A Data Quarantine Model to Secure Data in Edge Computing

TL;DR

This paper introduces a data quarantine model for edge computing that detects and isolates intruders to protect data integrity, utilizing machine learning for efficient dimensionality reduction and quarantine accuracy.

Contribution

It proposes a novel data quarantine framework for edge computing that employs LDA for intrusion detection and data sanitization, enhancing security against data integrity attacks.

Findings

LDA achieves 72.83% quarantine accuracy.

LDA training time is 0.9 seconds, outperforming other methods.

The model effectively isolates intruders and maintains data integrity.

Abstract

Edge computing provides an agile data processing platform for latency-sensitive and communication-intensive applications through a decentralized cloud and geographically distributed edge nodes. Gaining centralized control over the edge nodes can be challenging due to security issues and threats. Among several security issues, data integrity attacks can lead to inconsistent data and intrude edge data analytics. Further intensification of the attack makes it challenging to mitigate and identify the root cause. Therefore, this paper proposes a new concept of data quarantine model to mitigate data integrity attacks by quarantining intruders. The efficient security solutions in cloud, ad-hoc networks, and computer systems using quarantine have motivated adopting it in edge computing. The data acquisition edge nodes identify the intruders and quarantine all the suspected devices through dimensionality reduction. During quarantine, the proposed concept builds the reputation scores to determine the falsely identified legitimate devices and sanitize their affected data to regain data integrity. As a preliminary investigation, this work identifies an appropriate machine learning method, Linear Discriminant Analysis (LDA), for dimensionality reduction. The LDA results in 72.83% quarantine accuracy and 0.9 seconds training time, which is efficient than other state-of-the-art methods. In future, this would be implemented and validated with ground truth data.