Computer Science and Engineering, Department of

 

First Advisor

Hamid Bagheri

Second Advisor

Qiben Yan

Date of this Version

Fall 12-4-2019

Document Type

Article

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Engineering (Computer Engineering-Computer Science), Under the Supervision of Professors Hamid Bagheri and Qiben Yan. Lincoln, Nebraska: December, 2019

Copyright 2019 Mohannad Alhanahnah

Abstract

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems.

This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms.

Advisers: Hamid Bagheri and Qiben Yan

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