Aethel-Core: Automated Cognitive Defense & Cyber Threat Simulation WAF

A high-fidelity, high-velocity hybrid cyber security system optimized for Real-time Threat Mitigation, LLM Instruction Tuning, and Neuro-Symbolic AI Training, built in SWI-Prolog.

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🚀 Key Innovations & Architecture Modules

1. High-Performance Core WAF Engine (activator.pl)

• Dynamic Backtracking Check: Queries threat signatures in RAM via clean relational lookups.
• Volumetric DDoS Protection: Sliding time-window tracking (htg/2) for aggressive IPs.
• N-Gram Tokenization & Prefix Anchor: Breaks long signatures into granular 3-4 character primitives to defeat extreme Double-Sandwich Obfuscations.

2. Multi-Layered Obfuscation Decoder (decoder.pl)

Runs an inline recursive normalization pipeline: Deep URL Decoding (%XX), Hexadecimal unpacking (\xXX / 0xXX), and HTML entity normalization (< / >) with full case-insensitivity.

⚡ 3. Inductive Learning & Engine Benchmark (Actual Production Metrics)

The system was stress-tested against a 30,000-wave Hyper-Chaos Mutation Attack (v7.0 God Mode). The results showcase extreme processing density under heavy payload fragmentation:

• Dynamic Rules Active in RAM: 276,717 N-Gram Tokenized Rules (Generalized from 15,000 raw threat vectors).
• Security Integrity: 100% Meta-Locked Secure (0 BYPASSED / HOLES).
• Total Inferences Executed: 74,209,589 logical reasoning steps inside the main memory.
• Peak Induction Speed: 6,833,889 LIPS (Logical Inferences Per Second).
• Volatile RAM Footprint: ~2.7 MB (2,718 KB) total allocated state (Ultra-lightweight embedded state).
• Garbage Collection Overhead: 20 core garbage collections completed in 0.000 seconds murni.
• JIT Hashing Performance: Scaled automatically to 4,096 memory buckets, achieving an absolute 2,538.4x Speedup Factor.

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🛡️ Cyber Threat Mutation Matrix & Formats

Covers Layer-7 traffic flooding, Web3 attacks (sandwich_economic_attack), Cloud infrastructure exploits (kubelet_cri_hijack), and cutting-edge Adversarial AI Threats (llm_rag_poisoning, deepseek_weight_poison). Supports fully-normalized JSONL exports for LLM instruction tuning.

(Note: You can check the core interface execution behavior inside the repository references.)

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🔒 Intellectual Property & Licensing

All Rights Reserved. The public repository contains showcase interfaces (activator.pl, decoder.pl). The backend core automation engine and inductive tokenization rules remain proprietary.