A hardware-embedded, delay-based PUF engine designed for use in cryptographic and authentication applications

Cryptographic and authentication applications in application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs), as well as codes for the activation of on-chip features, require the use of embedded secret information. The generation of secret bitstrings using physical unclonable functions, or PUFs, provides several distinct advantages over conventional methods, including the elimination of costly non-volatile memory, and the potential to increase the random bits available to applications. In this dissertation, a Hardware-Embedded Delay PUF (HELP) is proposed that is designed to leverage path delay variations that occur in the core logic macros of a chip to create random bitstrings. A thorough discussion is provided of the operational details of an embedded path timing structure called REBEL that is used by HELP to provide the timing functionality upon which HELP relies for the entropy source for the cryptographic quality of the bitstrings. Further details of the FPGA-based implementation used to prove the viability of the HELP PUF concept are included, along with a discussion of the evolution of the techniques employed in realizing the final PUF engine design. The bitstrings produced by a set of 30 FPGA boards are evaluated with regard to several statistical quality metrics including uniqueness, randomness, and stability. The stability characteristics of the bitstrings are evaluated by subjecting the FPGAs to commercial-grade temperature and power supply voltage variations. In particular, this work evaluates the reproducibility of the bitstrings generated at 0C, 25C, and 70C, and 10% of the rated supply voltage. A pair of error avoidance schemes are proposed and presented that provide significant improvements to the HELP PUF\u27s resiliency against bit-flip errors in the bitstrings

So we drove on towards death : release and mechanized violence in The Great Gatsby

Eric J. Leed postulates a theory of restraint and release as formative forces in the trenches of the European fronts. I examine F. Scott Fitzgerald’s 1925 novel The Great Gatsby for its portrayal of release that is tied to the presence of automobile “accidents.” These events emblematize the suppressed memory of mechanized violence. My first chapter addresses Gatsby and his fantasy of release in pursuing Daisy and the implications of release for morality in the post-war world. My second chapter is concerned with Tom Buchanan, whose traditional ideas about “civilization” are juxtaposed with the mechanistic violence of his defense of those ideals. My third chapter explores the relationships the novel’s characters possess to the automobile as a character, and further, how automobility enables release. My conclusion will synthesize the evidence in Fitzgerald’s novel to make observations about the disconnect, forged out of a new age of mechanized warfare, between individuals and their actions

Rule-based Out-Of-Distribution Detection

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. Results are available via open source and open data at the following link: https://github.com/giacomo97cnr/Rule-based-ODD

Enabling individually entrusted routing security for open and decentralized community networks

Routing in open and decentralized networks relies on cooperation. However, the participation of unknown nodes and node administrators pursuing heterogeneous trust and security goals is a challenge. Community-mesh networks are good examples of such environments due to their open structure, decentralized management, and ownership. As a result, existing community networks are vulnerable to various attacks and are seriously challenged by the obligation to find consensus on the trustability of participants within an increasing user size and diversity. We propose a practical and novel solution enabling a secured but decentralized trust management. This work presents the design and analysis of securely-entrusted multi-topology routing (SEMTOR), a set of routing-protocol mechanisms that enable the cryptographically secured negotiation and establishment of concurrent and individually trusted routing topologies for infrastructure-less networks without relying on any central management. The proposed mechanisms have been implemented, tested, and evaluated for their correctness and performance to exclude non-trusted nodes from the network. Respective safety and liveness properties that are guaranteed by our protocol have been identified and proven with formal reasoning. Benchmarking results, based on our implementation as part of the BMX7 routing protocol and tested on real and minimal (OpenWRT, 10 Euro) routers, qualify the behaviour, performance, and scalability of our approach, supporting networks with hundreds of nodes despite the use of strong asymmetric cryptography.Peer ReviewedPostprint (author's final draft

Technology and Biological Weapons: Future Threats

Ye

Rule-based Out-Of-Distribution Detection

Out-of-distribution detection is one of the most critical issue in the deployment of machine learning. The data analyst must assure that data in operation should be compliant with the training phase as well as understand if the environment has changed in a way that autonomous decisions would not be safe anymore. The method of the paper is based on eXplainable Artificial Intelligence (XAI); it takes into account different metrics to identify any resemblance between in-distribution and out of, as seen by the XAI model. The approach is non-parametric and distributional assumption free. The validation over complex scenarios (predictive maintenance, vehicle platooning, covert channels in cybersecurity) corroborates both precision in detection and evaluation of training-operation conditions proximity. Results are available via open source and open data at the following link: https://github.com/giacomo97cnr/Rule-based-ODD