Datasets:
File size: 8,900 Bytes
bc8cfb4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | import numpy as np
import pandas as pd
import random
import os
import json
np.random.seed(42)
random.seed(42)
COUNTRIES = {
"Kenya": {"region": "EAC", "mobile_money_maturity": 0.95, "fraud_rate": 0.042, "awareness": 0.65},
"Nigeria": {"region": "ECOWAS", "mobile_money_maturity": 0.78, "fraud_rate": 0.055, "awareness": 0.45},
"South Africa": {"region": "SADC", "mobile_money_maturity": 0.82, "fraud_rate": 0.038, "awareness": 0.72},
"Ghana": {"region": "ECOWAS", "mobile_money_maturity": 0.70, "fraud_rate": 0.048, "awareness": 0.55},
"Tanzania": {"region": "EAC", "mobile_money_maturity": 0.75, "fraud_rate": 0.040, "awareness": 0.50},
"Uganda": {"region": "EAC", "mobile_money_maturity": 0.68, "fraud_rate": 0.045, "awareness": 0.48},
"Rwanda": {"region": "EAC", "mobile_money_maturity": 0.72, "fraud_rate": 0.035, "awareness": 0.58},
"Ivory Coast": {"region": "ECOWAS", "mobile_money_maturity": 0.65, "fraud_rate": 0.050, "awareness": 0.42},
"Ethiopia": {"region": "COMESA", "mobile_money_maturity": 0.45, "fraud_rate": 0.038, "awareness": 0.35},
"Cameroon": {"region": "ECOWAS", "mobile_money_maturity": 0.52, "fraud_rate": 0.052, "awareness": 0.40},
"Senegal": {"region": "ECOWAS", "mobile_money_maturity": 0.58, "fraud_rate": 0.046, "awareness": 0.45},
"Mozambique": {"region": "SADC", "mobile_money_maturity": 0.40, "fraud_rate": 0.042, "awareness": 0.38},
"Democratic Republic of Congo": {"region": "CEN-SAD", "mobile_money_maturity": 0.35, "fraud_rate": 0.058, "awareness": 0.28},
"Zambia": {"region": "SADC", "mobile_money_maturity": 0.55, "fraud_rate": 0.044, "awareness": 0.50},
"Burkina Faso": {"region": "ECOWAS", "mobile_money_maturity": 0.42, "fraud_rate": 0.050, "awareness": 0.35},
}
ATTACK_VECTORS = ["sms_phishing", "ussd_phishing", "voice_phishing", "social_media", "email", "fake_app", "mimicked_website"]
FRAUD_STAGES = ["initial_contact", "credentialHarvest", "authentication_bypass", "transaction_execution", "money_laundering"]
BANKING_APPS = ["M-Pesa", "MoMo", "Flutterwave", "Paystack", "Stripe", "Zenith", "GTBank", "Standard Chartered", "Absa", "Ecobank"]
def get_country():
return np.random.choice(list(COUNTRIES.keys()))
def generate_phishing_record(record_id, year, scenario):
country = get_country()
country_data = COUNTRIES[country]
if scenario == "baseline":
attack_frequency = country_data["fraud_rate"] * np.random.uniform(0.8, 1.2)
success_rate = np.random.uniform(0.08, 0.18)
detection_rate = np.random.uniform(0.15, 0.30)
financial_impact = np.random.uniform(150, 800)
campaign_duration = np.random.uniform(5, 20)
sophistication = np.random.uniform(0.30, 0.55)
target_sophistication = np.random.uniform(0.40, 0.70)
elif scenario == "ai_amplification":
attack_frequency = country_data["fraud_rate"] * np.random.uniform(1.3, 1.8)
success_rate = np.random.uniform(0.15, 0.30)
detection_rate = np.random.uniform(0.10, 0.22)
financial_impact = np.random.uniform(300, 1500)
campaign_duration = np.random.uniform(8, 35)
sophistication = np.random.uniform(0.55, 0.85)
target_sophistication = np.random.uniform(0.55, 0.85)
else:
attack_frequency = country_data["fraud_rate"] * np.random.uniform(0.5, 0.8)
success_rate = np.random.uniform(0.04, 0.10)
detection_rate = np.random.uniform(0.35, 0.55)
financial_impact = np.random.uniform(80, 400)
campaign_duration = np.random.uniform(3, 12)
sophistication = np.random.uniform(0.25, 0.45)
target_sophistication = np.random.uniform(0.30, 0.50)
attack_vector = np.random.choice(ATTACK_VECTORS,
p=[0.32, 0.25, 0.12, 0.15, 0.08, 0.05, 0.03])
target_app = np.random.choice(BANKING_APPS)
num_targets = int(np.random.uniform(500, 50000) * country_data["mobile_money_maturity"])
num_successful = int(num_targets * success_rate)
total_loss = num_successful * financial_impact
avg_loss_per_victim = financial_impact * np.random.uniform(0.6, 1.0)
fraud_stage = np.random.choice(FRAUD_STAGES, p=[0.25, 0.30, 0.20, 0.15, 0.10])
if attack_vector in ["sms_phishing", "ussd_phishing"]:
message_theme = np.random.choice(["account_suspended", "winning_prize", "urgent_verification",
"bill_payment", "loan_approval", "security_alert"])
else:
message_theme = np.random.choice(["customer_support", "transaction_confirm",
"new_device_login", "profile_update", "bonus_offer"])
time_to_detect = campaign_duration * np.random.uniform(0.3, 0.9)
law_enforcement_response = np.random.choice(["investigating", "arrest_made", "no_action", "international_coop"],
p=[0.50, 0.15, 0.25, 0.10])
record = {
"record_id": f"PHISH-{country[:3].upper()}-{record_id:07d}",
"year": year,
"country": country,
"region": country_data["region"],
"mobile_maturity_index": round(country_data["mobile_money_maturity"], 3),
"campaign_id": f"CAMP-{np.random.randint(100000, 999999):06d}",
"attack_vector": attack_vector,
"target_platform": target_app,
"num_targets": num_targets,
"num_successful_compromises": num_successful,
"attack_success_rate_pct": round(success_rate * 100, 2),
"total_financial_loss_usd": round(total_loss, 2),
"average_loss_per_victim_usd": round(avg_loss_per_victim, 2),
"campaign_duration_days": round(campaign_duration, 1),
"fraud_stage": fraud_stage,
"message_theme": message_theme,
"sophistication_score": round(sophistication, 3),
"target_sophistication_needed": round(target_sophistication, 3),
"time_to_detect_days": round(time_to_detect, 1),
"detection_rate_pct": round(detection_rate * 100, 1),
"user_awareness_score": round(country_data["awareness"], 3),
"law_enforcement_response": law_enforcement_response,
"victim_age_group": np.random.choice(["18-25", "26-35", "36-45", "46-55", "55+"],
p=[0.25, 0.35, 0.22, 0.12, 0.06]),
"victim_education": np.random.choice(["primary", "secondary", "tertiary", "none"],
p=[0.15, 0.40, 0.38, 0.07]),
"reporting_rate_pct": round(np.random.uniform(5, 25), 1),
"repeat_victim_pct": round(np.random.uniform(8, 35), 1),
"cross_border_attack": int(np.random.random() < 0.25),
"scenario": scenario,
}
return record
def generate_dataset(output_dir, n_records=18000):
scenarios = [
("baseline", 6000, [2018, 2019, 2020, 2021]),
("ai_amplification", 6000, [2022, 2023, 2024]),
("enhanced_protection", 6000, [2025, 2026]),
]
all_records = []
record_id = 0
for scenario_name, n_scenario, years in scenarios:
print(f"Generating {n_scenario} records for {scenario_name}")
for _ in range(n_scenario):
year = np.random.choice(years)
record = generate_phishing_record(record_id, year, scenario_name)
all_records.append(record)
record_id += 1
if record_id % 10000 == 0:
print(f" Progress: {record_id} records")
df = pd.DataFrame(all_records)
data_dir = os.path.join(output_dir, "data")
os.makedirs(data_dir, exist_ok=True)
for scenario in scenarios:
scenario_name = scenario[0]
scenario_df = df[df["scenario"] == scenario_name]
filename = f"phishing_{scenario_name}.csv"
scenario_df.to_csv(os.path.join(data_dir, filename), index=False)
print(f"Saved {len(scenario_df)} records to {filename}")
df.to_csv(os.path.join(data_dir, "phishing_full.csv"), index=False)
print(f"Saved {len(df)} total records to phishing_full.csv")
stats = {
"total_records": len(df),
"countries": df["country"].nunique(),
"by_scenario": df["scenario"].value_counts().to_dict(),
"mean_attack_frequency": float(df["attack_success_rate_pct"].mean()),
"mean_total_loss": float(df["total_financial_loss_usd"].mean()),
}
with open(os.path.join(output_dir, "generation_stats.json"), "w") as f:
json.dump(stats, f, indent=2)
print(f"Saved statistics to generation_stats.json")
return df
if __name__ == "__main__":
output_dir = os.path.dirname(os.path.abspath(__file__))
print("Generating African Mobile Banking Phishing Dataset...")
df = generate_dataset(output_dir, n_records=18000)
print("Dataset generation complete!") |