Thesis/Project: 

- Full-time focus on implementation (e.g., building a secure system, malware analysis toolkit, or threat intelligence platform). 

- Final documentation, viva voce defense, and industry collaboration (if applicable). 

Additional Components:

- Industrial Internship (optional, with firms like Palo Alto Networks, CrowdStrike, or CERT-In). 

- Workshops: Training in *SIEM tools* (Splunk, ELK Stack), *threat hunting, or **OSINT frameworks* (Maltego, Shodan). 

Elective Tracks (Specializations): 

1. Ethical Hacking & Offensive Security 

   - Pen testing, exploit development, and red teaming. 

2. Digital Forensics & Incident Response (DFIR) 

   - Cybercrime investigation, forensic tools, and IR playbooks. 

3. Cloud & IoT Security 

   - Securing cloud-native apps, IoT device hardening, and edge security. 

4. AI-Driven Cybersecurity 

   - ML-based anomaly detection, adversarial ML defense.