🚀 Next Steps Implementation

#!/bin/bash
# Advanced Security Features Implementation

echo "Implementing advanced security features..."

# Install additional security tools
sudo apt update
sudo apt install -y \
    fail2ban \
    ufw \
    aide \
    rkhunter \
    chkrootkit \
    lynis \
    auditd \
    apparmor

# Configure fail2ban
sudo tee /etc/fail2ban/jail.local > /dev/null << 'EOF'
[DEFAULT]
bantime = 3600
findtime = 600
maxretry = 3

[sshd]
enabled = true
port = ssh
logpath = /var/log/auth.log

[yatebts]
enabled = true
port = 80,443
logpath = /var/log/yatebts/access.log
maxretry = 5
EOF

# Configure UFW firewall
sudo ufw default deny incoming
sudo ufw default allow outgoing
sudo ufw allow ssh
sudo ufw allow 80/tcp
sudo ufw allow 443/tcp
sudo ufw allow 5060/udp
sudo ufw allow 5061/tcp
sudo ufw enable

# Set up file integrity monitoring
sudo aide --init
sudo mv /var/lib/aide/aide.db.new /var/lib/aide/aide.db

# Configure audit logging
sudo tee -a /etc/audit/audit.rules > /dev/null << 'EOF'
# Monitor system calls
-a always,exit -F arch=b64 -S adjtimex -S settimeofday -k time-change
-a always,exit -F arch=b32 -S adjtimex -S settimeofday -S stime -k time-change
-a always,exit -F arch=b64 -S clock_settime -k time-change
-a always,exit -F arch=b32 -S clock_settime -k time-change

# Monitor file access
-w /etc/passwd -p wa -k identity
-w /etc/group -p wa -k identity
-w /etc/shadow -p wa -k identity
-w /etc/sudoers -p wa -k identity

# Monitor network configuration
-w /etc/network/ -p wa -k network-config
-w /etc/hosts -p wa -k network-config
-w /etc/hostname -p wa -k network-config
EOF

echo "Advanced security features implemented successfully"

#!/bin/bash
# Performance Optimization Implementation

echo "Implementing performance optimizations..."

# Kernel parameter optimization
sudo tee -a /etc/sysctl.conf > /dev/null << 'EOF'
# Network optimization
net.core.rmem_max = 134217728
net.core.wmem_max = 134217728
net.core.rmem_default = 65536
net.core.wmem_default = 65536
net.core.netdev_max_backlog = 5000
net.core.somaxconn = 65535

# TCP optimization
net.ipv4.tcp_rmem = 4096 65536 134217728
net.ipv4.tcp_wmem = 4096 65536 134217728
net.ipv4.tcp_congestion_control = bbr
net.ipv4.tcp_slow_start_after_idle = 0
net.ipv4.tcp_tw_reuse = 1

# Memory optimization
vm.swappiness = 10
vm.dirty_ratio = 15
vm.dirty_background_ratio = 5
vm.vfs_cache_pressure = 50

# File system optimization
fs.file-max = 2097152
fs.nr_open = 1048576
EOF

# Apply kernel parameters
sudo sysctl -p

# Set CPU governor to performance
echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

# Optimize I/O scheduler
echo mq-deadline | sudo tee /sys/block/mmcblk0/queue/scheduler

# Install and configure monitoring tools
sudo apt install -y \
    htop \
    iotop \
    nethogs \
    iftop \
    nload \
    collectd \
    grafana \
    influxdb

# Configure collectd
sudo tee /etc/collectd/collectd.conf > /dev/null << 'EOF'
Hostname "rfs-portable-bts"
FQDNLookup true
BaseDir "/var/lib/collectd"
PluginDir "/usr/lib/collectd"
TypesDB "/usr/share/collectd/types.db"

LoadPlugin cpu
LoadPlugin memory
LoadPlugin disk
LoadPlugin network
LoadPlugin processes
LoadPlugin load
LoadPlugin uptime


    Server "127.0.0.1" "25826"



    Disk "/^mmcblk/"
    IgnoreSelected false

EOF

# Start monitoring services
sudo systemctl enable collectd
sudo systemctl start collectd

echo "Performance optimization completed successfully"

#!/bin/bash
# Cloud Integration Implementation

echo "Setting up cloud integrations..."

# Install AWS CLI
curl "https://awscli.amazonaws.com/awscli-exe-linux-aarch64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install

# Install Azure CLI
curl -sL https://aka.ms/InstallAzureCLIDeb | sudo bash

# Install Google Cloud SDK
echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] https://packages.cloud.google.com/apt cloud-sdk main" | sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list
curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key --keyring /usr/share/keyrings/cloud.google.gpg add -
sudo apt update && sudo apt install -y google-cloud-sdk

# Install Docker
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker $USER

# Install Kubernetes tools
curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/arm64/kubectl"
sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl

# Install Helm
curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

# Configure cloud monitoring
sudo tee /etc/systemd/system/cloud-monitor.service > /dev/null << 'EOF'
[Unit]
Description=Cloud Monitoring Service
After=network.target

[Service]
Type=simple
User=yatebts
WorkingDirectory=/opt/yatebts
ExecStart=/usr/local/bin/cloud-monitor.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

# Create cloud monitoring script
sudo tee /usr/local/bin/cloud-monitor.py > /dev/null << 'EOF'
#!/usr/bin/env python3
import json
import time
import subprocess
import boto3
from azure.identity import DefaultAzureCredential
from azure.monitor.ingestion import LogsIngestionClient

def get_system_metrics():
    """Collect system metrics"""
    metrics = {}
    
    # CPU usage
    result = subprocess.run(['top', '-bn1'], capture_output=True, text=True)
    metrics['cpu_usage'] = float(result.stdout.split('\n')[2].split()[1])
    
    # Memory usage
    with open('/proc/meminfo', 'r') as f:
        meminfo = f.read()
    metrics['memory_usage'] = 100 - (int(meminfo.split('\n')[2].split()[1]) / int(meminfo.split('\n')[0].split()[1]) * 100)
    
    # Disk usage
    result = subprocess.run(['df', '-h', '/'], capture_output=True, text=True)
    metrics['disk_usage'] = result.stdout.split('\n')[1].split()[4]
    
    return metrics

def send_to_aws_cloudwatch(metrics):
    """Send metrics to AWS CloudWatch"""
    cloudwatch = boto3.client('cloudwatch')
    
    for metric_name, value in metrics.items():
        cloudwatch.put_metric_data(
            Namespace='RFS-Portable-BTS',
            MetricData=[
                {
                    'MetricName': metric_name,
                    'Value': value,
                    'Unit': 'Percent' if 'usage' in metric_name else 'None'
                }
            ]
        )

def send_to_azure_monitor(metrics):
    """Send metrics to Azure Monitor"""
    credential = DefaultAzureCredential()
    client = LogsIngestionClient(endpoint="https://your-workspace.ods.opinsights.azure.com", credential=credential)
    
    # Send metrics to Azure Monitor
    # Implementation depends on your Azure setup

if __name__ == "__main__":
    while True:
        metrics = get_system_metrics()
        
        # Send to cloud providers
        try:
            send_to_aws_cloudwatch(metrics)
            send_to_azure_monitor(metrics)
        except Exception as e:
            print(f"Error sending metrics: {e}")
        
        time.sleep(60)  # Send metrics every minute
EOF

sudo chmod +x /usr/local/bin/cloud-monitor.py
sudo systemctl enable cloud-monitor
sudo systemctl start cloud-monitor

echo "Cloud integration setup completed successfully"

#!/bin/bash
# AI and Machine Learning Implementation

echo "Setting up AI and ML capabilities..."

# Install Python and ML libraries
sudo apt update
sudo apt install -y \
    python3 \
    python3-pip \
    python3-venv \
    python3-dev \
    build-essential

# Create virtual environment
python3 -m venv /opt/yatebts/ai-env
source /opt/yatebts/ai-env/bin/activate

# Install ML libraries
pip install \
    numpy \
    pandas \
    scikit-learn \
    tensorflow \
    torch \
    opencv-python \
    matplotlib \
    seaborn \
    jupyter \
    flask \
    fastapi \
    uvicorn

# Create AI monitoring service
sudo tee /usr/local/bin/ai-monitor.py > /dev/null << 'EOF'
#!/usr/bin/env python3
import numpy as np
import pandas as pd
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import json
import time
import subprocess
import logging

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class AIMonitor:
    def __init__(self):
        self.model = IsolationForest(contamination=0.1, random_state=42)
        self.scaler = StandardScaler()
        self.is_trained = False
        self.data_buffer = []
        
    def collect_metrics(self):
        """Collect system metrics"""
        metrics = {}
        
        # CPU usage
        result = subprocess.run(['top', '-bn1'], capture_output=True, text=True)
        metrics['cpu_usage'] = float(result.stdout.split('\n')[2].split()[1])
        
        # Memory usage
        with open('/proc/meminfo', 'r') as f:
            meminfo = f.read()
        metrics['memory_usage'] = 100 - (int(meminfo.split('\n')[2].split()[1]) / int(meminfo.split('\n')[0].split()[1]) * 100)
        
        # Network activity
        result = subprocess.run(['cat', '/proc/net/dev'], capture_output=True, text=True)
        metrics['network_activity'] = sum([int(line.split()[1]) for line in result.stdout.split('\n')[2:] if line.strip()])
        
        # Process count
        result = subprocess.run(['ps', 'aux'], capture_output=True, text=True)
        metrics['process_count'] = len(result.stdout.split('\n')) - 1
        
        return metrics
    
    def train_model(self, data):
        """Train the anomaly detection model"""
        df = pd.DataFrame(data)
        X = self.scaler.fit_transform(df)
        self.model.fit(X)
        self.is_trained = True
        logger.info("AI model trained successfully")
    
    def detect_anomalies(self, metrics):
        """Detect anomalies in system metrics"""
        if not self.is_trained:
            return False, 0.0
        
        df = pd.DataFrame([metrics])
        X = self.scaler.transform(df)
        anomaly_score = self.model.decision_function(X)[0]
        is_anomaly = self.model.predict(X)[0] == -1
        
        return is_anomaly, anomaly_score
    
    def run(self):
        """Main monitoring loop"""
        logger.info("Starting AI monitoring service")
        
        while True:
            try:
                metrics = self.collect_metrics()
                self.data_buffer.append(metrics)
                
                # Train model with initial data
                if len(self.data_buffer) >= 100 and not self.is_trained:
                    self.train_model(self.data_buffer)
                
                # Detect anomalies
                if self.is_trained:
                    is_anomaly, score = self.detect_anomalies(metrics)
                    
                    if is_anomaly:
                        logger.warning(f"Anomaly detected! Score: {score:.2f}")
                        # Send alert or take action
                        self.handle_anomaly(metrics, score)
                
                # Keep buffer size manageable
                if len(self.data_buffer) > 1000:
                    self.data_buffer = self.data_buffer[-500:]
                
                time.sleep(30)  # Check every 30 seconds
                
            except Exception as e:
                logger.error(f"Error in AI monitoring: {e}")
                time.sleep(60)
    
    def handle_anomaly(self, metrics, score):
        """Handle detected anomalies"""
        # Log anomaly
        logger.warning(f"Anomaly detected: {metrics}, Score: {score}")
        
        # Send notification
        # Implement notification logic here
        
        # Take corrective action if needed
        if score < -0.5:  # High severity anomaly
            logger.critical("High severity anomaly detected, taking corrective action")
            # Implement corrective actions here

if __name__ == "__main__":
    monitor = AIMonitor()
    monitor.run()
EOF

sudo chmod +x /usr/local/bin/ai-monitor.py

# Create systemd service
sudo tee /etc/systemd/system/ai-monitor.service > /dev/null << 'EOF'
[Unit]
Description=AI Monitoring Service
After=network.target

[Service]
Type=simple
User=yatebts
WorkingDirectory=/opt/yatebts
ExecStart=/opt/yatebts/ai-env/bin/python /usr/local/bin/ai-monitor.py
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target
EOF

sudo systemctl enable ai-monitor
sudo systemctl start ai-monitor

echo "AI and ML implementation completed successfully"