Click here to download base paper

Click here to download base paper

Machine Learning in Detecting Network Security of Edge Computing would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.

Edge computing refers to the paradigm where computation and data storage are performed closer to the source of data generation rather than relying solely on centralized cloud servers. This proximity to data sources offers advantages in terms of reduced latency, increased bandwidth efficiency, and enhanced privacy. However, it also introduces unique challenges and considerations for network security. Here are some key aspects to consider for securing edge computing systems:

  1. Physical Security:
    • Ensure physical security of edge devices to prevent unauthorized access or tampering.
    • Implement measures such as secure enclosures, surveillance, and access controls.
  2. Device Authentication and Authorization:
    • Use strong authentication mechanisms to ensure that only authorized devices can connect to the edge computing network.
    • Implement proper authorization controls to define what actions each device is allowed to perform.
  3. Data Encryption:
    • Employ encryption mechanisms to protect data both in transit and at rest.
    • Use protocols such as TLS/SSL for securing communication channels.
    • Encrypt sensitive data stored on edge devices to prevent unauthorized access.
  4. Network Segmentation:
    • Implement network segmentation to isolate different components of the edge computing system.
    • This helps contain potential security breaches and limit lateral movement of attackers.
  5. Firewalls and Intrusion Detection/Prevention Systems:
    • Deploy firewalls to monitor and control network traffic between devices in the edge network.
    • Use intrusion detection and prevention systems to identify and mitigate potential security threats.
  6. Update and Patch Management:
    • Regularly update and patch both operating systems and software on edge devices to address known vulnerabilities.
    • Establish a robust update management process to ensure timely application of security patches.
  7. Security Monitoring and Logging:
    • Machine Learning in Detecting Network Security of Edge Computing
    • Implement comprehensive monitoring tools to detect abnormal behavior or potential security incidents.
    • Maintain detailed logs for forensic analysis in the event of a security breach.
  8. Edge-to-Cloud Security Integration:
    • Ensure a secure communication channel between edge devices and the central cloud infrastructure.
    • Implement secure protocols for data exchange between edge and cloud components.
  9. Container Security:
    • If edge computing involves containerization (e.g., Docker), implement security measures for container isolation and secure deployment.
  10. Zero Trust Security Model:
    • Adopt a zero-trust security model, where every device, user, and application is treated as untrusted until proven otherwise.
    • Verify and authenticate all communications and access attempts.
  11. Incident Response and Disaster Recovery:
    • Develop and regularly test an incident response plan to efficiently respond to security incidents.
    • Establish a disaster recovery plan to minimize downtime and data loss in case of a security breach.
  12. Regulatory Compliance:
    • Be aware of and comply with relevant data protection and privacy regulations applicable to your industry and location.

Securing an edge computing system requires a holistic approach, considering both physical and digital aspects. It’s crucial to continually assess and adapt security measures as the technology landscape evolves and new threats emerge. Regular security audits and penetration testing can help identify vulnerabilities and weaknesses in the system

Architecture Diagram

Use of Machine Learning in Detecting Network Security of Edge Computing System
Leave a Comment


No comments yet. Why don’t you start the discussion?

Leave a Reply

Your email address will not be published. Required fields are marked *