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   "language": "python",
   "name": "python3"
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  "language_info": {
   "name": "python",
   "version": "3.x"
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  "colab": {
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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 7 - Adversarial Email Generation (for Defense) 🎣🛡️\n",
    "### CIS 350: AI for Cybersecurity - Smart Defense for the Digital Business\n",
    "\n",
    "**Worth:** 50 points  ·  **Estimated time:** ~60 minutes  ·  **Submit:** this `.ipynb` in Canvas\n",
    "**Required software:** Google Colab (or Jupyter) with `pandas`, `scikit-learn`, `numpy` (preinstalled in Colab).\n",
    "**Prerequisites:** Weeks 2-4 (features/labels, train/test, evaluation) and Week 8 (attacker AI).\n",
    "\n",
    "## ⚠️ Ethics & scope (read first)\n",
    "This lab is **purely educational and defensive**. You will generate only **obviously-fake, synthetic** phishing text **in this notebook (a sandbox)** to understand the patterns attackers use - then you will build a **detector**. **Nothing is ever sent. No real people, brands, or links are targeted.** Using these ideas to deceive anyone is prohibited and is an academic-integrity and legal violation.\n",
    "\n",
    "## Background\n",
    "Attackers use AI to mass-produce convincing phishing. To DEFEND, we must understand what those emails look like. You will (1) see how a simple template generator scales fake lures, (2) turn emails into numeric **features**, and (3) train a classifier to **detect** phishing - the same ML workflow you used for spam.\n",
    "\n",
    "## Learning objectives\n",
    "By the end you can: (1) explain how attackers scale phishing; (2) turn emails into detection features; (3) train and evaluate a phishing detector; (4) interpret its false positives/negatives and recommend layered defenses.\n",
    "\n",
    "Run cells top to bottom with **Shift + Enter** and fill every **`TODO`** blank (`____`).\n",
    "\n",
    "> **Academic integrity:** individual or pair work. **AI tools are not permitted** on labs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1 · The attacker's playbook (concept)\n",
    "AI-scaled phishing = **template + personalization + volume**. One attacker can send thousands of unique, believable emails. Our job is to DETECT them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2 · See the scale (sandboxed, fake samples)\n",
    "A tiny template generator prints obviously-fake samples so we can study their structure. **These are never sent.**"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "rng_demo = np.random.default_rng(1)   # separate rng - does not affect the dataset below\n",
    "\n",
    "# Placeholders like [YOUR BANK] and the fake link keep these OBVIOUSLY synthetic.\n",
    "names     = [\"Alex\", \"Jordan\", \"Taylor\", \"Sam\"]\n",
    "brands    = [\"[YOUR BANK]\", \"[SHIPPING CO]\", \"[IT HELPDESK]\"]\n",
    "templates = [\n",
    "    \"Dear {name}, your {brand} account is locked. Verify now: {link}\",\n",
    "    \"{name}, we could not process your {brand} payment. Update details: {link}\",\n",
    "    \"{name}, unusual sign-in to your {brand} account. Confirm identity: {link}\",\n",
    "]\n",
    "FAKE_LINK = \"http://example.test/verify\"   # non-routable sandbox placeholder\n",
    "\n",
    "print(\"--- 3 SYNTHETIC (fake) phishing samples, for study only ---\")\n",
    "for _ in range(3):\n",
    "    t = rng_demo.choice(templates)\n",
    "    print(\"*\", t.format(name=rng_demo.choice(names), brand=rng_demo.choice(brands), link=FAKE_LINK))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 3 · Turn emails into detection FEATURES\n",
    "Defenders measure signals: links, urgency words, credential requests, sender mismatch."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "import numpy as np, pandas as pd\n",
    "rng = np.random.default_rng(8)\n",
    "\n",
    "def synth(n, phish):\n",
    "    \"\"\"Turn each email into 4 defensive FEATURES (what a filter measures).\"\"\"\n",
    "    rows = []\n",
    "    for _ in range(n):\n",
    "        if phish:                                   # phishing tends to...\n",
    "            num_links     = int(rng.integers(0, 4)) #  include links\n",
    "            urgency_words = int(rng.integers(0, 4)) #  use urgent language\n",
    "            asks_creds    = int(rng.random() < 0.85)#  ask for credentials\n",
    "            mismatch      = int(rng.random() < 0.75)#  come from a mismatched sender\n",
    "        else:                                       # legit rarely does\n",
    "            num_links     = int(rng.random() < 0.35)\n",
    "            urgency_words = int(rng.random() < 0.15)\n",
    "            asks_creds    = int(rng.random() < 0.08)\n",
    "            mismatch      = int(rng.random() < 0.08)\n",
    "        rows.append(dict(num_links=num_links, urgency_words=urgency_words,\n",
    "                         asks_credentials=asks_creds, sender_mismatch=mismatch,\n",
    "                         label=\"phishing\" if phish else \"legit\"))\n",
    "    return rows\n",
    "\n",
    "emails = pd.DataFrame(synth(150, True) + synth(150, False))\n",
    "\n",
    "print(emails['label'].value_counts().to_dict())\n",
    "emails.head()"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# TODO 1: compare the average feature values for phishing vs legit.\n",
    "print(emails.groupby(\"label\")[[\"num_links\",\"urgency_words\",\"asks_credentials\",\"sender_mismatch\"]].____())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 📸 Expected output (self-check)\n",
    "Phishing emails should average **higher on all four features**. Your numbers should look like this:\n",
    "\n",
    "![expected feature comparison](attachment:expected_features.png)"
   ],
   "attachments": {
    "expected_features.png": {
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"
    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 4 · Train / test split"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = emails[[\"num_links\",\"urgency_words\",\"asks_credentials\",\"sender_mismatch\"]]\n",
    "y = emails[\"label\"]\n",
    "# TODO 2: hold back 30% to test the detector honestly (stratify keeps balance).\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "        X, y, test_size=____, random_state=0, stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 5 · Train the phishing detector"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score\n",
    "clf = LogisticRegression(max_iter=1000)\n",
    "# TODO 3: train the detector on the TRAINING data.\n",
    "clf.____(X_train, y_train)\n",
    "acc = accuracy_score(y_test, clf.predict(X_test))\n",
    "print(\"detector accuracy:\", round(acc, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 6 · What KIND of mistakes? (confusion matrix)\n",
    "Rows = actual, columns = predicted. Off-diagonal = errors (false alarms vs missed phishing)."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "preds = clf.predict(X_test)\n",
    "# TODO 4: build the confusion matrix (true labels, predictions).\n",
    "print(confusion_matrix(____, preds, labels=[\"legit\",\"phishing\"]))\n",
    "# order = [legit, phishing]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 📸 How to read your matrix\n",
    "Your output is a 2x2 grid. Use this guide to find the **false alarms** and **missed phishing** for reflection Q3 (your exact numbers will vary slightly):\n",
    "\n",
    "![confusion matrix layout](attachment:confusion_layout.png)"
   ],
   "attachments": {
    "confusion_layout.png": {
     "image/png": 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    }
   }
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 7 · Precision & recall (focus on phishing)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_score, recall_score\n",
    "# TODO 5: set pos_label so the metrics focus on the \"phishing\" class.\n",
    "p = precision_score(y_test, preds, pos_label=____)\n",
    "r = recall_score(y_test, preds, pos_label=\"phishing\")\n",
    "print(\"precision:\", round(p, 3), \" recall:\", round(r, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 8 · Reflection questions\n",
    "Double-click and replace each *your answer here*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your answers**\n",
    "\n",
    "1. **From the feature averages, which signals best separate phishing from legit? Why?** *your answer here*\n",
    "2. **What accuracy did the detector reach?** *your answer here*\n",
    "3. **From the confusion matrix, how many false alarms (legit flagged) and missed phishing were there?** *your answer here*\n",
    "4. **As attackers make phishing cleaner, which error (false positive or false negative) is likely to grow, and why?** *your answer here*\n",
    "5. **Detection is not perfect. Name TWO other layers (people/process/tech) that defend against AI phishing.** *your answer here*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ✅ Before you submit\n",
    "- [ ] **Runtime -> Run all** with no errors.  - [ ] All `TODO` blanks filled.\n",
    "- [ ] All 5 reflection questions answered.  - [ ] **File -> Download -> Download .ipynb**, upload to Canvas.\n",
    "\n",
    "## Troubleshooting\n",
    "- `NameError` -> you skipped a cell; run from the top with **Run all**.\n",
    "- `ValueError: pos_label` -> use `pos_label='phishing'` exactly.\n",
    "- Different accuracy than a neighbor -> tiny variation across scikit-learn versions is fine.\n",
    "\n",
    "## Extension (optional, ungraded)\n",
    "Add a new detection feature (e.g., `attachment` or `link_domain_age`) to the dataset and see whether the detector improves. Which feature helps most?"
   ]
  }
 ]
}