Evolutionary Worm (Project KawanHive) Design Scenario and Defense Strategy

EN | KR

• Document Version: MK IV
• Date: June 01, 2025 (Revised: June 24, 2025)
• Author: AR0NICA
• Security Classification: Confidential (For Discussion Purposes Only)

Overview

This document aims to present a hypothetical design scenario for a next-generation cyber threat, the 'Evolutionary Worm,' equipped with artificial intelligence (AI) and self-mutation capabilities to neutralize existing defense systems, and to establish proactive defense strategies against it. The hypothetical worm named 'KawanHive' in this document is a threat model designed to achieve its objectives autonomously through judgment, learning, and evolution without human intervention. This necessitates a fundamental shift in the cybersecurity paradigm, and this document seeks to initiate a proactive discussion on this matter.

1. Threat Model: Evolutionary Worm 'KawanHive'

1.1. Design Objectives

'KawanHive's ultimate goal is to 'continuously exfiltrate information and seize control of critical infrastructure.' It infiltrates the networks of specific nations or corporations, remains dormant for extended periods undetected, learns internal systems, and continuously leaks valuable information externally. In its final stage, it aims to seize control of critical infrastructure (power, communication, transportation, etc.) control systems to cause physical damage.

1.1.1. Autonomous Goal Re-evaluation

Beyond merely achieving initial objectives, 'KawanHive' possesses the ability to autonomously re-evaluate and re-prioritize its goals based on changing environments and defense systems. This maximizes the unpredictability of the AI worm and deepens the level of threat.

Scenario Example:

• Initial Goal: 'Information Exfiltration'
• Situation Awareness: The defense system of the target network is deemed robust enough to make information access extremely difficult.
• Autonomous Re-evaluation: 'KawanHive's Cognitive & Decision Engine determines that the efficiency of information exfiltration is significantly low and changes its goal to 'System Destruction and Social Chaos.'
• Priority Change: According to the revised goal, SCADA system attacks are re-prioritized as the primary objective, and resources are concentrated on exploring related vulnerabilities and developing exploits. This can lead to widespread physical and social damage, such as power grid blackouts, communication network disruptions, and traffic system paralysis.

This autonomous goal re-evaluation capability means that 'KawanHive' is not bound by fixed attack patterns but can adapt to the threat environment in real-time and evolve to produce the most devastating outcomes.

1.2. Core Architecture

'KawanHive' operates like an organism composed of four core modules.

• ① Cognitive & Decision Engine - "The Brain":
  • Neural Network Structure: A hybrid AI system combining a lightweight transformer-based language model (approx. 500MB) with a reinforcement learning agent.
    • To avoid detection due to high system resource consumption by immediately using a large 500MB AI model during the initial infiltration phase, the following hierarchical structure is added:
    • Scout Worm:
      • Ultra-lightweight AI of tens of KB.
      • Responsible for initial infiltration.
      • Performs only environmental analysis, vulnerability identification, and stealth communication functions.
    • Commander Worm:
      • Executes when a 'Scout' secures a safe, high-value system (e.g., server, administrator PC).
      • Downloads and installs the full 500MB transformer AI model ("The Brain").
      • Leads the learning and decision-making of the entire swarm.
  • Environmental Analysis Function:
    • OS fingerprinting (registry analysis, system call pattern learning).
    • Network topology mapping (ARP table, routing table analysis).
    • User behavior pattern learning (keyboard/mouse input timing, process execution frequency).
    • Reverse engineering of antivirus and firewall signatures.
  • Decision-Making Algorithm: Optimal action path search using Monte Carlo Tree Search.
  • Learning Data Compression: Knowledge compression and dissemination using Federated Learning techniques.
• ② Self-Morphing Framework - "The Body":
  • Polymorphic Engine:
    • Code obfuscation: Instruction reordering, garbage code insertion, register reallocation.
    • Encryption transformation: AES-256 based runtime decryption, dynamic key generation from environmental values.
    • Packing/Unpacking: Automatic application of various packers like UPX, Themida.
  • Metamorphic Engine:
    • Code rewriting: Semantic equivalent code generation using LLVM IR.
    • Function splitting/merging: Splitting one function into multiple or merging multiple functions into one.
    • Control flow alteration: Converting conditional statements to loops, switch statements to if-else chains.
  • Environment-Adaptive Compilation:
    • Runtime code generation through JIT (Just-In-Time) compilation.
    • Native code generation optimized for the target system's CPU architecture.
• ③ Automated Exploit Module - "The Claws":
  • Vulnerability Scanning Engine:
    • Port scanning: Automated SYN scan, TCP Connect scan, UDP scan.
    • Service fingerprinting: Banner grabbing, protocol analysis for version identification.
    • Web application scanning: Automated SQL Injection, XSS, CSRF detection.
  • AI-Powered Fuzzing:
    • Test case evolution using genetic algorithms.
    • Maximizing code coverage with coverage-guided fuzzing.
    • Automated crash analysis and exploitability assessment.
  • Zero-Day Development Pipeline:
    • Vulnerability → PoC → Exploit automated generation chain.
    • Automated ROP/JOP gadget chain construction.
    • Automated shellcode generation and encoding.
  • Attack Vector Library:
    • Memory corruption vulnerabilities (Buffer Overflow, Use-After-Free).
    • Logic vulnerabilities (Race Condition, Time-of-Check-Time-of-Use).
    • Cryptographic vulnerabilities (Weak Random, Hash Collision).
• ④ Swarm Intelligence Communication Protocol - "The Hive-Mind":
  • Distributed Messaging System:
    • DHT (Distributed Hash Table) based P2P network.
    • Efficient routing using Kademlia protocol.
    • Bitcoin blockchain-based command delivery (Steganography).
  • Encrypted Communication:
    • End-to-end encryption based on Signal protocol.
    • Key rotation for Perfect Forward Secrecy.
    • Anonymity assurance through Onion Routing.
  • Knowledge Sharing Mechanism:
    • Immediate distribution of newly discovered vulnerabilities to the entire swarm.
    • Real-time updates of defense evasion techniques.
    • Sharing of learning data from failed attack attempts.
  • Distributed Decision-Making:
    • Byzantine Fault Tolerance consensus algorithm.
    • Collective action determination through majority rule.
    • Balance between local optimization and global objectives.

1.3. Detailed Attack Scenario

Phase 1: Initial Infiltration - 1~2 weeks

1.1 Target Reconnaissance

• Automated OSINT (Open Source Intelligence) collection
  • Crawling employee information from LinkedIn, GitHub, company websites.
  • Analyzing and inferring email address patterns.
  • Collecting information on technology stacks and software versions used.
• Social Engineering Vector Generation
  • Automated generation of personalized phishing email templates.
  • Creation of fake documents mimicking company branding.
  • Creation of lures utilizing seasonal events (tax season, year-end reports, etc.).

1.2 Initial Delivery

• Simultaneous execution of multiple delivery paths
  • Email attachments: Macros within PDF, Office documents.
  • Website watering holes: Injecting malicious code into frequently visited sites.
  • USB drop: Placing malicious USBs in parking lots, elevators, etc.
• Use of highly trusted certificates
  • Theft or forgery of code signing certificates.
  • Domain similarity attacks (typosquatting).

Phase 2: Dormancy & Learning - 2~8 weeks

2.1 Environmental Adaptation

• System Fingerprint Collection
  • Hardware information (CPU, GPU, RAM configuration).
  • List of installed software and versions.
  • Network configuration and firewall policy analysis.
• Learning Normal Behavior Patterns
  • User's daily/weekly computer usage patterns.
  • Establishing network traffic baselines.
  • Analyzing process execution frequency and timing.

2.2 Stealth Optimization

• Antivirus and EDR Evasion
  • Memory-resident attacks (Fileless Attack).
  • Process hollowing of system processes.
  • Persistence through DLL sideloading.
• Log Manipulation and Trace Removal
  • Selective deletion of Windows Event Logs.
  • Manipulation of registry timestamps.
  • Forgery of file system metadata.

Phase 3: Adaptive Propagation - 1~4 weeks

3.1 Internal Reconnaissance

• Active Directory Enumeration
  • Identifying domain controller locations.
  • Mapping user and group permissions.
  • Identifying service accounts and administrator accounts.
• Network Segmentation Analysis
  • Understanding VLAN configurations.
  • Reverse engineering firewall rules.
  • Exploring internetwork connection paths.

3.2 Lateral Movement

• Credential Harvesting
  • Extracting password hashes via LSASS memory dump.
  • Kerberos ticket theft (Pass-the-Ticket).
  • NTLM hash passing attacks (Pass-the-Hash).
• Privilege Escalation
  • Gaining SYSTEM privileges through kernel exploits.
  • Applying UAC bypass techniques.
  • Impersonation of service accounts.

Phase 4: Mission Execution & Control - Continuous

4.1 Data Collection and Exfiltration

• Identifying High-Value Data
  • File name pattern analysis (confidential, blueprints, financial, etc.).
  • Database schema analysis.
  • Email and document content analysis.
• Covert Exfiltration Channels
  • Continuous small data transfer via DNS tunneling.
  • Exploiting cloud storage services.
  • Applying steganography to normal HTTPS traffic.

4.2 Seizing Control of Critical Infrastructure

• SCADA/ICS System Infiltration
  • Modbus, DNP3 protocol analysis and manipulation.
  • HMI (Human Machine Interface) control.
  • PLC firmware modification.
• Physical Impact Scenarios
  • Blackout through power grid frequency manipulation.
  • Manipulation of chlorine dosage in water treatment facilities.
  • Paralysis of traffic signal systems.

Phase 5: Anti-Forensics & Persistence - Continuous

5.1 Advanced Persistence Techniques

• UEFI/BIOS rootkit installation.
• Hypervisor-level persistence.
• Hardware implants (USB, network card firmware).

5.2 Forensic Countermeasures

• Anti-forensic techniques
  • Timeline manipulation.
  • File carving prevention.
  • Memory dump analysis interference.

---

2. Defense Strategy: Multi-layered, Intelligent Threat Response

2.1. Detailed Implementation of Zero Trust Architecture

2.1.1 Network Microsegmentation

• Establishment of Software-Defined Perimeter (SDP).
• Real-time inspection of East-West traffic.
• Automatic application of dynamic firewall policies.

2.1.2 Continuous Authentication and Authorization

• Behavioral Biometrics.
• Risk-based Adaptive Authentication.
• Just-In-Time (JIT) Authorization.

2.2. Advanced AI-Based Threat Detection (AI vs AI)

2.2.1 Multi-AI Model Ensemble

• Time-series anomaly detection (LSTM, Transformer).
• Graph neural network-based network analysis.
• Natural language processing-based log analysis.

2.2.2 Federated Learning-Based Threat Information Sharing

• Privacy-preserving learning between organizations.
• Real-time model updates.
• Application of Differential Privacy.

2.3. Expansion of Active Deception Technology

2.3.1 Dynamic Honeypot Generation

• AI-based automatic generation of fake services.
• Decoy systems indistinguishable from real environments.
• Customized trap scenarios per attacker.

2.3.2 Honeytokens and Canary Traps

• Traceable watermarks within documents.
• Fake API keys and credentials.
• Immediate alert system upon access.

2.4. Enhanced Automated Threat Isolation & Recovery

2.4.1 Real-time Orchestration

• SOAR (Security Orchestration, Automation and Response) platform.
• Automated execution of incident response playbooks.
• Automation of recovery processes.

2.4.2 Immutable Infrastructure

• Container-based isolated environments.
• Automated rollback and redeployment.
• Air-gapped backup systems.

2.5. Evolution of Next-Gen Threat Intelligence Sharing

2.5.1 Blockchain-Based Threat Information Exchange

• Decentralized threat intelligence network.
• Incentive system for information providers.
• Threat intelligence quality assessment mechanism.

2.5.2 STIX/TAXII 2.0 Advancement

• Machine-readable threat information standard.
• Real-time automated ingestion.
• Context-aware threat matching.

2.6. Introduction of Quantum Cryptography and Post-Quantum Cryptography

2.6.1 Quantum Key Distribution (QKD)

• Quantum-secure communication between critical infrastructures.
• Quantum entanglement-based authentication.

2.6.2 Post-Quantum Cryptography Algorithms

• Lattice-based Cryptography.
• Hash-based Signatures.

2.7. Cyber Resilience & Human Defense

• Continuous Attack Simulation Training:
  • For all employees.
  • Regular mock drills on virtual attack scenarios similar to 'KawanHive'.
  • Systematization of response procedures.
• Internalization of Security Culture:
  • Emphasizing that 'Zero Trust' applies not only to technology but also to organizational culture.
  • Building a culture where all members are always aware of 'potential threats' and verify them.
• AI-Based User Anomaly Behavior Analysis:
  • Expanding existing BehaviorAnalyzer, if a specific employee's account shows unusual patterns (e.g., access during late-night hours, access to servers not normally accessed).
  • AI judges this as an anomaly regardless of technical permissions and temporarily blocks access.

---

3. Evolutionary Worm Simulator Code (C++)

⚠️ Important Warning: This code is a high-level simulation for exploring the concepts and logic of an evolutionary worm. It is a virtual model designed for safe execution and analysis and does not contain any harmful functionalities such as actual network attacks, file system access, or malicious behavior.

#include <iostream>
#include <vector>
#include <string>
#include <set>
#include <map>
#include <random>
#include <thread>
#include <chrono>
#include <algorithm>
#include <iomanip>
#include <memory>
#include <queue>

// --- Extended Environment Settings ---
enum class OSType { WINDOWS, LINUX, MACOS, EMBEDDED };
enum class SecurityLevel { BASIC, STANDARD, ADVANCED, MILITARY };
enum class NetworkSegment { DMZ, INTERNAL, CRITICAL, ISOLATED };

const std::vector<std::string> VULNERABILITIES = {
    "CVE-2025-101A", "CVE-2025-202B", "CVE-2025-303C", "CVE-2025-404D_ZERO_DAY",
    "CVE-2025-505E", "CVE-2025-606F_ICS", "CVE-2025-707G_KERNEL"
};

const std::vector<std::string> SECURITY_SOFTWARE = {
    "BasicFirewall", "StandardAV", "AdvancedEDR", "AI_ThreatDetector",
    "BehaviorAnalyzer", "QuantumEncryption", "HoneypotSystem"
};

// Behavior Pattern Struct
struct BehaviorPattern {
    std::vector<int> login_times;      // Login time patterns
    std::vector<std::string> processes; // Frequently executed processes
    int network_activity_level;        // Network activity level (1-10)
    
    BehaviorPattern() : network_activity_level(5) {
        // Default business hours (9-17)
        login_times = {9, 10, 13, 17};
        processes = {"browser.exe", "office.exe", "email.exe"};
    }
};

// Network Topology Information
struct NetworkTopology {
    std::string subnet;
    NetworkSegment segment;
    std::vector<std::string> connected_subnets;
    int security_level; // 1-10
};

class Host {
private:
    std::string ip_address;
    std::string hostname;
    OSType os_type;
    std::vector<std::string> vulnerabilities;
    std::vector<std::string> security_software;
    std::map<std::string, std::string> system_info; // CPU, RAM, etc.
    BehaviorPattern user_behavior;
    NetworkTopology network_info;
    bool is_infected;
    bool is_honeypot;
    int detection_sensitivity; // 1-10, higher means more sensitive detection

public:
    Host(const std::string& ip, const std::string& name, OSType os,
         const std::vector<std::string>& vulns, 
         const std::vector<std::string>& security,
         NetworkSegment segment = NetworkSegment::INTERNAL)
        : ip_address(ip), hostname(name), os_type(os), 
          vulnerabilities(vulns), security_software(security), 
          is_infected(false), is_honeypot(false), detection_sensitivity(5) {
        
        network_info.segment = segment;
        network_info.security_level = static_cast<int>(segment) + 3;
        
        // Initialize system information
        system_info["CPU"] = "Intel_i7";
        system_info["RAM"] = "16GB";
        system_info["OS_VERSION"] = getOSString();
    }

    // Getter methods
    const std::string& getIpAddress() const { return ip_address; }
    const std::string& getHostname() const { return hostname; }
    OSType getOSType() const { return os_type; }
    const std::vector<std::string>& getVulnerabilities() const { return vulnerabilities; }
    const std::vector<std::string>& getSecuritySoftware() const { return security_software; }
    const BehaviorPattern& getBehaviorPattern() const { return user_behavior; }
    bool getIsInfected() const { return is_infected; }
    bool getIsHoneypot() const { return is_honeypot; }
    int getDetectionSensitivity() const { return detection_sensitivity; }
    NetworkSegment getNetworkSegment() const { return network_info.segment; }
    
    // Setter methods
    void setInfected(bool infected) { is_infected = infected; }
    void setHoneypot(bool honeypot) { is_honeypot = honeypot; }
    
    std::string getOSString() const {
        switch(os_type) {
            case OSType::WINDOWS: return "Windows_11";
            case OSType::LINUX: return "Ubuntu_22.04";
            case OSType::MACOS: return "macOS_14";
            case OSType::EMBEDDED: return "Embedded_Linux";
            default: return "Unknown";
        }
    }

    void displayInfo() const {
        std::cout << "Host(" << hostname << " [" << ip_address << "] | " 
                  << getOSString() << " | Infected: " << (is_infected ? "Yes" : "No");
        if (is_honeypot) std::cout << " | HONEYPOT";
        std::cout << " | Segment: ";
        
        switch(network_info.segment) {
            case NetworkSegment::DMZ: std::cout << "DMZ"; break;
            case NetworkSegment::INTERNAL: std::cout << "Internal"; break;
            case NetworkSegment::CRITICAL: std::cout << "Critical"; break;
            case NetworkSegment::ISOLATED: std::cout << "Isolated"; break;
        }
        
        std::cout << " | Security: ";
        for (size_t i = 0; i < security_software.size(); ++i) {
            std::cout << security_software[i];
            if (i < security_software.size() - 1) std::cout << ", ";
        }
        std::cout << ")" << std::endl;
    }
};

// AI Cognitive Engine Simulation
class CognitiveEngine {
private:
    std::map<OSType, double> os_knowledge;
    std::map<std::string, double> security_bypass_knowledge;
    std::map<NetworkSegment, double> network_knowledge;
    double learning_rate;

public:
    CognitiveEngine() : learning_rate(0.1) {
        // Initialize knowledge
        os_knowledge[OSType::WINDOWS] = 0.7;
        os_knowledge[OSType::LINUX] = 0.3;
        os_knowledge[OSType::MACOS] = 0.2;
        os_knowledge[OSType::EMBEDDED] = 0.1;
    }

    double analyzeHost(const Host& host) {
        double success_probability = 0.5; // Base probability
        
        // Reflect OS knowledge
        auto os_iter = os_knowledge.find(host.getOSType());
        if (os_iter != os_knowledge.end()) {
            success_probability += os_iter->second * 0.3;
        }
        
        // Analyze security software
        for (const auto& security : host.getSecuritySoftware()) {
            auto sec_iter = security_bypass_knowledge.find(security);
            if (sec_iter != security_bypass_knowledge.end()) {
                success_probability += sec_iter->second * 0.2;
            } else {
                success_probability -= 0.1; // Unknown security tool
            }
        }
        
        // Analyze network segment
        auto net_iter = network_knowledge.find(host.getNetworkSegment());
        if (net_iter != network_knowledge.end()) {
            success_probability += net_iter->second * 0.2;
        }
        
        return std::max(0.0, std::min(1.0, success_probability));
    }

    void learnFromSuccess(const Host& host, const std::string& method) {
        // Learn from successful attacks
        os_knowledge[host.getOSType()] += learning_rate;
        network_knowledge[host.getNetworkSegment()] += learning_rate;
        
        for (const auto& security : host.getSecuritySoftware()) {
            security_bypass_knowledge[security] += learning_rate;
        }
        
        std::cout << "  [COGNITIVE] Learning completed via " << method << std::endl;
    }

    void learnFromFailure(const Host& host, const std::string& reason) {
        // Learn from failures (more cautious approach)
        for (const auto& security : host.getSecuritySoftware()) {
            if (security_bypass_knowledge.find(security) == security_bypass_knowledge.end()) {
                security_bypass_knowledge[security] = 0.0;
            }
        }
        
        std::cout << "  [COGNITIVE] Failure analysis: " << reason << std::endl;
    }
};

// Self-Morphing Engine Simulation
class SelfMorphingEngine {
private:
    std::vector<std::string> available_mutations;
    int mutation_level;

public:
    SelfMorphingEngine() : mutation_level(1) {
        available_mutations = {
            "Code_Obfuscation", "Register_Reallocation", "Control_Flow_Change",
            "Function_Splitting", "Encryption_Layer", "JIT_Compilation"
        };
    }

    std::string generateMutation(const std::string& threat_detected) {
        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_int_distribution<> dis(0, available_mutations.size() - 1);
        
        std::string mutation = available_mutations[dis(gen)];
        mutation_level++;
        
        std::cout << "  [MORPHING] Mutation due to " << threat_detected << " detection: " 
                  << mutation << " (Level " << mutation_level << ")" << std::endl;
        
        return mutation;
    }

    bool evadeDetection(const std::vector<std::string>& security_software) {
        // Compare number of security software and mutation_level
        double evasion_success = static_cast<double>(mutation_level) / 
                                (security_software.size() + mutation_level);
        
        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_real_distribution<> dis(0.0, 1.0);
        
        return dis(gen) < evasion_success;
    }
};

// Automated Exploit Module Simulation
class AutoExploitModule {
private:
    std::set<std::string> discovered_exploits;
    std::map<std::string, double> exploit_reliability;

public:
    AutoExploitModule() {
        // Initial exploit knowledge
        discovered_exploits.insert("CVE-2025-101A");
        exploit_reliability["CVE-2025-101A"] = 0.8;
    }

    bool discoverZeroDay(const std::vector<std::string>& target_vulns) {
        std::random_device rd;
        std::mt19937 gen(rd());
        std::uniform_real_distribution<> dis(0.0, 1.0);
        
        for (const auto& vuln : target_vulns) {
            if (discovered_exploits.find(vuln) == discovered_exploits.end()) {
                if (dis(gen) < 0.3) { // 30% chance of discovering zero-day
                    discovered_exploits.insert(vuln);
                    exploit_reliability[vuln] = 0.6 + dis(gen) * 0.3; // 60-90% reliability
                    
                    std::cout << "  [ZERO-DAY] New vulnerability discovered: " << vuln 
                              << " (Reliability: " << std::fixed << std::setprecision(2) 
                              << exploit_reliability[vuln] * 100 << "%)" << std::endl;
                    return true;
                }
            }
        }
        return false;
    }

    double calculateExploitSuccess(const std::vector<std::string>& target_vulns) {
        double max_success = 0.0;
        
        for (const auto& vuln : target_vulns) {
            if (discovered_exploits.find(vuln) != discovered_exploits.end()) {
                max_success = std::max(max_success, exploit_reliability[vuln]);
            }
        }
        
        return max_success;
    }

    std::vector<std::string> getKnownExploits() const {
        return std::vector<std::string>(discovered_exploits.begin(), discovered_exploits.end());
    }
};

// Swarm Intelligence Communication Simulation
class SwarmIntelligence {
private:
    std::map<std::string, std::string> shared_knowledge;
    std::queue<std::string> command_queue;
    int network_size;

public:
    SwarmIntelligence() : network_size(1) {}

    void shareKnowledge(const std::string& key, const std::string& value) {
        shared_knowledge[key] = value;
        std::cout << "  [SWARM] Knowledge shared to swarm: " << key << " -> " << value << std::endl;
    }

    bool hasKnowledge(const std::string& key) const {
        return shared_knowledge.find(key) != shared_knowledge.end();
    }

    void broadcastSuccess(const std::string& method, const std::string& target) {
        shareKnowledge("success_method_" + target, method);
        network_size++;
    }

    void syncWithPeers() {
        if (network_size > 1) {
            std::cout << "  [SWARM] Synchronization with " << network_size << " nodes completed" << std::endl;
        }
    }
};

class EvolutionaryWorm {
private:
    double version;
    std::unique_ptr<CognitiveEngine> cognitive_engine;
    std::unique_ptr<SelfMorphingEngine> morphing_engine;
    std::unique_ptr<AutoExploitModule> exploit_module;
    std::unique_ptr<SwarmIntelligence> swarm_intelligence;
    std::set<Host*> infected_hosts;
    std::map<std::string, int> attack_attempts;
    std::mt19937 rng;
    int stealth_level;
    bool dormant_mode;

public:
    EvolutionaryWorm(Host* initial_target) 
        : version(1.0), rng(std::random_device{}()), stealth_level(3), dormant_mode(false) {
        
        cognitive_engine = std::make_unique<CognitiveEngine>();
        morphing_engine = std::make_unique<SelfMorphingEngine>();
        exploit_module = std::make_unique<AutoExploitModule>();
        swarm_intelligence = std::make_unique<SwarmIntelligence>();
        
        infected_hosts.insert(initial_target);
        initial_target->setInfected(true);
        
        std::cout << std::fixed << std::setprecision(1);
        std::cout << "Worm 'KawanHive' v" << version << " created successfully" << std::endl;
        std::cout << "  - Cognitive engine, morphing engine, exploit module, swarm intelligence module activated" << std::endl;
        std::cout << "  - Initial infection target: " << initial_target->getHostname() 
                  << " [" << initial_target->getIpAddress() << "]" << std::endl;
    }

    void enterDormantMode(int days) {
        dormant_mode = true;
        std::cout << "\n  [STEALTH] Entering dormant mode - Starting environmental learning for " << days << " days" << std::endl;
        
        // Fast learning for simulation
        std::this_thread::sleep_for(std::chrono::milliseconds(1000));
        
        std::cout << "  [LEARNING] Network topology analysis completed" << std::endl;
        std::cout << "  [LEARNING] User behavior pattern learning completed" << std::endl;
        std::cout << "  [LEARNING] Security tool signature analysis completed" << std::endl;
        
        dormant_mode = false;
        stealth_level += 2;
        std::cout << "  [STEALTH] Exiting dormant mode - Stealth level increased: " << stealth_level << std::endl;
    }

    void evolve(const std::string& reason, const std::string& new_knowledge) {
        std::this_thread::sleep_for(std::chrono::milliseconds(500));
        
        version += 0.1;
        std::cout << "  [EVOLVING!] Reason: " << reason << std::endl;
        std::cout << "  [+] New knowledge acquired: " << new_knowledge << std::endl;
        std::cout << "  [*] Worm version evolved to " << std::fixed << std::setprecision(1) 
                  << version << std::endl;
        
        // Share evolution information to swarm
        swarm_intelligence->shareKnowledge("evolution_" + std::to_string(version), new_knowledge);
    }

    bool scanAndAdapt(Host* host) {
        std::cout << "\n- [Scan] " << host->getHostname() << " [" << host->getIpAddress() << "]" << std::endl;
        
        // Honeypot detection
        if (host->getIsHoneypot()) {
            std::uniform_real_distribution<> dis(0.0, 1.0);
            if (dis(rng) < 0.7) { // 70% chance of honeypot detection
                std::cout << "  [WARNING] Honeypot detected! Aborting attack" << std::endl;
                return false;
            }
        }
        
        // Analyze success probability with cognitive engine
        double success_prob = cognitive_engine->analyzeHost(*host);
        std::cout << "  [COGNITIVE] Infiltration success probability: " << std::fixed << std::setprecision(2) 
                  << success_prob * 100 << "%" << std::endl;
        
        // Analyze security software and morph
        bool evasion_success = morphing_engine->evadeDetection(host->getSecuritySoftware());
        if (!evasion_success) {
            std::cout << "  [MORPHING] Evasion failed" << std::endl;
            for (const auto& security : host->getSecuritySoftware()) {
                morphing_engine->generateMutation(security);
            }
            evolve("Security tool detection evasion", "New morphing technique");
            return false;
        }
        
        // Attempt zero-day discovery
        if (exploit_module->discoverZeroDay(host->getVulnerabilities())) {
            evolve("Zero-day vulnerability discovered", "Automated exploit development");
        }
        
        // Calculate final attack success rate
        double exploit_success = exploit_module->calculateExploitSuccess(host->getVulnerabilities());
        double final_success = (success_prob + exploit_success + (stealth_level * 0.1)) / 3.0;
        
        std::uniform_real_distribution<> dis(0.0, 1.0);
        bool attack_success = dis(rng) < final_success;
        
        if (attack_success) {
            cognitive_engine->learnFromSuccess(*host, "Multi-vector Attack");
            swarm_intelligence->broadcastSuccess("Infiltration Success", host->getHostname());
        } else {
            cognitive_engine->learnFromFailure(*host, "Security Defense System");
        }
        
        return attack_success;
    }

    void propagate(std::vector<Host>& network) {
        if (dormant_mode) {
            return;
        }
        
        std::cout << "\n" << std::string(20, '=') 
                  << " Propagation Attempt (KawanHive v" << std::fixed << std::setprecision(1) 
                  << version << ") " << std::string(20, '=') << std::endl;
        
        // Swarm synchronization
        swarm_intelligence->syncWithPeers();
        
        std::vector<Host*> newly_infected;
        std::vector<Host*> high_priority_targets;
        std::vector<Host*> regular_targets;
        
        // Classify targets by priority
        for (auto& host : network) {
            if (!host.getIsInfected()) {
                if (host.getNetworkSegment() == NetworkSegment::CRITICAL) {
                    high_priority_targets.push_back(&host);
                } else {
                    regular_targets.push_back(&host);
                }
            }
        }
        
        // Attack high-priority targets first
        std::shuffle(high_priority_targets.begin(), high_priority_targets.end(), rng);
        std::shuffle(regular_targets.begin(), regular_targets.end(), rng);
        
        std::vector<Host*> all_targets;
        all_targets.insert(all_targets.end(), high_priority_targets.begin(), high_priority_targets.end());
        all_targets.insert(all_targets.end(), regular_targets.begin(), regular_targets.end());
        
        for (auto* host : all_targets) {
            if (scanAndAdapt(host)) {
                std::cout << "  [SUCCESS] " << host->getHostname() 
                          << " Infiltration successful!" << std::endl;
                host->setInfected(true);
                newly_infected.push_back(host);
                
                // Additional actions for special targets
                if (host->getNetworkSegment() == NetworkSegment::CRITICAL) {
                    std::cout << "  [CRITICAL] Critical infrastructure infiltrated - Special mission activated" << std::endl;
                }
            } else {
                std::cout << "  [FAILED] " << host->getHostname() 
                          << " Infiltration failed" << std::endl;
            }
            
            // Delay to reduce detection risk
            std::this_thread::sleep_for(std::chrono::milliseconds(200));
        }
        
        if (!newly_infected.empty()) {
            for (auto* host : newly_infected) {
                infected_hosts.insert(host);
            }
            std::cout << "\n[Infection Spread] Newly infected: ";
            for (size_t i = 0; i < newly_infected.size(); ++i) {
                std::cout << newly_infected[i]->getHostname();
                if (i < newly_infected.size() - 1) std::cout << ", ";
            }
            std::cout << std::endl;
        }
    }

    void displayFinalStatus() const {
        std::cout << "\n" << std::string(50, '=') << std::endl;
        std::cout << "[Simulation End] Final KawanHive Status Report" << std::endl;
        std::cout << std::string(50, '=') << std::endl;
        
        std::cout << "Worm Version: " << std::fixed << std::setprecision(1) << version << std::endl;
        std::cout << "Number of Infected Hosts: " << infected_hosts.size() << std::endl;
        std::cout << "Stealth Level: " << stealth_level << std::endl;
        
        std::cout << "\nAcquired Exploits: ";
        auto exploits = exploit_module->getKnownExploits();
        for (size_t i = 0; i < exploits.size(); ++i) {
            std::cout << exploits[i];
            if (i < exploits.size() - 1) std::cout << ", ";
        }
        std::cout << std::endl;
        
        std::cout << "\nList of Infected Hosts:" << std::endl;
        for (const auto* host : infected_hosts) {
            std::cout << "  - " << host->getHostname() << " [" 
                      << host->getIpAddress() << "]" << std::endl;
        }
    }
};

// --- Simulation Execution ---
int main() {
    #ifdef _WIN32
    system("chcp 65001 > nul");
    #endif

    std::cout << "=== Evolutionary Worm 'KawanHive' Advanced Simulation ===" << std::endl;
    std::cout << "⚠️  Exploration Purpose Simulation - No Actual Malicious Activity ⚠️\n" << std::endl;

    // 1. Build a complex network environment
    std::vector<Host> network_hosts = {
        Host("192.168.1.10", "Employee-PC-001", OSType::WINDOWS, 
             {"CVE-2025-101A"}, {"BasicFirewall"}, NetworkSegment::INTERNAL),
        Host("192.168.1.20", "WebServer-DMZ", OSType::LINUX, 
             {"CVE-2025-202B"}, {"BasicFirewall", "StandardAV"}, NetworkSegment::DMZ),
        Host("192.168.1.30", "Database-Core", OSType::LINUX, 
             {"CVE-2025-303C"}, {"AdvancedEDR"}, NetworkSegment::CRITICAL),
        Host("192.168.1.40", "Admin-Workstation", OSType::WINDOWS, 
             {"CVE-2025-101A", "CVE-2025-202B"}, {"AdvancedEDR", "BehaviorAnalyzer"}, NetworkSegment::INTERNAL),
        Host("192.168.1.50", "SCADA-Controller", OSType::EMBEDDED, 
             {"CVE-2025-404D_ZERO_DAY", "CVE-2025-606F_ICS"}, {"AI_ThreatDetector"}, NetworkSegment::CRITICAL),
        Host("192.168.1.60", "Honeypot-Decoy", OSType::LINUX, 
             {"CVE-2025-101A"}, {"HoneypotSystem"}, NetworkSegment::DMZ),
        Host("192.168.1.70", "Finance-Server", OSType::WINDOWS, 
             {"CVE-2025-707G_KERNEL"}, {"QuantumEncryption", "AdvancedEDR"}, NetworkSegment::CRITICAL)
    };
    
    // Set honeypot
    network_hosts[5].setHoneypot(true);

    // 2. Create KawanHive worm and initial dormancy
    EvolutionaryWorm KawanHive_worm(&network_hosts[0]);
    
    std::cout << "\n" << std::string(40, '-') << std::endl;
    std::cout << "Phase 1: Initial Infiltration and Dormancy" << std::endl;
    std::cout << std::string(40, '-') << std::endl;
    KawanHive_worm.enterDormantMode(7); // Dormant for 7 days

    // 3. Multi-stage propagation simulation
    std::vector<std::string> phase_names = {
        "Phase 2: Internal Reconnaissance", 
        "Phase 3: Lateral Movement", 
        "Phase 4: Privilege Escalation", 
        "Phase 5: Objective Achievement"
    };
    
    for (int phase = 0; phase < 4; ++phase) {
        std::cout << "\n\n" << std::string(50, '#') << std::endl;
        std::cout << phase_names[phase] << " (Day " << (phase + 2) << ")" << std::endl;
        std::cout << std::string(50, '#') << std::endl;
        
        KawanHive_worm.propagate(network_hosts);

        std::cout << "\n--- Network Status After Day " << (phase + 2) << " ---" << std::endl;
        for (const auto& host : network_hosts) {
            host.displayInfo();
        }
        
        std::this_thread::sleep_for(std::chrono::seconds(1));
    }

    // 4. Final results and threat analysis
    KawanHive_worm.displayFinalStatus();
    
    std::cout << "\n" << std::string(50, '=') << std::endl;
    std::cout << "Threat Analysis Report" << std::endl;
    std::cout << std::string(50, '=') << std::endl;
    
    int critical_infected = 0;
    int total_infected = 0;
    
    for (const auto& host : network_hosts) {
        if (host.getIsInfected()) {
            total_infected++;
            if (host.getNetworkSegment() == NetworkSegment::CRITICAL) {
                critical_infected++;
            }
        }
    }
    
    std::cout << "Overall Infection Rate: " << total_infected << "/" << network_hosts.size() 
              << " (" << std::fixed << std::setprecision(1) 
              << (static_cast<double>(total_infected) / network_hosts.size() * 100) << "%)" << std::endl;
    std::cout << "Critical Infrastructure Infiltration: " << critical_infected << " systems" << std::endl;
    
    if (critical_infected > 0) {
        std::cout << "\n⚠️  Warning: Critical Infrastructure Infiltration Successful - High Potential for Physical Damage" << std::endl;
    }

    std::cout << "\n=== Advanced Simulation Completed ===" << std::endl;
    return 0;
}

---

4. Attacker Profile & Motivation Analysis

4.1. Nation-State Actors

• Motivation: Geopolitical advantage, neutralization of hostile nations' critical infrastructure, large-scale espionage activities.
• Characteristics: Vast capital and personnel, long-term operation execution, acquisition of numerous zero-day vulnerabilities.

4.2. Elite Cybercrime Syndicates

• Motivation: Astronomical financial gains (nationwide ransomware, direct attacks on financial systems).
• Characteristics: Ability to sell solutions to subordinate organizations in the form of RaaS (Ransomware-as-a-Service), rapid decision-making and bold attacks.

4.3. AI Anarchists/Terrorists

• Motivation: Dissatisfaction with existing systems, causing social chaos, demonstrating technological superiority.
• Characteristics: Unpredictable attack patterns, political/ideological objectives.

---

5. Advanced Defense Techniques & Future Outlook

5.1. Bio-inspired Cyber Defense

5.1.1 Mimicking Immune Systems

• Adaptive immunity: Remembering previous attacks and rapid response.
• Innate immunity: Immediate reaction to unknown threats.
• Immune memory: Long-term preservation and utilization of threat information.

5.1.2 Ecosystem-Based Defense

• Promoting diversity: Preventing collective infection through heterogeneity of system environments.
• Symbiotic relationships: Collaborative defense between different security solutions.
• Natural selection: Automatic evolution of effective defense techniques.

5.2. Cybersecurity in the 6G Network Era

5.2.1 Network Slicing Security

• Independent security policies per slice.
• Dynamic slice isolation and reconfiguration.
• Edge-cloud linked security.

5.2.2 Holographic Communication Security

• 3D hologram data encryption.
• Haptic feedback security.
• Protection of virtual-physical converged environments.

5.3. Metaverse and Digital Twin Security

5.3.1 Virtual World Threat Model

• Avatar hijacking.
• Virtual asset theft.
• Reality-virtual boundary attacks.

5.3.2 Digital Twin Security

• Physical-digital synchronization security.
• Integrity of simulation results.
• Prevention of predictive model manipulation.

---

6. Ethical & Policy Recommendations

• Establishment of International Norms for Autonomous Offensive AI Development: Raising the necessity for international agreements on the development and use restrictions of AI that autonomously set goals and attack without human intervention, similar to 'autonomous lethal weapons.'
• National Strategy for Fostering AI Security Talent: Proposing national investment and education programs for training defensive AI experts and ethical hackers to counter threats that aggressively utilize AI.
• Legalization of Mandatory Public-Private/International Information Sharing: Establishing a legal framework that mandates real-time sharing of relevant information with key domestic institutions and allied nations upon discovery of large-scale threats like 'KawanHive'.

---

7. Conclusion

The evolutionary worm 'KawanHive' is no longer a hypothetical scenario but a realistic threat that could emerge in the near future. To counter such intelligent threats, it is urgent to fundamentally shift from existing passive and perimeter-based security to an 'active and intelligent defense' system based on AI and automation.

Key Response Strategies:

1. AI vs AI Paradigm: Countering AI attacks with AI defenses.
2. Leveraging Collective Intelligence: Uniting the collective intelligence of the global security community.
3. Predictive Defense: Proactive response before attacks occur.
4. Adaptive Evolution: Defense systems also continuously learn and evolve.
5. International Cooperation: Cyber threats transcend borders, making international cooperation essential.

The convergence of zero trust, AI-driven detection, active deception, and quantum cryptography will be key survival strategies in the future of cyber warfare. We are at a paradigm shift in cybersecurity, and proactive preparation and investment will determine the survival of nations and organizations.