Build a distributed password cracking system coordinating GPU-accelerated cracking across multiple machines using job queuing and load balancing, supporting various hash algorithms and providing real-time progress monitoring. This project teaches distributed systems, GPU computing, and demonstrates techniques used in large-scale password recovery operations.
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Understand distributed computing and GPU acceleration for password cracking by learning that password cracking is computationally expensive (billions of hashes tested), GPU acceleration provides 10-100x speedup vs. CPU, and distributing across multiple machines provides linear scaling. Study GPU programming: CUDA for NVIDIA GPUs, OpenCL for cross-platform support. Research distributed job frameworks: Redis queues, RabbitMQ for task distribution, and database backends for state tracking. Understand hash algorithm GPU implementations and their limitations.
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Implement GPU-accelerated hash computation using CUDA or OpenCL: implement MD5, SHA1, SHA256, bcrypt hashing on GPU, testing performance (throughput measured in hashes-per-second). Build custom CUDA kernels optimizing for specific algorithms: batch hashing multiple password candidates in parallel, minimize memory transfers between CPU and GPU (primary bottleneck), and optimize memory layout for cache efficiency. Benchmark performance improvements vs. CPU implementations.
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Build distributed job queue system coordinating cracking jobs across worker machines: implement job submission accepting target hashes, algorithm type, and cracking method (dictionary, brute-force, pattern-based). Create job queue (Redis/RabbitMQ) distributing jobs to available workers, assign sub-ranges of password space to different workers (e.g., worker 1 tries AAA-AAZ, worker 2 tries ABA-ABZ) preventing duplicate work. Track job progress: estimate time remaining, track found hashes, detect stalled workers.
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Create load balancing and worker management distributing work efficiently: dynamically assign jobs to workers based on GPU availability and current load, implement health checking detecting failed workers, redistribute their work to other workers. Implement worker registration: workers report GPU type, hashrate, and availability. Build priority queuing: high-value hashes (system accounts, important users) processed first.
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Implement multiple cracking strategies supporting different attack types: dictionary attacks (hash wordlist, compare hashes), brute-force (generate all character combinations), pattern-based cracking (common patterns like "Password123"), and rainbow table integration (hash precomputed passwords). Allow strategy combination: try common passwords first (fast), then brute-force if unsuccessful. Implement ruleset application: take base words and apply transformations (l33t speak, capitalization variations).
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Build progress monitoring and real-time dashboards tracking cracking operation: show job status (queued, in-progress, completed), display worker statistics (hashes/sec, current assignment, GPU temperature), track found passwords with confidence scores, estimate time-to-completion. Implement logging capturing all hashes cracked, timestamps, and worker information. Create alerts when high-value passwords recovered or unusual activity detected.
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Create result database and post-processing storing and analyzing cracking results: maintain database of recovered passwords, hash types, and algorithms. Implement breach analysis: correlate recovered passwords with user accounts, identify commonly used passwords suggesting weak password policies, detect password patterns (seasons, names, numbers). Generate reports showing password strength distribution and policy violations.
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Build comprehensive documentation explaining distributed cracking methodology, GPU programming basics (not requiring deep CUDA knowledge), and cluster setup/configuration. Discuss ethical and legal considerations: password cracking is illegal without authorization (own systems or explicit permission), explain legitimate uses (recovering lost passwords, security assessments with authorization). Provide security recommendations: strong password policies, salted hashing (bcrypt/argon2), multi-factor authentication reducing password compromise impact. Compare to commercial password recovery tools (Hashcat, John the Ripper), discuss limitations (some algorithms (bcrypt, argon2) deliberately slow down cracking making distributed systems less effective), and explain integration into incident response when recovering compromised credentials.
- GPU programming and CUDA/OpenCL
- Distributed job queuing and scheduling
- Load balancing and worker management
- Password attack methodologies
- GPU performance optimization
- Redis/RabbitMQ for task distribution
- Real-time monitoring and progress tracking
- Database design for cracking results
- Password analysis and reporting
- GPU-accelerated hash computation
- CUDA/OpenCL kernel implementations
- Redis or RabbitMQ job queue
- Distributed worker coordination
- Load balancing and health checking
- Dictionary attack implementation
- Brute-force generation and optimization
- Pattern-based attack strategies
- Real-time progress dashboards
- Results database and analysis
- Password strength reporting