First Generation (1987-1995):
  - Signature-based only
  - Static pattern matching
  - Simple string searches
  - Limited effectiveness
  
Second Generation (1995-2005):
  - Heuristic analysis added
  - Behavioral monitoring
  - Generic detection rules
  - Better unknown threat handling
  
Third Generation (2005-2015):
  - Cloud-based intelligence
  - Real-time reputation
  - Machine learning basics
  - Proactive protection
  
Fourth Generation (2015-Present):
  - AI/ML integration
  - Advanced behavioral analysis
  - Zero-day protection
  - Threat hunting capabilities

Hash-based Signatures:
  - MD5, SHA-1, SHA-256 hashes
  - Exact file matching
  - Fast comparison
  - Easily evaded by modification
  
String-based Signatures:
  - Text patterns in malware
  - Hexadecimal byte sequences
  - Regular expressions
  - More flexible than hashes
  
Structural Signatures:
  - File structure patterns
  - Import table analysis
  - Section characteristics
  - PE header anomalies

rule Trojan_Banker_Generic
{
    meta:
        author = "Security Analyst"
        date = "2024-01-01"
        description = "Generic banking trojan"
        
    strings:
        $a = "GetWindowText" ascii
        $b = "keylog" ascii nocase
        $c = { 48 89 5C 24 08 48 89 74 24 10 }
        $d = /https:\/\/[a-z0-9]+\.onion\//
        
    condition:
        2 of them and filesize < 5MB
}

# Example: Simple polymorphic technique
import random
import string

def generate_variant(malware_code):
    # Add random junk code
    junk = ''.join(random.choices(string.ascii_letters, k=100))
    
    # Insert NOPs (No Operation instructions)
    nops = 'NOP ' * random.randint(10, 50)
    
    # Change variable names
    old_vars = ['temp', 'data', 'buffer']
    new_vars = [''.join(random.choices(string.ascii_letters, k=8)) 
                for _ in old_vars]
    
    # Return modified code
    return modify_code(malware_code, junk, nops, old_vars, new_vars)

Code Analysis:
  - Suspicious API imports
  - Unusual file structure
  - Encrypted/packed sections
  - Anti-debugging techniques
  
Entropy Analysis:
  - File randomness measurement
  - Packed/encrypted detection
  - Normal range: 1.0-7.0
  - Suspicious: 7.5-8.0
  
Import Analysis:
  - Dangerous API calls
  - CreateProcess, WriteFile
  - Registry modification
  - Network communication
  
Pattern Recognition:
  - Known malware families
  - Compiler signatures
  - Development patterns
  - Obfuscation techniques

import math
from collections import Counter

def calculate_entropy(data):
    if not data:
        return 0
    
    # Count byte frequencies
    byte_counts = Counter(data)
    data_length = len(data)
    
    # Calculate entropy
    entropy = 0
    for count in byte_counts.values():
        probability = count / data_length
        entropy -= probability * math.log2(probability)
    
    return entropy

# Example usage
with open('suspicious.exe', 'rb') as f:
    file_data = f.read()
    entropy = calculate_entropy(file_data)
    
    if entropy > 7.5:
        print("High entropy - possible packing/encryption")

Process Behavior:
  - Unusual process creation
  - Code injection attempts
  - Memory manipulation
  - Privilege escalation
  - DLL hijacking
  
File System Activity:
  - Mass file encryption
  - Hidden file creation
  - System file modification
  - Autostart registry entries
  - Shadow copy deletion
  
Network Behavior:
  - C2 server communication
  - DNS tunneling
  - Data exfiltration
  - Port scanning
  - Protocol anomalies
  
System Modifications:
  - Security software disabling
  - Firewall rule changes
  - Service installations
  - Boot sector modifications
  - System file replacements

class BehaviorAnalyzer:
    def __init__(self):
        self.risk_score = 0
        self.detected_behaviors = []
    
    def analyze_process_creation(self, parent, child):
        suspicious_patterns = [
            ('cmd.exe', 'powershell.exe'),
            ('winword.exe', 'cmd.exe'),
            ('excel.exe', 'wscript.exe')
        ]
        
        if (parent, child) in suspicious_patterns:
            self.risk_score += 30
            self.detected_behaviors.append("Suspicious process chain")
    
    def analyze_file_activity(self, file_operations):
        encryption_extensions = ['.locked', '.encrypted', '.crypto']
        mass_file_changes = len([op for op in file_operations 
                                if op.type == 'modify']) > 100
        
        if mass_file_changes:
            self.risk_score += 50
            self.detected_behaviors.append("Mass file modification")
    
    def get_verdict(self):
        if self.risk_score > 70:
            return "MALWARE"
        elif self.risk_score > 40:
            return "SUSPICIOUS"
        else:
            return "CLEAN"

File Reputation:
  - Global file prevalence
  - First seen timestamp
  - Digital signatures
  - Certification authority
  - Download sources
  
URL Reputation:
  - Website categorization
  - Malware hosting history
  - Phishing attempts
  - Suspicious redirects
  - Geographic anomalies
  
IP Reputation:
  - Botnet participation
  - C2 server hosting
  - Spam source
  - Attack participation
  - Geographic location

Supervised Learning:
  - Known malware training
  - Feature extraction
  - Classification models
  - High accuracy on known patterns
  
Unsupervised Learning:
  - Anomaly detection
  - Clustering similar samples
  - Zero-day identification
  - Behavioral pattern discovery
  
Deep Learning:
  - Neural networks
  - Image recognition (visualization)
  - Natural language processing
  - Advanced pattern recognition

# ML feature extraction for PE files
import pefile
import numpy as np

def extract_pe_features(filename):
    try:
        pe = pefile.PE(filename)
        features = {}
        
        # Basic PE information
        features['num_sections'] = len(pe.sections)
        features['size_of_image'] = pe.OPTIONAL_HEADER.SizeOfImage
        features['entry_point'] = pe.OPTIONAL_HEADER.AddressOfEntryPoint
        
        # Import table analysis
        if hasattr(pe, 'DIRECTORY_ENTRY_IMPORT'):
            features['num_imported_dlls'] = len(pe.DIRECTORY_ENTRY_IMPORT)
            features['num_imported_functions'] = sum(
                len(entry.imports) for entry in pe.DIRECTORY_ENTRY_IMPORT
            )
        else:
            features['num_imported_dlls'] = 0
            features['num_imported_functions'] = 0
        
        # Section analysis
        section_entropies = []
        for section in pe.sections:
            data = section.get_data()
            entropy = calculate_entropy(data)
            section_entropies.append(entropy)
        
        features['max_section_entropy'] = max(section_entropies)
        features['avg_section_entropy'] = np.mean(section_entropies)
        
        return features
        
    except Exception as e:
        return None

File System Events:
  - File creation/modification
  - File execution attempts
  - Archive extractions
  - Downloads completion
  - USB/removable media
  
Process Events:
  - Process creation
  - DLL loading
  - Memory allocation
  - Code injection
  - Privilege escalation
  
Network Events:
  - HTTP/HTTPS requests
  - Email attachments
  - File transfers
  - P2P connections
  - DNS resolutions
  
Registry Events:
  - Autorun modifications
  - Security setting changes
  - Service installations
  - Policy modifications
  - Browser settings

// Simplified file system filter driver
NTSTATUS FileSystemFilter(
    PDEVICE_OBJECT DeviceObject,
    PIRP Irp
) {
    PIO_STACK_LOCATION stack = IoGetCurrentIrpStackLocation(Irp);
    
    switch (stack->MajorFunction) {
        case IRP_MJ_CREATE:
            // File open/create operation
            return HandleFileOpen(Irp);
            
        case IRP_MJ_WRITE:
            // File write operation
            return HandleFileWrite(Irp);
            
        case IRP_MJ_SET_INFORMATION:
            // File modification
            return HandleFileModify(Irp);
    }
    
    // Pass through to next driver
    return PassThroughIrp(DeviceObject, Irp);
}

Digital Signatures:
  - Certificate validation
  - Publisher verification
  - Code signing checks
  - Revocation status
  
Path-based Rules:
  - Trusted directories
  - System locations
  - Application folders
  - User-defined paths
  
Hash-based Rules:
  - File hash allowlists
  - Known good binaries
  - Approved versions
  - Custom applications
  
Behavioral Rules:
  - Application behavior patterns
  - Normal operation profiles
  - Resource usage limits
  - Network communication rules

# Create WDAC policy
New-CIPolicy -Level Publisher -FilePath "C:\Policy\BasePolicy.xml" `
  -UserPEs -ScanPath "C:\Program Files\"

# Convert to binary format
ConvertFrom-CIPolicy -XmlFilePath "C:\Policy\BasePolicy.xml" `
  -BinaryFilePath "C:\Policy\Policy.bin"

# Deploy policy
Copy-Item "C:\Policy\Policy.bin" `
  "C:\Windows\System32\CodeIntegrity\SiPolicy.p7b"

# Enable policy enforcement
Set-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\CI\Config" `
  -Name "VirtualizationBasedProtection" -Value 1

Containment:
  - Network isolation
  - Process termination
  - File quarantine
  - User account suspension
  
Investigation:
  - Process tree analysis
  - Network connection mapping
  - File system timeline
  - Registry change tracking
  
Remediation:
  - Malware removal
  - System restoration
  - Patch deployment
  - Configuration hardening

class EDRResponseEngine:
    def __init__(self):
        self.threat_threshold = 75
        self.response_actions = []
    
    def analyze_alert(self, alert):
        risk_score = self.calculate_risk(alert)
        
        if risk_score > self.threat_threshold:
            # Immediate containment
            self.isolate_endpoint(alert.endpoint_id)
            
            # Kill malicious processes
            for process in alert.malicious_processes:
                self.terminate_process(process.pid)
            
            # Quarantine files
            for file in alert.malicious_files:
                self.quarantine_file(file.path)
            
            # Notify analysts
            self.create_incident(alert, risk_score)
    
    def calculate_risk(self, alert):
        score = 0
        
        # Process-based indicators
        if alert.has_code_injection():
            score += 30
        if alert.has_lateral_movement():
            score += 25
        if alert.has_privilege_escalation():
            score += 20
            
        # Network indicators
        if alert.has_c2_communication():
            score += 40
        if alert.has_data_exfiltration():
            score += 35
            
        return score
    
    def isolate_endpoint(self, endpoint_id):
        # Remove network access
        self.firewall_block_all(endpoint_id)
        # Maintain management connection only
        self.allow_management_traffic(endpoint_id)

Scalability:
  - Number of endpoints
  - Geographic distribution
  - Network bandwidth
  - Server capacity
  
Performance:
  - System resource usage
  - Scan scheduling
  - Update distribution
  - Real-time protection impact
  
Management:
  - Centralized console
  - Policy deployment
  - Reporting and alerts
  - Compliance monitoring

# Deploy antivirus via Group Policy
$GPO = New-GPO -Name "Antivirus Deployment"
$OU = "OU=Computers,DC=company,DC=com"

# Link GPO to OU
New-GPLink -Name "Antivirus Deployment" -Target $OU

# Configure software installation
Set-GPPrefRegistryValue -Name "Antivirus Deployment" `
  -Context Computer `
  -Key "HKLM\Software\Policies\Microsoft\Windows\Installer" `
  -ValueName "AlwaysInstallElevated" -Value 1 -Type DWord

Scan Optimization:
  - Intelligent scheduling
  - File type exclusions
  - Path exclusions
  - Priority-based scanning
  
Resource Management:
  - CPU throttling
  - Memory limits
  - I/O throttling
  - Background operations
  
Update Optimization:
  - Delta updates
  - Peer-to-peer distribution
  - Local caching servers
  - Bandwidth controls
  
False Positive Reduction:
  - Whitelist management
  - Application profiling
  - Custom signatures
  - Behavioral tuning

import psutil
import time

class AVPerformanceMonitor:
    def __init__(self, av_process_name="antivirus.exe"):
        self.av_process_name = av_process_name
        self.metrics = []
    
    def monitor_performance(self, duration=3600):  # 1 hour
        start_time = time.time()
        
        while time.time() - start_time < duration:
            try:
                # Find AV process
                for proc in psutil.process_iter(['pid', 'name']):
                    if proc.info['name'] == self.av_process_name:
                        # Collect metrics
                        metrics = {
                            'timestamp': time.time(),
                            'cpu_percent': proc.cpu_percent(),
                            'memory_mb': proc.memory_info().rss / 1024 / 1024,
                            'disk_io': proc.io_counters()._asdict(),
                            'num_handles': proc.num_handles()
                        }
                        
                        self.metrics.append(metrics)
                        break
                
                time.sleep(60)  # Collect every minute
                
            except psutil.NoSuchProcess:
                continue
    
    def generate_report(self):
        if not self.metrics:
            return "No data collected"
        
        avg_cpu = sum(m['cpu_percent'] for m in self.metrics) / len(self.metrics)
        avg_memory = sum(m['memory_mb'] for m in self.metrics) / len(self.metrics)
        
        return f"Average CPU: {avg_cpu:.2f}%, Average Memory: {avg_memory:.2f} MB"

Fileless Malware:
  - Memory-only execution
  - Living-off-the-land techniques
  - PowerShell/WMI abuse
  - Registry-based persistence
  
AI-Powered Attacks:
  - Machine learning evasion
  - Adaptive malware
  - Deepfake social engineering
  - Automated exploit development
  
Supply Chain Attacks:
  - Software build processes
  - Third-party components
  - Update mechanisms
  - Hardware implants
  
Zero-Day Exploits:
  - Unknown vulnerabilities
  - Advanced exploitation
  - Targeted campaigns
  - Nation-state actors

# Example: Advanced evasion technique
import time
import random
import ctypes

class AdvancedEvasion:
    def __init__(self):
        self.sandbox_checks = [
            self.check_virtual_environment,
            self.check_debugging,
            self.check_analysis_tools,
            self.check_execution_delay
        ]
    
    def check_virtual_environment(self):
        # Check for VM artifacts
        vm_artifacts = [
            "VirtualBox", "VMware", "QEMU", 
            "Xen", "Parallels", "Hyper-V"
        ]
        
        system_info = self.get_system_info()
        return any(artifact in system_info for artifact in vm_artifacts)
    
    def check_analysis_tools(self):
        # Check for analysis tools
        analysis_tools = [
            "wireshark.exe", "procmon.exe", "regshot.exe",
            "ida.exe", "ollydbg.exe", "x64dbg.exe"
        ]
        
        running_processes = self.get_process_list()
        return any(tool in running_processes for tool in analysis_tools)
    
    def evade_detection(self):
        # Perform evasion checks
        if any(check() for check in self.sandbox_checks):
            # Exit quietly if analysis environment detected
            return False
        
        # Proceed with malicious payload
        return True

Machine Learning Integration:
  - Deep neural networks
  - Ensemble methods
  - Anomaly detection
  - Behavioral clustering
  
Automated Threat Hunting:
  - Proactive threat search
  - Hypothesis-driven investigation
  - Pattern recognition
  - IoC correlation
  
Predictive Analytics:
  - Risk scoring
  - Threat forecasting
  - Attack path analysis
  - Impact assessment
  
Adaptive Defenses:
  - Dynamic policy adjustment
  - Contextual security
  - Risk-based responses
  - Learning from attacks

class ZeroTrustEngine:
    def __init__(self):
        self.trust_levels = {
            'UNTRUSTED': 0,
            'LIMITED': 25,
            'STANDARD': 50,
            'ELEVATED': 75,
            'PRIVILEGED': 100
        }
    
    def calculate_trust_score(self, context):
        score = 0
        
        # Device trust
        if context.device.is_managed():
            score += 20
        if context.device.is_compliant():
            score += 15
        if context.device.has_certificate():
            score += 10
            
        # User behavior
        if context.user.normal_location():
            score += 15
        if context.user.normal_time():
            score += 10
        if context.user.mfa_authenticated():
            score += 20
            
        # Network context
        if context.network.is_corporate():
            score += 10
            
        return min(score, 100)
    
    def make_access_decision(self, request):
        trust_score = self.calculate_trust_score(request.context)
        required_trust = self.get_required_trust(request.resource)
        
        if trust_score >= required_trust:
            return self.grant_access(request, trust_score)
        else:
            return self.challenge_or_deny(request, trust_score)