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bayesian-fusion

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基于 UNSW-NB15 的双阶段贝叶斯融合入侵检测系统。从零手写 梯度下降 (GD)、SGD、Adam 等基础优化算法,并独立实现 逻辑回归、高斯朴素贝叶斯、决策树、多层感知机 (MLP) —— 无任何第三方深度学习框架。 系统采用 两阶段级联架构:Stage 1 二分类判定攻击/正常,Stage 2 九分类细分攻击类型,最终通过 贝叶斯后验概率融合 输出 10 类检测结果。在预处理中引入 数值清洗、偏度变换、类别降基及交互特征工程,并结合 分层重采样 缓解严重类别不平衡。训练支持早停与 L2 正则化。端到端评估涵盖 ROC/PR 曲线、10×10 混淆矩阵、Top‑K 置信度分析及误差案例诊断,最终 10 类 macro‑F1 达 0.44,所有优化与模型代码完全手写!

  • Updated May 20, 2026
  • Jupyter Notebook

NASA — IRIS: In-situ Rover Intelligent System. Public reference implementation of operational rover flight software for autonomous in-situ water-state detection on Mars and Moon surfaces. Bayesian-Neural sensor fusion + power-and-thermal exploration planner + NASA Trek operator surface.

  • Updated Jun 13, 2026
  • Python

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