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aethel_core — Next-gen logical WAF engine built in SWI-Prolog. Features an inductive learning brain running at 2M+ LIPS with an integrated recursive decoder to neutralize nested URL/Hex/HTML obfuscations in real-time. | Kitploit
Next-gen logical WAF engine built in SWI-Prolog. Features an inductive learning brain running at 2M+ LIPS with an integrated recursive decoder to neutralize nested URL/Hex/HTML obfuscations in real-time.
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.
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 40,000-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).
Total Inferences Executed: 100,201,875 logical reasoning steps inside the main memory.
Peak Induction Speed: 7,024,009 LIPS (Logical Inferences Per Second).
Volatile RAM Footprint: ~4.2 MB (4,270 KB allocated, 3,754 KB in use), berjalan sangat stabil di platform RAM 8.0 GB.
Garbage Collection Overhead: 7 core garbage collections dan 24 clause garbage collections diselesaikan dalam 0.000 seconds murni.
JIT Hashing Performance: Terindeks penuh pada 276,717 JITI Rules dalam 4,096 memory buckets, mengunci akselerasi mutlak pada 2,538.4x Speedup Factor.
🛡️ 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.)
🔒 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.
lokinpendawa/aethel_core
