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It represents a paradigm shift from polling-based monitoring to real-time kernel telemetry using **eBPF (CO-RE)**, augmented by a **Hybrid AI Brain**.\n\n## 🚀 Key Features\n\n- **🧠 Hybrid Brain Architecture**:\n    - **Rule Engine**: Deterministic detection using Sigma-compatible YAML rules.\n    - **Anomaly AI**: Unsupervised Isolation Forest for detecting statistical outliers.\n    - **Sequence AI**: Hidden Markov Models (HMM) for detecting malicious command chains.\n- **👁️ eBPF Vision**:\n    - High-performance kernel telemetry using `sys_enter_execve` tracepoints.\n    - Implemented as a lightweight **Go Sidecar** (Probe) for minimal overhead.\n- **🕸️ Distributed Orchestration**:\n    - Kubernetes DaemonSet ready.\n    - Heartbeat and Alert reporting via REST API.\n    - Centralized Threat Intelligence integration.\n- **⚡ High Performance**: \n    - Capable of processing 600k+ EPS on a single core (Rule Engine).\n    - Adaptive sampling for AI modules in high-load scenarios.\n\n## 💡 Why SharpEye?\n\n| Feature | Traditional IDS (Snort/Suricata) | Legacy HIDS (OSSEC/Wazuh) | **SharpEye (Gen 4)** |\n| :--- | :--- | :--- | :--- |\n| **Visibility** | Network Packets | Log Files / Syscalls (Auditd) | **Kernel Tracepoints (eBPF)** |\n| **Performance** | High (Deep Packet Inspection) | Medium (Auditd Overhead) | **Extreme (Zero-Copy)** |\n| **Detection** | Signatures Only | Rules + File Integrity | **Hybrid (Sigma Rules + AI)** |\n| **Unknown Threats**| ❌ Misses 0-days | ❌ Misses logic bugs | ✅ **Detects Anomalies** |\n| **Deployment** | Network Tap | Heavy Agent | **Lightweight Sidecar** |\n\n## 🏗️ Architecture\n\nSharpEye follows a decoupled **Brain-Eye** architecture:\n\n- **The Eye (Probe)**: A Go binary running eBPF programs. It captures kernel events and forwards them to user space via Perf Buffers.\n- **The Brain (Engine)**: A Python process that ingests the event stream, applies detection logic, and manages state (sessions, network connections).\n\nSee [ARCHITECTURE.md](docs/ARCHITECTURE.md) for deep dive.\n\n## 📦 Installation\n\n### Prerequisites\n- Linux Kernel 5.8+ (BTF enabled recommended)\n- Docker / Kubernetes\n\n### Quick Start (Docker)\n```bash\n# Build the container (Multi-stage build)\ndocker build -t sharpeye:latest -f Dockerfile.k8s .\n\n# Run in privileged mode (Required for eBPF)\ndocker run --privileged \\\n  -v /sys/kernel/debug:/sys/kernel/debug \\\n  -v /lib/modules:/lib/modules:ro \\\n  sharpeye:latest\n```\n\n### Kubernetes Deployment\n```bash\nkubectl apply -f k8s/daemonset.yaml\n```\n\n## 🧠 AI Capabilities\n\nSharpEye features a self-learning mechanism:\n1.  **Cold Start**: Automatically collects the first 1000 process events to train a baseline model.\n2.  **Online Learning**: Retrains models every 1000 events to adapt to drift.\n3.  **Sequence Analysis**: Tracks `PPID` sessions to detect anomalous command chains (e.g., `curl` -\u003e `chmod` -\u003e `exec`).\n\n## 🤝 Contributing\n\nWe welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for details on setting up the dev environment.\n\n## 📜 License\n\nMIT License. See [LICENSE](LICENSE) for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsginnora%2Fsharpeye","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsginnora%2Fsharpeye","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsginnora%2Fsharpeye/lists"}