Category: Publications

Home / Publications
Post

Efficient Detection for Malicious and Random Errors in Additive Encrypted Computation

Nektarios Georgios Tsoutsos and Michail Maniatakos Although data confidentiality is the primary security objective in additive encrypted computation applications, such as the aggregation of encrypted votes in electronic elections, ensuring the trustworthiness of data is equally important. And yet, integrity protections are generally orthogonal to additive homomorphic encryption, which enables efficient encrypted computation, due to the...

Post

Cyber– Physical Systems Security and Privacy

Guest Editors: Michail Maniatakos, Ramesh Karri and Alvaro A. Cardenas During the past decade, several catch-phrases have been used to emphasize the increasing importance of cyber–physical systems (CPS) in our everyday life: Internet-of-Things, Internet-of-Everything, Smart-Cities, Smart-X, Intelligent-X, etc. All such systems, in their core, consist of networked computing (cyber) devices continuously interacting with the physical...

Post

Throughput maximization of large-scale secondary networks over licensed and unlicensed spectra

Manjesh K. Hanawal, Yezekael Hayel and Quanyan Zhu. Throughput of a mobile ad hoc network (MANET) operating on an unlicensed spectrum can increase if nodes can also transmit on a (shared) licensed spectrum. However, the transmissions on the licensed spectrum has to be limited to avoid degradation of quality of service (QoS) to primary users (PUs). We...

Post

IoT-enabled Distributed Cyber-attacks on Transmission and Distribution Grids.

Yury Dvorkin and Siddharth Garg The Internet of things (IoT) will make it possible to interconnect and simultaneously control distributed electrical loads. Various technical and regulatory concerns have been raised that IoT-operated loads are being deployed without appropriately considering and systematically addressing potential cyber-security challenges. Hence, one can envision a hypothetical scenario when an ensemble...

Post

ObfusCADe: Obfuscating Additive Manufacturing CAD Models Against Counterfeiting: Invited

Nikhil Gupta, Fei Chen,Nektarios Georgios Tsoutsos and Michail Maniatakos As additive manufacturing (AM) becomes more pervasive, its supply chains shift towards distributed business models that heavily rely on cloud resources. Despite its countless benefits, this paradigm raises significant concerns about the trustworthiness of the globalized process, as there exist several classes of cybersecurity attacks that...

Post

Security as a Service for Cloud-Enabled Internet of Controlled Things under Advanced Persistent Threats: A Contract Design Approach

Juntao Chen and Quanyan Zhu In this paper, we aim to establish a holistic framework that integrates the cyber-physical layers of a cloud-enabled Internet of Controlled Things (IoCT) through the lens of contract theory. At the physical layer, the device uses cloud services to operate the system. The quality of cloud services is unknown to...

Post

TTLock: Tenacious and traceless logic locking

Muhammad Yasin, Bodhisatwa Mazumdar, Jeyavijayan J V Rajendran and Ozgur Sinanoglu Logic locking is an intellectual property (IP) protection technique that prevents IP piracy, reverse engineering and overbuilding attacks by the untrusted foundry or endusers. Existing logic locking techniques are all vulnerable to various attacks, such as sensitization, key-pruning and signal skew analysis enabled removal...

Post

On Mitigation of Side-Channel Attacks in 3D ICs: Decorrelating Thermal Patterns from Power and Activity

Johann Knechtel and Ozgur Sinanoglu Various side-channel attacks (SCAs) on ICs have been successfully demonstrated and also mitigated to some degree. In the context of 3D ICs, however, prior art has mainly focused on efficient implementations of classical SCA countermeasures. That is, SCAs tailored for up-and-coming 3D ICs have been overlooked so far. In this...

Post

Distributed Transfer Linear Support Vector Machines

Rui Zhang and Quanyan Zhu Transfer learning has been developed to improve the performances of different but related tasks in machine learning. However, such processes become less efficient with the increase of the size of training data and the number of tasks. Moreover, privacy can be violated as some tasks may contain sensitive and private...