Date of Award

2020

Document Type

Thesis

Degree Name

M. S.

Department

Computer Science

First Advisor

Rinku Dewri

Second Advisor

David Wenzhong Gao

Third Advisor

Anneliese Andrews

Abstract

An increased usage in IoT devices across the globe has posed a threat to the power grid. When an attacker has access to multiple IoT devices within the same geographical location, they can possibly disrupt the power grid by regulating a botnet of high-wattage IoT devices. Based on the time and situation of the attack, an adversary needs access to a fixed number of IoT devices to synchronously switch on/off all of them, resulting in an imbalance between the supply and demand. When the frequency of the power generators drops below a threshold value, it can lead to the generators tripping and potentially failing. Attacks such as these can cause an imbalance in the grid frequency, line failures and cascades, can disrupt a black start or increase the operating cost. The challenge lies in early detection of abnormal demand peaks in a large section of the power grid from the power operator’s side, as it only takes seconds to cause a generator failure before any action could be taken.

Anomaly detection comes handy to flag the power operator of an anomalous behavior while such an attack is taking place. However, it is difficult to detect anomalies especially when such attacks are taking place obscurely and for prolonged time periods. With this motive, we compare different anomaly detection systems in terms of detecting these anomalies collectively. We generate attack data using real-world power consumption data across multiple apartments to assess the performance of various prediction-based detection techniques as well as commercial detection applications and observe the cases when the attacks were not detected. Using static thresholds for the detection process does not reliably detect attacks when they are performed in different times of the year and also lets the attacker exploit the system to create the attack obscurely. To combat the effects of using static thresholds, we propose a novel dynamic thresholding mechanism, which improves the attack detection reaching up to 100% detection rate, when used with prediction-based anomaly score techniques.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Provenance

Received from ProQuest

Rights holder

Srinidhi Madabhushi

File size

103 p.

File format

application/pdf

Language

en

Discipline

Computer science

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