KS-Arafat/Anomaly-Detection

This is a zero-star, single-fork academic/educational repository focused on explainable anomaly detection for network intrusion. The core concept—applying interpretable ML to network security—is well-established in both academia and industry. Critical defensibility factors: (1) No adoption signal (0 stars, zero commits in 175 days, no velocity) suggests this is a completed coursework or thesis project with no active maintenance or community; (2) The problem domain (intrusion detection, explainability) is saturated by well-funded incumbents (Darktrace, Fortinet, Palo Alto Networks, Splunk) and cloud providers (AWS GuardDuty, Azure Security Center, Google Cloud Threat Detection) who have moved explainability into their platforms; (3) The approach is a standard application of existing interpretable ML techniques (SHAP, LIME, tree-based models) to a well-known dataset class (NSL-KDD, CICIDS2017, etc.); (4) No evidence of novel architecture, novel dataset, or novel interpretation methodology—this appears to be a straightforward proof-of-concept. Platform threat is high because AWS, Google Cloud, and Microsoft are rapidly embedding explainability into their security services, and OpenAI/Anthropic-backed security startups are doing the same. Market consolidation risk is high because enterprise security buyers consolidate vendors heavily; a point solution in IDS would need acquisition or integration with a larger platform to reach critical mass. The 6-month horizon reflects that explainable IDS is actively being built into major SIEM/SOC platforms right now. This project has no defensible position without either: (a) proprietary datasets, (b) breakthrough interpretability technique, or (c) significant production deployment and community—none of which are evident.