  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.
