Doctoral Dissertations

Keywords and Phrases

Anomaly; Data; Falsification; Security; Smartgrid; Vanet

Abstract

Smart city applications like smart grid, smart transportation, healthcare deal with very important data collected from IoT devices. False reporting of data consumption from device failures or by organized adversaries may have drastic consequences on the quality of operations. To deal with this, we propose a coarse grained and a fine grained anomaly based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to detect different attacks. We also built a trust scoring metric to filter out the malicious devices. Another challenging problem is injection of stealthy data falsification. To counter this, we propose a novel information-theory inspired data driven device anomaly classification framework to identify compromised devices launching low margins of stealthy data falsification attacks. The modifications such as expected self-similarity with weighted abundance shifts across various temporal scales, and diversity order are appropriately embedded in resulting diversity index score to classify the devices launching different attacks with high sensitivity compared to the existing works. Active learning, a semi-supervised classification approach is used to cluster the malicious and benign sensors depending on the score.

Adversarial machine learning (AML) is a technique that fools the machine learning models with the malicious input. The resulting performance of the existing machine learning models will drop when the adversary employs AML. Common types of AML techniques are evasion attacks and poisoning attacks. For this purpose, we proposed a Generative Adversarial Network (GAN) based solution to detect different kinds of evasion and poisoning attacks. Our proposed solutions are validated with the help of real-world smart metering datasets from Texas and Ireland, and smart transportation data from Nashville”--Abstract, page iii.

Advisor(s)

Das, Sajal K.

Committee Member(s)

Bhattacharjee, Shameek
Nadendla, V. Sriram Siddhardh
Luo, Tony Tie
Cen, Nan

Department(s)

Computer Science

Degree Name

Ph. D. in Computer Science

Comments

The author acknowledges the National Science Foundation for supporting this work with NSF grants CNS-1545050, CNS-1818942, and SaTC-2030624.

Publisher

Missouri University of Science and Technology

Publication Date

Fall 2021

Pagination

xii, 126 pages

Note about bibliography

Includes bibliographic references (pages 120-125).

Rights

© 2021 Venkata Praveen Kumar Madhavarapu, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11956

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