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!")