Cybersecurity Lab & PCAP Dataset Creation

Title: Cybersecurity Lab Setup + Structured PCAP Dataset (Strict Project Workflow) Description: I need a freelancer to complete the initial phase of a cybersecurity project, strictly following a structured workflow until a properly generated and labeled PCAP dataset is produced. This is NOT a simple PCAP generation task — the work must follow the correct academic methodology step-by-step. Scope of Work (Must Follow This Order): 1. Lab Design & Setup Build an isolated virtual lab environment (VirtualBox or VMware only) Use 3–4 virtual machines: Kali Linux → Attacker Windows Server → Target server Windows Client → Normal user traffic (Optional but preferred) Ubuntu/Security Onion → Traffic monitoring Configure: Internal/host-only network (fully isolated) Clear IP addressing scheme Proper role separation (attacker vs normal vs target vs monitor) 2. Network Traffic Generation You must generate both types of traffic in a controlled way: Normal Traffic: Web browsing (HTTP/HTTPS) File transfers Basic user activity simulation Attack Traffic: SYN Flood (e.g., hping3) UDP Flood (e.g., nping) OR equivalent realistic attacks 3. Centralized Traffic Capture (MANDATORY) Use a dedicated monitoring machine OR configured capture point Capture ALL traffic flowing between machines Tools: Wireshark OR tcpdump The capture must include: Normal traffic Attack traffic Mixed traffic (important) 4. PCAP Dataset Creation (Final Goal) Produce clean PCAP file(s) that include: Clearly distinguishable normal and attack traffic Dataset must be: Structured and usable for later ML processing Minimum ~1000+ packets (more is better) 5. Labeling & Documentation Clearly explain: When normal traffic was generated When attacks were launched Provide: Simple labeling reference (timestamps or notes) Steps followed from lab setup → PCAP generation Deliverables: Final PCAP file (main deliverable) Short documentation (lab setup + traffic generation steps) Screenshots: VM setup Network configuration Traffic capture in progress

Project ID: 40420321