Graduation Year

2019

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Electrical Engineering

Major Professor

Nasir Ghani, Ph.D.

Co-Major Professor

Elias Bou-Harb, Ph.D.

Committee Member

Ismail Uysal, Ph.D.

Committee Member

Zhuo Lu, Ph.D.

Committee Member

Srinivas Katkoori, Ph.D.

Keywords

Darknet, Deep Learning, Ensemble Learners, Network Statistics

Abstract

The recent advancements in computing and sensor technologies, coupled with improvements in embedded system design methodologies, have resulted in the novel paradigm called the Internet of Things (IoT). IoT is essentially a network of small embedded devices enabled with sensing capabilities that can interact with multiple entities to relay information about their environments. This sensing information can also be stored in the cloud for further analysis, thereby reducing storage requirements on the devices themselves. The above factors, coupled with the ever increasing needs of modern society to stay connected at all times, has resulted in IoT technology penetrating all facets of modern life. In fact IoT systems are already seeing widespread applications across multiple industries such as transport, utility, manufacturing, healthcare, home automation, etc.

Although the above developments promise tremendous benefits in terms of productivity and efficiency, they also bring forth a plethora of security challenges. Namely, the current design philosophy of IoT devices, which focuses more on rapid prototyping and usability, results in security often being an afterthought. Furthermore, one needs to remember that unlike traditional computing systems, these devices operate under the assumption of tight resource constraints. As such this makes IoT devices a lucrative target for exploitation by adversaries. This inherent flaw of IoT setups has manifested itself in the form of various distributed denial of service (DDoS) attacks that have achieved massive throughputs without the need for techniques such as amplification, etc. Furthermore, once exploited, an IoT device can also function as a pivot point for adversaries to move laterally across the network and exploit other, potentially more valuable, systems and services. Finally, vulnerable IoT devices operating in industrial control systems and other critical infrastructure setups can cause sizable loss of property and in some cases even lives, a very sobering fact.

In light of the above, this dissertation research presents several novel strategies for identifying known and zero-day attacks against IoT devices, as well as identifying infected IoT devices present inside a network along with some mitigation strategies. To this end, network telescopes are leveraged to generate Internet-scale notions of maliciousness in conjunction with signatures that can be used to identify such devices in a network. This strategy is further extended by developing a taxonomy-based methodology which is capable of categorizing unsolicited IoT behavior by leveraging machine learning (ML) techniques, such as ensemble learners, to identify similar threats in near-real time. Furthermore, to overcome the challenge of insufficient (malicious) training data within the IoT realm, a generative adversarial network (GAN) based framework is also developed to identify known and unseen attacks on IoT devices. Finally, a software defined networking (SDN) based solution is proposed to mitigate threats from unsolicited IoT devices.

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