Ingeniería en Ciberseguridad y Gestión de TI · PUCE Virtual
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NvTu9iudki_2"
},
"source": [
"## ¿Cómo usar este notebook?\n",
"\n",
"Cada sección sigue este orden:\n",
"\n",
"| Bloque | Qué encontrarás |\n",
"|--------|----------------|\n",
"| 📘 **Recordatorio teórico** | Conceptos clave que necesitas antes de programar |\n",
"| 💻 **Ejemplo resuelto** | Código completo listo para ejecutar |\n",
"| ✏️ **Ejercicio** | Código con espacios `___` que tú debes completar |\n",
"\n",
"**Para ejecutar una celda:** selecciónala y presiona `Shift + Enter`.\n",
"\n",
"> ⚠️ Ejecuta siempre la celda de preparación (siguiente) antes de cualquier otra."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "o-8BuuCYki_2",
"outputId": "56af2fa2-c480-4f38-b4bc-e3d564b46359"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Todo listo. Puedes continuar.\n"
]
}
],
"source": [
"# ── Preparación: librerías ──────────────────────────────────────────────────\n",
"import math\n",
"import random\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Estilo uniforme para todas las gráficas\n",
"plt.rcParams.update({\n",
" 'axes.spines.top': False,\n",
" 'axes.spines.right': False,\n",
" 'axes.grid': True,\n",
" 'grid.color': '#eeeeee',\n",
" 'grid.linewidth': 0.8,\n",
" 'font.size': 11,\n",
"})\n",
"\n",
"print('✅ Todo listo. Puedes continuar.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iJtD55I4ki_4"
},
"source": [
"---\n",
"## Sección 1 — Dato vs. Información: ¿cuándo un número dice algo?\n",
"\n",
"
\n",
"📘 Recordatorio teórico
\n",
"En la Teoría de la Información de Shannon, la diferencia entre dato e información es precisa:
\n",
"
\n",
"
Dato: símbolo emitido por una fuente. Sin contexto, no reduce la incertidumbre del receptor.
\n",
"
Información: surge cuando el dato es interpretado dentro de un marco de referencia y disminuye la incertidumbre de quien lo recibe.
\n",
"
\n",
"Un conjunto de datos solo se convierte en información cuando permite responder una pregunta, tomar una decisión o anticipar un comportamiento del sistema.\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OtkmTTrzki_5"
},
"source": [
"### 💻 Ejemplo resuelto 1.1 — Sistema de autenticación con intentos fallidos\n",
"\n",
"Un sistema de control de acceso registra el número de intentos de login fallidos por usuario en los últimos 10 minutos. El dato bruto es solo un número; se convierte en información cuando lo comparamos contra un umbral definido por la política de seguridad."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "HLZNu03mki_6"
},
"outputs": [],
"source": [
"# Política de seguridad de acceso\n",
"UMBRAL_ALERTA = 3 # más de 3 intentos → alerta\n",
"UMBRAL_BLOQUEO = 5 # más de 5 intentos → bloqueo automático\n"
]
},
{
"cell_type": "code",
"source": [
"# Registro del sistema (dato bruto)\n",
"registros = [\n",
" ('jlopez', 1),\n",
" ('mperez', 4),\n",
" ('agarcia', 6),\n",
" ('drojas', 2),\n",
" ('kmorales', 5),\n",
" ('pperez', 7)\n",
"]\n",
"registros\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bWD6jSJllKb6",
"outputId": "410f12a5-772a-486e-db7d-788b9bfffbc5"
},
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[('jlopez', 1),\n",
" ('mperez', 4),\n",
" ('agarcia', 6),\n",
" ('drojas', 2),\n",
" ('kmorales', 5),\n",
" ('pperez', 7)]"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"# CRÍTICO >= UMBRAL_BLOQUEO\n",
"# ALERTA >= UMBRAL_ALERTA\n",
"# NORMAL < UMBRAL_ALERTA\n",
"\n",
"\n",
"print(f\"{'Usuario':<12} {'Intentos':>8} {'Estado del acceso':<22} {'Acción recomendada'}\")\n",
"print('-' * 72)\n",
"\n",
"for usuario, intentos in registros:\n",
" if intentos > UMBRAL_BLOQUEO:\n",
" estado = '🔴 CRÍTICO'\n",
" accion = 'Bloquear cuenta y notificar al SOC'\n",
" elif intentos > UMBRAL_ALERTA:\n",
" estado = '🟡 ALERTA'\n",
" accion = 'Solicitar verificación adicional'\n",
" else:\n",
" estado = '🟢 NORMAL'\n",
" accion = 'Ninguna'\n",
" print(f\"{usuario:<12} {intentos:>8} {estado:<22} {accion}\")\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "HqHwLmY3lMOS",
"outputId": "a388bfd8-d04a-486a-dfbd-83fdd674b7c8"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Usuario Intentos Estado del acceso Acción recomendada\n",
"------------------------------------------------------------------------\n",
"jlopez 1 🟢 NORMAL Ninguna\n",
"mperez 4 🟡 ALERTA Solicitar verificación adicional\n",
"agarcia 6 🔴 CRÍTICO Bloquear cuenta y notificar al SOC\n",
"drojas 2 🟢 NORMAL Ninguna\n",
"kmorales 5 🟡 ALERTA Solicitar verificación adicional\n",
"pperez 7 🔴 CRÍTICO Bloquear cuenta y notificar al SOC\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"print()\n",
"print('→ El número de intentos por sí solo es un dato.')\n",
"print(' Compararlo contra la política de seguridad lo transforma en información útil.')"
],
"metadata": {
"id": "lssHwQyCl-zo"
}
},
{
"cell_type": "markdown",
"metadata": {
"id": "EAeqfRCwki_6"
},
"source": [
"### ✏️ Ejercicio 1.1 — Sensor de intrusión en sala de servidores\n",
"\n",
"Un sensor de movimiento físico en la sala de servidores de SecureData Corp envía señales cada hora. La política establece que **entre las 22:00 y las 06:00** cualquier movimiento detectado debe generar una alerta, ya que es horario no autorizado de acceso.\n",
"\n",
"El sensor reporta los siguientes registros:\n",
"\n",
"| Hora | Movimiento detectado |\n",
"|------|---------------------|\n",
"| 14:00 | Sí |\n",
"| 23:30 | Sí |\n",
"| 01:15 | Sí |\n",
"| 09:00 | No |\n",
"| 03:45 | Sí |\n",
"\n",
"Completa el código para clasificar cada registro como **NORMAL** o **INTRUSIÓN POSIBLE**.\n",
"\n",
"> **Pista:** considera que la hora se representa como número entero (23, 1, 3…). El horario no autorizado va de 22 a 23 y de 0 a 5 (inclusive)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "QNDQYs-Tki_6"
},
"outputs": [],
"source": [
"registros_sensor = [\n",
" (14, True),\n",
" (23, True),\n",
" ( 1, True),\n",
" ( 9, False),\n",
" ( 3, True),\n",
"]\n",
"\n",
"def es_horario_restringido(hora):\n",
" \"\"\"Devuelve True si la hora está entre las 22:00 y las 05:59.\"\"\"\n",
" return ___ # completa la condición\n",
"\n",
"print(f\"{'Hora':>6} {'Movimiento':>12} {'Estado'}\")\n",
"print('-' * 40)\n",
"\n",
"for hora, movimiento in registros_sensor:\n",
" if movimiento and es_horario_restringido(___):\n",
" estado = '🔴 INTRUSIÓN POSIBLE'\n",
" else:\n",
" estado = '🟢 NORMAL'\n",
" print(f\" {hora:02d}:xx {'Sí' if movimiento else 'No':>10} {estado}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "bcpjCAOYki_6"
},
"source": [
"---\n",
"## Sección 2 — Entropía de Shannon\n",
"\n",
"
\n",
"📘 Recordatorio teórico
\n",
"La entropía de Shannon cuantifica la incertidumbre de una fuente de información:\n",
"\n",
"$$H(X) = -\\sum_{i} p(x_i) \\cdot \\log_2\\, p(x_i) \\quad \\text{(en bits)}$$\n",
"\n",
"Donde $p(x_i)$ es la probabilidad del símbolo $x_i$. Propiedades esenciales:\n",
"
\n",
"
H = 0 cuando el resultado es completamente predecible (un símbolo ocurre siempre).
\n",
"
H es máxima cuando todos los símbolos son equiprobables: $H_{\\max} = \\log_2(N)$ bits.
\n",
"
La entropía no depende del contenido del mensaje, solo de la distribución de probabilidades.
\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SHb9_bEKki_7"
},
"source": [
"### 💻 Ejemplo resuelto 2.1 — Entropía de un escáner de puertos\n",
"\n",
"Un escáner analiza qué puertos son consultados en un firewall. Queremos saber cuánta \"sorpresa\" o variedad hay en los accesos — una distribución muy concentrada en pocos puertos tiene baja entropía."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "xY2gFLQAki_7"
},
"outputs": [],
"source": [
"# Definir función de entropía\n",
"\n",
"def entropia(probabilidades):\n",
" \"\"\"Entropía de Shannon en bits para una lista de probabilidades.\"\"\"\n",
" return -sum(p * math.log2(p) for p in probabilidades if p > 0)\n",
"\n"
]
},
{
"cell_type": "code",
"source": [
"# Distribución de accesos por puerto (últimas 6 horas)\n",
"puertos = {\n",
" 'HTTPS (443)': 0.72, # 360 accesos\n",
" 'HTTP (80)': 0.15, # 75 accesos\n",
" 'SSH (22)': 0.07,\n",
" 'DNS (53)': 0.04,\n",
" 'FTP (21)': 0.02,\n",
"}\n",
"puertos\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "zVD27ykwnXdj",
"outputId": "e2051734-31c4-4898-df6e-89e6fc262381"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'HTTPS (443)': 0.72,\n",
" 'HTTP (80)': 0.15,\n",
" 'SSH (22)': 0.07,\n",
" 'DNS (53)': 0.04,\n",
" 'FTP (21)': 0.02}"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"probs = list(puertos.values())\n",
"probs"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "9OplBulWnj8H",
"outputId": "820dd26f-8092-4dea-c77e-4cb7c6e339b8"
},
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[0.72, 0.15, 0.07, 0.04, 0.02]"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"source": [
"H = entropia(probs)\n",
"print('H =', H, 'bits')\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "PKQyQiA7oH55",
"outputId": "d0c4dbbc-809b-4894-b5f7-82fd1765b2da"
},
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"H = 1.3189617548509498 bits\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"H_max = math.log2(len(puertos)) # si todos los puertos fueran equiprobables\n",
"print('H_max =', H_max, 'bits')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ZS8hD0EqoJgQ",
"outputId": "4cfcb6eb-8bae-44ed-8ad8-2ab57060be01"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"H_max = 2.321928094887362 bits\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Informe resumen\n",
"\n",
"print('Distribución de accesos por puerto:','\\n')\n",
"for puerto, p in puertos.items():\n",
" barra = '█' * int(p * 40)\n",
" print(f' {puerto:<14} {p:.2f} {barra}')\n",
"\n",
"print(f'\\nEntropía observada : {H:.4f} bits')\n",
"print(f'Entropía máxima : {H_max:.4f} bits (distribución uniforme entre 5 puertos)')\n",
"print(f'Porcentaje del máximo: {H/H_max*100:.1f}%')\n",
"print()\n",
"print('→ La entropía es relativamente baja porque HTTPS domina el tráfico.')\n",
"print(' Eso es lo esperado en operación normal: un patrón predecible.')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1tKmZx57nXsA",
"outputId": "d452dece-0f6b-4578-ea4c-356ec27623c8"
},
"execution_count": 16,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Distribución de accesos por puerto: \n",
"\n",
" HTTPS (443) 0.72 ████████████████████████████\n",
" HTTP (80) 0.15 ██████\n",
" SSH (22) 0.07 ██\n",
" DNS (53) 0.04 █\n",
" FTP (21) 0.02 \n",
"\n",
"Entropía observada : 1.3190 bits\n",
"Entropía máxima : 2.3219 bits (distribución uniforme entre 5 puertos)\n",
"Porcentaje del máximo: 56.8%\n",
"\n",
"→ La entropía es relativamente baja porque HTTPS domina el tráfico.\n",
" Eso es lo esperado en operación normal: un patrón predecible.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H6ESFOjXki_7"
},
"source": [
"### 💻 Ejemplo resuelto 2.2 — ¿Qué pasa con la entropía cuando hay un escaneo masivo de puertos?\n",
"\n",
"Durante un ataque de reconocimiento, un adversario escanea todos los puertos aleatoriamente. Comparamos la entropía antes y durante el ataque."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Ou0LM_k0ki_8",
"outputId": "645a00c3-6a11-4a96-accc-b6243213eae0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"H_normal = 1.3189617548509498\n",
"H_ataque = 2.3212064465044864\n"
]
}
],
"source": [
"# Durante un escaneo masivo: el atacante accede a muchos puertos por igual\n",
"puertos_bajo_ataque = {\n",
" 'HTTPS (443)': 0.21, # 105 accesos\n",
" 'HTTP (80)': 0.20, # 100 accesos\n",
" 'SSH (22)': 0.20, # 100 accesos\n",
" 'DNS (53)': 0.20, # 100 accesos\n",
" 'FTP (21)': 0.19, # 095 accesos\n",
"}\n",
"\n",
"H_normal = entropia(list(puertos.values())) # del ejemplo anterior\n",
"H_ataque = entropia(list(puertos_bajo_ataque.values()))\n",
"print('H_normal =', H_normal)\n",
"print('H_ataque =', H_ataque)\n"
]
},
{
"cell_type": "code",
"source": [
"# Gráfica comparativa\n",
"fig, axes = plt.subplots(1, 2, figsize=(11, 4))\n",
"escenarios = [\n",
" ('Operación normal', puertos, H_normal, '#2E75B6'),\n",
" ('Escaneo de puertos', puertos_bajo_ataque, H_ataque, '#C03030'),\n",
"]\n",
"\n",
"for ax, (titulo, datos, H, color) in zip(axes, escenarios):\n",
" nombres = [p.split('(')[0].strip() for p in datos.keys()]\n",
" ax.bar(nombres, datos.values(), color=color, alpha=0.80, edgecolor='white', width=0.6)\n",
" ax.set_title(f'{titulo}\\nH = {H:.3f} bits', fontsize=12, fontweight='bold')\n",
" ax.set_ylabel('Proporción de accesos')\n",
" ax.set_ylim(0, 0.85)\n",
" ax.tick_params(axis='x', rotation=15)\n",
"\n",
"plt.suptitle('Distribución de accesos por puerto — Firewall SecureData Corp', fontsize=12)\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"print(f'Incremento de entropía: {H_ataque - H_normal:+.4f} bits')\n",
"print('→ Un salto brusco de entropía en la distribución de puertos es una señal de alerta.')\n",
"print(' El patrón se volvió casi uniforme — característica típica de un port scan.')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 464
},
"id": "WWwYw5iFp1aI",
"outputId": "95e7bd92-a4d3-4da3-ef5c-341ea8563a4b"
},
"execution_count": 18,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Incremento de entropía: +1.0022 bits\n",
"→ Un salto brusco de entropía en la distribución de puertos es una señal de alerta.\n",
" El patrón se volvió casi uniforme — característica típica de un port scan.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UbYLQwZjki_8"
},
"source": [
"### ✏️ Ejercicio 2.1 — Entropía de patrones de acceso a archivos confidenciales\n",
"\n",
"El DLP (Data Loss Prevention) de SecureData Corp registra cuántas veces se accede a cada tipo de archivo confidencial durante una semana. Se tienen dos semanas:\n",
"\n",
"| Tipo de archivo | Semana A | Semana B |\n",
"|-----------------|----------|----------|\n",
"| Contratos PDF | 420 | 110 |\n",
"| Planos de red | 180 | 108 |\n",
"| Base de datos clientes | 90 | 112 |\n",
"| Claves privadas | 30 | 115 |\n",
"| Backups cifrados | 80 | 105 |\n",
"\n",
"1. Calcula la entropía de cada semana.\n",
"2. ¿Cuál semana es más sospechosa desde el punto de vista de seguridad? Justifica.\n",
"3. ¿Qué tipo de amenaza podría explicar el patrón de la semana sospechosa?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "AHl2cgYHki_8"
},
"outputs": [],
"source": [
"tipos = ['Contratos PDF', 'Planos de red', 'BD clientes', 'Claves privadas', 'Backups']\n",
"\n",
"semana_A = [420, 180, 90, 30, 80]\n",
"semana_B = [110, 108, 112, 115, 105]\n",
"\n",
"# Paso 1: convertir a probabilidades\n",
"total_A = ___\n",
"total_B = ___\n",
"prob_A = [n / total_A for n in semana_A]\n",
"prob_B = [n / ___ for n in semana_B]\n",
"\n",
"# Paso 2: calcular entropías usando la función definida antes\n",
"H_A = ___\n",
"H_B = ___\n",
"\n",
"print(f'Entropía Semana A: {H_A:.4f} bits')\n",
"print(f'Entropía Semana B: {H_B:.4f} bits')\n",
"print(f'Entropía máxima posible: {math.log2(len(tipos)):.4f} bits')\n",
"\n",
"# Paso 3: responde aquí\n",
"print('\\n¿Cuál semana es más sospechosa?')\n",
"print('→ ___')\n",
"print('\\n¿Qué amenaza podría explicarlo?')\n",
"print('→ ___')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "P8UJU8AXki_8"
},
"source": [
"---\n",
"## Sección 3 — Entropía de claves y contraseñas\n",
"\n",
"
\n",
"📘 Recordatorio teórico
\n",
"Para evaluar la resistencia de una contraseña, se usa la fórmula:\n",
"$$H = L \\cdot \\log_2(N)$$\n",
"Donde $L$ es la longitud y $N$ el tamaño del alfabeto (cuántos caracteres distintos se pueden usar).
\n",
"Importante: esta fórmula calcula una cota superior — asume que cada carácter se elige completamente al azar con probabilidad uniforme. En la práctica, los usuarios no eligen al azar (\"12345\", \"password\"), por lo que la entropía real es siempre menor a este valor teórico.\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l9fs8mafki_8"
},
"source": [
"### 💻 Ejemplo resuelto 3.1 — Comparador de políticas de contraseñas\n",
"\n",
"SecureData Corp evalúa tres propuestas de política de contraseñas para sus empleados. Calculamos la cota superior de entropía de cada una."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "QWxX408Wki_8",
"outputId": "598cd2ec-bd31-4b04-eac6-85114ef679fa"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Política L N H (bits) Evaluación\n",
"-------------------------------------------------------------------------------------\n",
"Política A — Solo números, 6 dígitos 6 10 19.93 ❌ Insegura\n",
"Política B — Letras minúsculas, 8 caracteres 8 26 37.60 ❌ Insegura\n",
"Política C — Alfanumérico + símbolos, 12 caracteres 12 94 78.66 ✅ Robusta\n",
"\n",
"⚠ Estos valores son COTAS SUPERIORES bajo equiprobabilidad.\n",
" Una contraseña como \"aaaaaaaaaaaa\" tiene L=12, N=26, pero su entropía REAL es ~0.\n"
]
}
],
"source": [
"def cota_entropia(longitud, alfabeto):\n",
" \"\"\"Cota superior H = L * log2(N). Supone caracteres equiprobables e independientes.\"\"\"\n",
" return longitud * math.log2(alfabeto)\n",
"\n",
"politicas = [\n",
" {'nombre': 'Política A — Solo números, 6 dígitos',\n",
" 'L': 6, 'N': 10, 'ejemplo': '483920'},\n",
" {'nombre': 'Política B — Letras minúsculas, 8 caracteres',\n",
" 'L': 8, 'N': 26, 'ejemplo': 'qrtxmdpv'},\n",
" {'nombre': 'Política C — Alfanumérico + símbolos, 12 caracteres',\n",
" 'L': 12, 'N': 94, 'ejemplo': 'G!7kL#2xQp@z'},\n",
"]\n",
"\n",
"print(f\"{'Política':<48} {'L':>3} {'N':>4} {'H (bits)':>10} {'Evaluación'}\")\n",
"print('-' * 85)\n",
"\n",
"for p in politicas:\n",
" H = cota_entropia(p['L'], p['N'])\n",
" if H < 40:\n",
" evaluacion = '❌ Insegura'\n",
" elif H < 70:\n",
" evaluacion = '⚠️ Aceptable'\n",
" else:\n",
" evaluacion = '✅ Robusta'\n",
" print(f\"{p['nombre']:<48} {p['L']:>3} {p['N']:>4} {H:>10.2f} {evaluacion}\")\n",
"\n",
"print()\n",
"print('⚠ Estos valores son COTAS SUPERIORES bajo equiprobabilidad.')\n",
"print(' Una contraseña como \"aaaaaaaaaaaa\" tiene L=12, N=26, pero su entropía REAL es ~0.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ui2QbkkLki_9"
},
"source": [
"### 💻 Ejemplo resuelto 3.2 — Impacto de la longitud vs. el alfabeto\n",
"\n",
"¿Qué conviene más: agregar caracteres al alfabeto o aumentar la longitud? Lo visualizamos."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 539
},
"id": "cOwsMhxQki_9",
"outputId": "f45f8f33-bc42-4287-8df5-5117027a7d84"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Observación: aumentar la longitud tiene mayor impacto que ampliar el alfabeto\n",
"una vez que el alfabeto ya es grande. Con N=94 y L=12 se supera el umbral de 70 bits.\n"
]
}
],
"source": [
"longitudes = range(4, 21) # de 4 a 20 caracteres\n",
"\n",
"alfabetos = {\n",
" 'Solo números (N=10)': 10,\n",
" 'Minúsculas (N=26)': 26,\n",
" 'Mayúsc. + minúsc. (N=52)': 52,\n",
" 'Alfanumérico + símbolos (N=94)': 94,\n",
"}\n",
"\n",
"colores = ['#C03030', '#E09020', '#2E75B6', '#1D9E75']\n",
"\n",
"plt.figure(figsize=(10, 5))\n",
"for (nombre, N), color in zip(alfabetos.items(), colores):\n",
" H_vals = [cota_entropia(L, N) for L in longitudes]\n",
" plt.plot(list(longitudes), H_vals, label=nombre, color=color, linewidth=2)\n",
"\n",
"plt.axhline(y=70, color='gray', linestyle='--', linewidth=0.9, label='Umbral recomendado (70 bits)')\n",
"plt.xlabel('Longitud de la contraseña (caracteres)', fontsize=12)\n",
"plt.ylabel('Entropía máxima H (bits)', fontsize=12)\n",
"plt.title('Cota superior de entropía según longitud y alfabeto', fontsize=13, fontweight='bold')\n",
"plt.legend(fontsize=10, loc='upper left')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"\n",
"print('Observación: aumentar la longitud tiene mayor impacto que ampliar el alfabeto')\n",
"print('una vez que el alfabeto ya es grande. Con N=94 y L=12 se supera el umbral de 70 bits.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NKQaw8oGki_9"
},
"source": [
"### ✏️ Ejercicio 3.1 — Auditoría de política de contraseñas\n",
"\n",
"El área de RR.HH. de SecureData Corp propone cuatro políticas nuevas. Tu tarea es evaluarlas:\n",
"\n",
"| Política | Longitud | Alfabeto |\n",
"|----------|----------|----------|\n",
"| P1 | 4 dígitos (PIN) | 10 |\n",
"| P2 | 8 letras minúsculas | 26 |\n",
"| P3 | 10 alfanuméricos (sin símbolos) | 62 |\n",
"| P4 | 16 caracteres con símbolos | 94 |\n",
"\n",
"Criterio de tu empresa: entropía ≥ 60 bits para usuarios internos, ≥ 80 bits para administradores.\n",
"\n",
"1. Calcula la entropía de cada política.\n",
"2. Clasifica cada una como APROBADA o RECHAZADA para usuarios internos y para administradores.\n",
"3. ¿Cuál política recomendarías para los administradores del firewall? ¿Por qué?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zO8AL1MOki_9"
},
"outputs": [],
"source": [
"politicas_rrhh = [\n",
" {'nombre': 'P1 — PIN 4 dígitos', 'L': 4, 'N': 10},\n",
" {'nombre': 'P2 — 8 letras minúsculas', 'L': 8, 'N': 26},\n",
" {'nombre': 'P3 — 10 alfanuméricos', 'L': 10, 'N': 62},\n",
" {'nombre': 'P4 — 16 con símbolos', 'L': 16, 'N': 94},\n",
"]\n",
"\n",
"UMBRAL_USUARIO = ___ # bits\n",
"UMBRAL_ADMIN = ___ # bits\n",
"\n",
"print(f\"{'Política':<28} {'H (bits)':>10} {'Usuario interno':>17} {'Administrador':>15}\")\n",
"print('-' * 76)\n",
"\n",
"for p in politicas_rrhh:\n",
" H = ___ # usa cota_entropia()\n",
" aprobado_usuario = '✅ APROBADA' if H >= UMBRAL_USUARIO else '❌ RECHAZADA'\n",
" aprobado_admin = ___ if H >= ___ else '❌ RECHAZADA'\n",
" print(f\"{p['nombre']:<28} {H:>10.2f} {aprobado_usuario:>17} {aprobado_admin:>15}\")\n",
"\n",
"print('\\n¿Cuál política recomendarías para administradores del firewall?')\n",
"print('→ ___')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oC7WnL7xki_9"
},
"source": [
"---\n",
"## Sección 4 — Modelo de Shannon-Weaver: fuente, canal y ruido\n",
"\n",
"
\n",
"📘 Recordatorio teórico
\n",
"El modelo de Shannon-Weaver describe cualquier sistema de comunicación con cinco componentes:\n",
"\n",
"
Fuente: genera el mensaje (símbolos con sus probabilidades).
\n",
"
Transmisor: convierte el mensaje en señal para transmitirlo.
\n",
"
Canal: medio de transporte. Puede introducir ruido.
\n",
"
Receptor: reconstruye el mensaje original a partir de la señal.
\n",
"
Destino: entidad que usa la información recibida.
\n",
"\n",
"El ruido adversarial es el más relevante en seguridad: no es accidental, es el atacante modificando o interceptando el canal deliberadamente.\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TeIrihd6ki_-"
},
"source": [
"### 💻 Ejemplo resuelto 4.1 — Transmisión de un token de autenticación con ruido\n",
"\n",
"Un token OTP (One-Time Password) de 6 dígitos se transmite como cadena de bits. Simulamos qué ocurre cuando el canal introduce errores de distintas magnitudes."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4vKyMuy_ki_-",
"outputId": "902374db-0006-45ee-ef77-52188b046452"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Token original (fuente): \"849302\"\n",
"Longitud en bits: 48 bits\n",
"\n",
"Escenario p_error Token recibido ¿Coincide?\n",
"--------------------------------------------------------------------------------\n",
"Canal seguro (TLS activo) 0.00 '849302' ✅ Sí\n",
"Ruido físico leve 0.02 '849302' ✅ Sí\n",
"Ataque de degradación de señal 0.10 '*\\x84=3P2' ❌ No\n",
"Ataque MitM agresivo 0.30 'j\\x87<û@z' ❌ No\n",
"\n",
"→ Incluso un ruido del 2% puede corromper el token y denegar un acceso legítimo.\n",
" Pilar CIA afectado: INTEGRIDAD (y potencialmente DISPONIBILIDAD).\n"
]
}
],
"source": [
"random.seed(7)\n",
"\n",
"def texto_a_bits(texto):\n",
" \"\"\"Convierte una cadena de texto a lista de bits (ASCII).\"\"\"\n",
" bits = []\n",
" for char in texto:\n",
" byte = format(ord(char), '08b') # 8 bits por carácter\n",
" bits.extend([int(b) for b in byte])\n",
" return bits\n",
"\n",
"def bits_a_texto(bits):\n",
" \"\"\"Convierte lista de bits de vuelta a texto (ignorando errores de decodificación).\"\"\"\n",
" chars = []\n",
" for i in range(0, len(bits), 8):\n",
" byte = bits[i:i+8]\n",
" if len(byte) == 8:\n",
" val = int(''.join(str(b) for b in byte), 2)\n",
" try:\n",
" chars.append(chr(val))\n",
" except:\n",
" chars.append('?')\n",
" return ''.join(chars)\n",
"\n",
"def canal_ruidoso(bits, p_error):\n",
" \"\"\"Introduce errores aleatorios en una lista de bits con probabilidad p_error.\"\"\"\n",
" return [1 - b if random.random() < p_error else b for b in bits]\n",
"\n",
"# Token OTP generado por la fuente\n",
"token_original = '849302'\n",
"bits_originales = texto_a_bits(token_original)\n",
"\n",
"print(f'Token original (fuente): \"{token_original}\"')\n",
"print(f'Longitud en bits: {len(bits_originales)} bits\\n')\n",
"\n",
"escenarios = [\n",
" ('Canal seguro (TLS activo)', 0.00),\n",
" ('Ruido físico leve', 0.02),\n",
" ('Ataque de degradación de señal', 0.10),\n",
" ('Ataque MitM agresivo', 0.30),\n",
"]\n",
"\n",
"print(f\"{'Escenario':<35} {'p_error':>8} {'Token recibido':>16} {'¿Coincide?'}\")\n",
"print('-' * 80)\n",
"for nombre, p in escenarios:\n",
" random.seed(7) # misma semilla para comparar\n",
" bits_recibidos = canal_ruidoso(bits_originales, p)\n",
" token_recibido = bits_a_texto(bits_recibidos)\n",
" coincide = '✅ Sí' if token_recibido == token_original else '❌ No'\n",
" print(f'{nombre:<35} {p:>8.2f} {repr(token_recibido):>16} {coincide}')\n",
"\n",
"print()\n",
"print('→ Incluso un ruido del 2% puede corromper el token y denegar un acceso legítimo.')\n",
"print(' Pilar CIA afectado: INTEGRIDAD (y potencialmente DISPONIBILIDAD).')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GoJg6u0lki_-"
},
"source": [
"### 💻 Ejemplo resuelto 4.2 — Mapeo del modelo Shannon-Weaver a un ataque real\n",
"\n",
"Representamos cómo un ataque de **DNS Spoofing** se puede analizar con el modelo Shannon-Weaver."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DWJ7EQAwki_-",
"outputId": "18f0cfd3-00ae-4083-bb44-fa94d35af797"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"=== Análisis Shannon-Weaver: Ataque de DNS Spoofing ===\n",
"\n",
"Componente Descripción del ataque Pilar CIA\n",
"-------------------------------------------------------------------------------------\n",
"Fuente Navegador del empleado solicita: www.securedata.corp —\n",
"Transmisor Stack TCP/IP del equipo codifica la consulta DNS Integridad\n",
"Canal Red interna corporativa (potencialmente interceptada) Confidencialidad + Integridad\n",
"Ruido ⚠️ Atacante inyecta respuesta DNS falsa: IP del atacante Integridad (datos alterados)\n",
"Receptor Resolver DNS del equipo acepta la respuesta envenenada Integridad\n",
"Destino Empleado accede al sitio FALSO creyendo que es legítimo Confidencialidad (credenciales expuestas)\n",
"\n",
"Conclusión: el \"ruido\" en este caso no es físico — es el atacante.\n",
"Shannon-Weaver permite identificar exactamente qué componente fue vulnerado.\n"
]
}
],
"source": [
"# Descripción del sistema bajo un ataque de DNS Spoofing\n",
"sistema = {\n",
" 'Fuente': 'Navegador del empleado solicita: www.securedata.corp',\n",
" 'Transmisor': 'Stack TCP/IP del equipo codifica la consulta DNS',\n",
" 'Canal': 'Red interna corporativa (potencialmente interceptada)',\n",
" 'Ruido': '⚠️ Atacante inyecta respuesta DNS falsa: IP del atacante',\n",
" 'Receptor': 'Resolver DNS del equipo acepta la respuesta envenenada',\n",
" 'Destino': 'Empleado accede al sitio FALSO creyendo que es legítimo',\n",
"}\n",
"\n",
"cia_afectado = {\n",
" 'Fuente': '—',\n",
" 'Transmisor': 'Integridad',\n",
" 'Canal': 'Confidencialidad + Integridad',\n",
" 'Ruido': 'Integridad (datos alterados)',\n",
" 'Receptor': 'Integridad',\n",
" 'Destino': 'Confidencialidad (credenciales expuestas)',\n",
"}\n",
"\n",
"print('=== Análisis Shannon-Weaver: Ataque de DNS Spoofing ===')\n",
"print()\n",
"print(f\"{'Componente':<13} {'Descripción del ataque':<72} {'Pilar CIA'}\")\n",
"print('-' * 85)\n",
"for comp in sistema:\n",
" print(f\"{comp:<13} {sistema[comp]:<72} {cia_afectado[comp]}\")\n",
"\n",
"print()\n",
"print('Conclusión: el \"ruido\" en este caso no es físico — es el atacante.')\n",
"print('Shannon-Weaver permite identificar exactamente qué componente fue vulnerado.')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Eb0xHRVhki_-"
},
"source": [
"### ✏️ Ejercicio 4.1 — Análisis de un ataque de phishing con Shannon-Weaver\n",
"\n",
"Un empleado de SecureData Corp recibe un correo electrónico que aparenta venir del área de TI, solicitando sus credenciales de VPN. El empleado responde con su usuario y contraseña.\n",
"\n",
"Tu tarea es mapear este escenario en el modelo de Shannon-Weaver e identificar qué componente introduce el \"ruido\" y qué pilar CIA queda comprometido.\n",
"\n",
"> **Pista:** el atacante actúa como si fuera la fuente legítima, pero en realidad está inyectando ruido en el sistema de comunicación."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OsUv1vl4ki_-"
},
"outputs": [],
"source": [
"# Completa cada valor con una descripción del ataque de phishing\n",
"ataque_phishing = {\n",
" 'Fuente': '___', # ¿Quién aparenta ser la fuente?\n",
" 'Transmisor': '___', # ¿Cómo se codifica y envía el mensaje falso?\n",
" 'Canal': '___', # ¿Por qué medio llega el mensaje?\n",
" 'Ruido': '___', # ¿Qué perturba la comunicación? ¿Dónde actúa el atacante?\n",
" 'Receptor': '___', # ¿Quién recibe el mensaje?\n",
" 'Destino': '___', # ¿Quién usa la información recibida? ¿Con qué consecuencia?\n",
"}\n",
"\n",
"cia_comprometido = {\n",
" 'Fuente': '___',\n",
" 'Transmisor': '___',\n",
" 'Canal': '___',\n",
" 'Ruido': '___',\n",
" 'Receptor': '___',\n",
" 'Destino': '___',\n",
"}\n",
"\n",
"print('=== Análisis Shannon-Weaver: Ataque de Phishing ===')\n",
"print()\n",
"print(f\"{'Componente':<13} {'Descripción':<50} {'Pilar CIA'}\")\n",
"print('-' * 78)\n",
"for comp in ataque_phishing:\n",
" print(f\"{comp:<13} {ataque_phishing[comp]:<50} {cia_comprometido[comp]}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PNsC3syZki_-"
},
"source": [
"---\n",
"## Sección 5 — Modelos de comunicación: ¿cuándo usar cuál?\n",
"\n",
"
\n",
"📘 Recordatorio teórico
\n",
"Existen varios modelos de comunicación. La diferencia clave entre ellos:\n",
"
\n",
"
Lineales (Shannon-Weaver, Lasswell, Berlo): flujo en una sola dirección. Sin retroalimentación.
\n",
"
Interactivos (Schramm): emisor y receptor intercambian roles. Hay retroalimentación.
\n",
"
Transaccionales (Barnlund): comunicación simultánea e influencia mutua constante.
\n",
"
Socioculturales: la comunicación construye significado según el contexto cultural.
\n",
"
\n",
"Para análisis técnico de canales digitales, Shannon-Weaver es el modelo adecuado porque permite cuantificar la información y modelar el ruido matemáticamente.\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5sWfvdTVki__"
},
"source": [
"### 💻 Ejemplo resuelto 5.1 — Clasificador de escenarios por modelo de comunicación\n",
"\n",
"Dado un escenario de comunicación, el programa sugiere qué modelo es más adecuado para analizarlo."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "n1f90uZcki__",
"outputId": "163f3997-cdff-46a9-c282-d83e0dceeef5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Escenario: Transmisión cifrada de logs entre servidor y SIEM\n",
" Modelo recomendado: Shannon-Weaver\n",
" Razón: Canal digital con posibilidad de ruido y métricas cuantificables.\n",
"\n",
"Escenario: Videoconferencia de crisis entre CISO y directivos\n",
" Modelo recomendado: Barnlund (Transaccional)\n",
" Razón: Ambas partes emiten y reciben simultáneamente con influencia mutua.\n",
"\n",
"Escenario: Correo de alerta enviado a todos los empleados\n",
" Modelo recomendado: Lasswell / Berlo (Lineal)\n",
" Razón: Flujo unidireccional: quién dice qué, por qué canal, con qué efecto.\n",
"\n",
"Escenario: Capacitación de seguridad para equipo multinacional\n",
" Modelo recomendado: Schramm (Interactivo)\n",
" Razón: Existe retroalimentación: emisor y receptor intercambian roles.\n",
"\n"
]
}
],
"source": [
"# Reglas de clasificación basadas en características del escenario\n",
"def recomendar_modelo(hay_retroalimentacion, es_tecnico_digital,\n",
" comunicacion_simultanea, importa_contexto_cultural):\n",
" \"\"\"\n",
" Recomienda un modelo de comunicación según las características del escenario.\n",
" Retorna el nombre del modelo y la justificación.\n",
" \"\"\"\n",
" if es_tecnico_digital:\n",
" return ('Shannon-Weaver',\n",
" 'Canal digital con posibilidad de ruido y métricas cuantificables.')\n",
" elif comunicacion_simultanea:\n",
" return ('Barnlund (Transaccional)',\n",
" 'Ambas partes emiten y reciben simultáneamente con influencia mutua.')\n",
" elif hay_retroalimentacion:\n",
" return ('Schramm (Interactivo)',\n",
" 'Existe retroalimentación: emisor y receptor intercambian roles.')\n",
" elif importa_contexto_cultural:\n",
" return ('Sociocultural',\n",
" 'El significado depende del contexto cultural y social.')\n",
" else:\n",
" return ('Lasswell / Berlo (Lineal)',\n",
" 'Flujo unidireccional: quién dice qué, por qué canal, con qué efecto.')\n",
"\n",
"escenarios = [\n",
" {\n",
" 'desc': 'Transmisión cifrada de logs entre servidor y SIEM',\n",
" 'retro': False, 'tecnico': True, 'simult': False, 'cultural': False\n",
" },\n",
" {\n",
" 'desc': 'Videoconferencia de crisis entre CISO y directivos',\n",
" 'retro': True, 'tecnico': False, 'simult': True, 'cultural': False\n",
" },\n",
" {\n",
" 'desc': 'Correo de alerta enviado a todos los empleados',\n",
" 'retro': False, 'tecnico': False, 'simult': False, 'cultural': False\n",
" },\n",
" {\n",
" 'desc': 'Capacitación de seguridad para equipo multinacional',\n",
" 'retro': True, 'tecnico': False, 'simult': False, 'cultural': True\n",
" },\n",
"]\n",
"\n",
"for e in escenarios:\n",
" modelo, justificacion = recomendar_modelo(\n",
" e['retro'], e['tecnico'], e['simult'], e['cultural']\n",
" )\n",
" print(f'Escenario: {e[\"desc\"]}')\n",
" print(f' Modelo recomendado: {modelo}')\n",
" print(f' Razón: {justificacion}')\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6_oktIzjki__"
},
"source": [
"### ✏️ Ejercicio 5.1 — ¿Qué modelo aplica en cada situación?\n",
"\n",
"SecureData Corp enfrenta las siguientes situaciones. Para cada una, determina el modelo de comunicación más apropiado y explica brevemente por qué.\n",
"\n",
"Completa el diccionario con el nombre del modelo y la justificación."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DWP0jnyCki__"
},
"outputs": [],
"source": [
"situaciones = [\n",
" {\n",
" 'id': 1,\n",
" 'desc': 'Un IDS envía alertas automáticas al firewall sin esperar confirmación.',\n",
" 'modelo': '___',\n",
" 'justificacion': '___'\n",
" },\n",
" {\n",
" 'id': 2,\n",
" 'desc': 'Un analista de seguridad y un desarrollador debaten en tiempo real '\n",
" 'cómo corregir una vulnerabilidad crítica.',\n",
" 'modelo': '___',\n",
" 'justificacion': '___'\n",
" },\n",
" {\n",
" 'id': 3,\n",
" 'desc': 'El CISO envía un memorando formal sobre la nueva política de contraseñas '\n",
" 'a todos los empleados. No se espera respuesta.',\n",
" 'modelo': '___',\n",
" 'justificacion': '___'\n",
" },\n",
" {\n",
" 'id': 4,\n",
" 'desc': 'Un paquete IP viaja cifrado desde la sede en Quito hasta la '\n",
" 'sucursal en Guayaquil por VPN.',\n",
" 'modelo': '___',\n",
" 'justificacion': '___'\n",
" },\n",
"]\n",
"\n",
"print('=== Clasificación de escenarios por modelo de comunicación ===')\n",
"print()\n",
"for s in situaciones:\n",
" print(f\"Situación {s['id']}: {s['desc']}\")\n",
" print(f\" Modelo: {s['modelo']}\")\n",
" print(f\" Justificación: {s['justificacion']}\")\n",
" print()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Tp58wgYjki__"
},
"source": [
"---\n",
"## Sección 6 — Caso integrador: SecureData Corp\n",
"\n",
"
\n",
"🔒 Ejercicio final — Conectado al Reto 1
\n",
"Este ejercicio integra todos los conceptos de la sesión. Resuélvelo con el nivel de detalle que aplicarás en tu Reto 1.\n",
"
\n",
"\n",
"### Situación\n",
"\n",
"El equipo de ciberseguridad de SecureData Corp detecta algo inusual en el sistema de control de acceso físico del Data Center:\n",
"\n",
"- **Semana pasada:** de 1000 accesos registrados, el 80% correspondió al turno diurno, el 15% al vespertino y el 5% al nocturno.\n",
"- **Esta semana:** de 1000 accesos, la distribución cambió a 36%, 32% y 32% respectivamente.\n",
"- Adicionalmente, se detectaron **40 bits corruptos** en los logs de 800 bits transmitidos desde el sensor al servidor central."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5fo9wSGLki__"
},
"source": [
"### ✏️ Ejercicio 6.1 — Análisis completo"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JwWGt245kjAA"
},
"outputs": [],
"source": [
"# ─── Parte A: Entropía de los patrones de acceso ──────────────────────────\n",
"\n",
"dist_semana_pasada = [___, ___, ___] # probabilidades de los 3 turnos\n",
"dist_esta_semana = [___, ___, ___]\n",
"\n",
"H_pasada = ___\n",
"H_actual = ___\n",
"H_max = math.log2(3) # 3 turnos equiprobables\n",
"\n",
"print('── Parte A: Entropía de patrones de acceso ──')\n",
"print(f'H semana pasada : {H_pasada:.4f} bits')\n",
"print(f'H esta semana : {H_actual:.4f} bits')\n",
"print(f'H máxima (3 turnos equiprobables): {H_max:.4f} bits')\n",
"print()\n",
"\n",
"# ─── Parte B: Tasa de error en el canal ──────────────────────────────────\n",
"\n",
"bits_transmitidos = 800\n",
"bits_corruptos = 40\n",
"tasa_error = ___ # calcula la proporción\n",
"\n",
"print('── Parte B: Integridad del canal ──')\n",
"print(f'Bits transmitidos : {bits_transmitidos}')\n",
"print(f'Bits corruptos : {bits_corruptos}')\n",
"print(f'Tasa de error : {tasa_error*100:.1f}%')\n",
"print()\n",
"\n",
"# ─── Parte C: Diagnóstico y recomendaciones ───────────────────────────────\n",
"\n",
"print('── Parte C: Diagnóstico y recomendaciones ──')\n",
"print('¿Qué indica el cambio de entropía en los accesos?')\n",
"print('→ ___')\n",
"print()\n",
"print('¿Qué componente del modelo Shannon-Weaver está afectado por los bits corruptos?')\n",
"print('→ ___')\n",
"print()\n",
"print('¿Qué pilar del modelo CIA está en riesgo en cada hallazgo?')\n",
"print('→ Cambio de entropía: ___')\n",
"print('→ Bits corruptos: ___')\n",
"print()\n",
"print('Acción inmediata recomendada:')\n",
"print('→ ___')"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2RTtaBRekjAA"
},
"source": [
"---\n",
"## Resumen de la sesión\n",
"\n",
"
\n",
"
Tres ideas que debes llevarte hoy
\n",
"\n",
"
Entropía: mide incertidumbre. Un patrón que se vuelve demasiado uniforme o demasiado predecible es una señal de alerta en ciberseguridad.
\n",
"
Shannon-Weaver: cada componente del modelo (fuente, canal, receptor…) puede ser un punto de ataque. Mapear un incidente a este modelo permite identificar exactamente dónde ocurrió la falla y qué pilar CIA comprometió.
\n",
"
Ruido adversarial: en seguridad, el ruido tiene intención. Un atacante que intercepta el canal no es un fenómeno físico — es una amenaza activa que se puede modelar, detectar y mitigar.
\n",
"\n",
"
PUCE Virtual · Teoría de la Información y Comunicación · Semana 1