Subash-1507/Data_Mining_and_MachineLearning_project_new

This is a personal learning project (0 stars, 0 forks, 1 day old) combining well-established components: Isolation Forest (commodity anomaly detection), Streamlit (standard dashboard framework), and RAG (increasingly commoditized via LLM APIs). The README describes the *idea* rather than a working implementation—no code quality assessment possible from README alone. The approach of using Isolation Forest for system anomalies + RAG for explainability is a logical composition but not novel; both techniques are standard in enterprise SOC tooling. Frontier labs (OpenAI, Anthropic, Google) have already productized similar capabilities: CrowdStrike, Datadog, and Splunk all offer anomaly detection + AI-powered investigation. An LLM-based system could replicate this entire stack in a single prompt chain. The specificity to IT infrastructure (CPU, network, login events) provides minimal defensibility—these are commoditized metrics in any SIEM. The RAG layer depends on publicly available threat intel (MITRE ATT&CK, OWASP), adding no proprietary moat. Without evidence of novel detection logic, proprietary data, or achieved traction, this scores as a prototype-stage derivative work that would be trivial for a frontier lab to subsume as a feature (e.g., OpenAI's API + Streamlit, or Anthropic's Claude as the explainability backbone).