CSAI2026 labs: Lab-2..Lab-6 (Practical + Solution, data), Module-2..Module-6 notebooks and datasets
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mikrotik
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "cell-001",
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"metadata": {},
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"source": [
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"# Module 5 — Lab 4: Security Metrics and Error Costs\n",
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"\n",
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" \n",
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"**Topics:** false negatives, class weights, validation threshold, test discipline\n",
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"\n",
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"> **Lab version:** Complete every `TODO`, run the cells in order, and use the self-check before opening the Solution."
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-002",
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"metadata": {},
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"source": [
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"## Learning objectives\n",
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"\n",
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"After completing this lab, you will be able to:\n",
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"\n",
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"- quantify false-negative and false-positive costs;\n",
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"- compare class-weight policies on validation data;\n",
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"- select a probability threshold without inspecting the test set;\n",
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"- evaluate the chosen policy once on held-out test data;\n",
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"- describe the operational trade-off between missed malware and analyst workload."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-003",
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"metadata": {},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"DATA_PATH = Path(\"data/malware_features.csv\")\n",
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"\n",
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"if not DATA_PATH.exists():\n",
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" raise FileNotFoundError(\n",
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" \"Missing data/malware_features.csv. Follow data/README.md, place the three \"\n",
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" \"official UCI CSV files under data/raw/, and run: python data/prepare_dataset.py\"\n",
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" )\n",
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"\n",
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"\n",
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"from sklearn.dummy import DummyClassifier\n",
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"from sklearn.impute import SimpleImputer\n",
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"from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.pipeline import Pipeline\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"\n",
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"\n",
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"def security_metrics(y_true, y_pred):\n",
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" tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel()\n",
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" return {\n",
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" \"accuracy\": accuracy_score(y_true, y_pred),\n",
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" \"malware_precision\": precision_score(y_true, y_pred, zero_division=0),\n",
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" \"malware_recall\": recall_score(y_true, y_pred, zero_division=0),\n",
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" \"malware_f1\": f1_score(y_true, y_pred, zero_division=0),\n",
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" \"false_positives\": int(fp),\n",
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" \"false_negatives\": int(fn),\n",
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" \"false_positive_rate\": fp / (fp + tn) if fp + tn else 0.0,\n",
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" }"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-004",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO 1: load X and y, create a 20% stratified test set, then split the\n",
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"# remaining data into 75% train and 25% validation using random_state=42.\n",
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"df = None\n",
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"feature_cols = None\n",
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"X = None\n",
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"y = None\n",
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"X_train_full, X_test, y_train_full, y_test = None, None, None, None\n",
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"X_train, X_valid, y_train, y_valid = None, None, None, None\n",
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"print(\"Train/validation/test:\", None, None, None)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-005",
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"metadata": {},
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"source": [
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"## 1. Compare class-weight policies on validation data\n",
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"\n",
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"Weighting malware errors more heavily may reduce false negatives, but it can increase false positives. The correct choice depends on the cost of missed malware and available analyst capacity."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-006",
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"metadata": {},
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"outputs": [],
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"source": [
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"weight_policies = {\n",
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" \"unweighted\": None,\n",
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" \"malware_x2\": {0: 1, 1: 2},\n",
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" \"malware_x4\": {0: 1, 1: 4},\n",
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"}\n",
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"\n",
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"# TODO 2: fit one depth-4 tree pipeline per weight policy and collect\n",
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"# validation security_metrics in validation_results.\n",
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"validation_results = None\n",
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"fitted_candidates = None\n",
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"display(validation_results)\n",
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"\n",
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"# TODO 3: select highest recall, breaking ties by lower FPR then higher F1.\n",
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"selected_policy = None\n",
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"selected_model = None\n",
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"print(\"Selected class-weight policy:\", selected_policy)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-007",
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"metadata": {},
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"source": [
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"## 2. Select a validation threshold\n",
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"\n",
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"Search thresholds on validation probabilities only. Prefer thresholds that achieve at least 98% validation malware recall; among them, choose the one with the lowest false-positive rate."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-008",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO 4: obtain validation malware probabilities and evaluate thresholds\n",
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"# from 0.20 through 0.80 in steps of 0.05.\n",
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"validation_probabilities = None\n",
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"threshold_results = None\n",
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"\n",
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"# TODO 5: among thresholds with recall >= 0.98, select the lowest FPR.\n",
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"# If none qualify, use a threshold with maximum recall.\n",
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"selected_threshold = None\n",
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"display(threshold_results)\n",
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"print(\"Selected threshold:\", selected_threshold)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-009",
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"metadata": {},
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"source": [
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"## 3. Refit on all training data and evaluate once on the test set\n",
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"\n",
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"The test set is used only after all policy decisions are fixed."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-010",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO 6: refit the selected policy on X_train_full/y_train_full.\n",
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"final_model = None\n",
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"\n",
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"# TODO 7: compare threshold 0.50 and selected_threshold on the test set.\n",
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"test_probabilities = None\n",
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"default_predictions = None\n",
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"tuned_predictions = None\n",
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"test_results = None\n",
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"display(test_results)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-011",
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"metadata": {},
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"source": [
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"## Self-check"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cell-012",
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"metadata": {},
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"outputs": [],
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"source": [
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"assert set(validation_results.index) == set(weight_policies)\n",
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"assert 0.20 <= selected_threshold <= 0.80\n",
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"assert len(test_probabilities) == len(y_test)\n",
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"assert set(tuned_predictions).issubset({0, 1})\n",
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"assert test_results.shape[0] == 2\n",
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"assert final_model.named_steps[\"tree\"].get_depth() <= 4\n",
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"print(\"✓ Lab 4 completed\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "cell-013",
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"metadata": {},
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"source": [
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"## Reflection questions\n",
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"\n",
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"1. Why must model and threshold choices use validation data rather than test data?\n",
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"2. What operational harm can false negatives and false positives cause?\n",
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"3. Why should the selected threshold be revalidated after deployment conditions change?"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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