Division of Computer and Network Systems (CNS)

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Visible to the public SaTC: CORE: Small: Collaborative: CPS ACTS: Orchestrating CPS with Action blocks

The integration of computation, communication and sensing technologies into our transportation, power grid, healthcare, and manufacturing systems presents unprecedented challenges in ensuring the security and safety of these systems. Cyber attacks on such systems cross from the cyber realm into the physical world, and we must deal with new attack trends that may cause vehicles to veer off the road, manipulate devices responsible for power generation, distribution and consumption, and exploit robotic/drone systems for malicious activities.

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Visible to the public SaTC: CORE: Small: Design of Efficient, Horizontally-Scaling, and Strongly Anonymous Communication Networks

Anonymous communication is an important aspect of freedom of speech. In many cases, anonymity remains the most important defense for persons expressing unpopular or prohibited opinions, from protest organizers who are fighting against repressive governments to whistleblowers who report sensitive news against powerful entities. Owing to its importance, there have been many systems that were designed to protect users' anonymity in the past several decades.

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Visible to the public SaTC: CORE: Small: Enhanced Security and Reliability for Embedded Control Systems

Today, embedded devices are ubiquitous. These devices are inherently networked, which exposes them to malware attacks. Windows devices remain the most prominent targets of malware attacks to date. But this playing field is quickly changing, as demonstrated with firmware attacks to private access points or closed-circuit television cameras. Other intrusions to industrial and governmental infrastructure have been reported in the power grid, for industrial control and automotive systems, even in small devices in private homes that are networked, often referred to as the Internet-of-Things.

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Visible to the public SaTC: STARSS: Small: Analysis of Security and Countermeasures for Split Manufacturing of Integrated Circuits

Integrated circuit fabrication has spread across the globe, with over 90% of the world's fabrication capacity controlled by non-US companies. This project is on studying the security of chip fabrication by an untrusted foundry. The fabrication technique, known as split manufacturing, is based on partial sharing of the chip design information with the untrusted foundry in order to protect the intellectual property of the chip. With this technique, only a challenging portion of the chip is manufactured at the untrusted foundry.

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Visible to the public CRII: SaTC CPS: RUI: Cyber-Physical System Security in Implantable Insulin Injection Systems

Increasingly medical devices are dependent on software and the wireless channel for their operations, which also pose new vulnerabilities to their safe, dependable, and trustworthy operations. Medical devices such as implantable insulin pumps, which are in wide use today, continuously monitor and manage a patient's diabetes without the need for frequent daily patient interventions. These devices, not originally designed against cyber security threats, must now mitigate these threats.

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Visible to the public SaTC: CORE: Small: verifying security for data non-interference

Many security problems today stem from bugs in software. Although there has been significant effort in reducing bugs through better testing, fuzzing, model checking, and so on, subtle bugs remain and continue to be exploited. This proposal explores the use of formal verification to prove security of a file system implementation along with an example application in the form of a mail server. Machine-checked verification is a powerful approach that can eliminate a large class of bugs in software by proving that an implementation meets a precise specification.

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Visible to the public SaTC: CORE: Small: Collaborative: Evaluating Performance and Security of Executable Steganography for Surreptitious Programs

Intellectual property protection of software remains a priority for the commercial sector because counterfeiting and piracy erode profits and market share, ultimately causing impacts on companies, consumers and governments. Watermarking for proving digital ownership and obfuscation for hindering adversarial reverse engineering are currently used to provide some level of deterrence against this. This project will investigate novel methods of software protection, hiding executable programs within other executable code.

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Visible to the public SaTC: CORE: Small: Collaborative: Evaluating Performance and Security of Executable Steganography for Surreptitious Programs

Intellectual property protection of software remains a priority for the commercial sector because counterfeiting and piracy erode profits and market share, ultimately causing impacts on companies, consumers and governments. Watermarking for proving digital ownership and obfuscation for hindering adversarial reverse engineering are currently used to provide some level of deterrence against this. This project will investigate novel methods of software protection, hiding executable programs within other executable code.

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Visible to the public EAGER: Collaborative: Towards Understanding the Attack Vector of Privacy Technologies

Advances in privacy-enhancing technologies, including cryptographic mechanisms, standardized security protocols, and infrastructure, significantly improved privacy and had a significant impact on society by protecting users. At the same time, the success of such infrastructure has attracted abuse from illegal activities, including sophisticated botnets and ransomware, and has become a marketplace for drugs and contraband; botnets rose to be a major tool for cybercrime and their developers proved to be highly resourceful.

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Visible to the public SaTC: CORE: Frontier: Collaborative: End-to-End Trustworthiness of Machine-Learning Systems

This frontier project establishes the Center for Trustworthy Machine Learning (CTML), a large-scale, multi-institution, multi-disciplinary effort whose goal is to develop scientific understanding of the risks inherent to machine learning, and to develop the tools, metrics, and methods to manage and mitigate them. The center is led by a cross-disciplinary team developing unified theory, algorithms and empirical methods within complex and ever-evolving ML approaches, application domains, and environments.