Download or read book On the Learnability of Physically Unclonable Functions written by Fatemeh Ganji and published by Springer. This book was released on 2018-03-24 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the issue of Machine Learning (ML) attacks on Integrated Circuits through Physical Unclonable Functions (PUFs). It provides the mathematical proofs of the vulnerability of various PUF families, including Arbiter, XOR Arbiter, ring-oscillator, and bistable ring PUFs, to ML attacks. To achieve this goal, it develops a generic framework for the assessment of these PUFs based on two main approaches. First, with regard to the inherent physical characteristics, it establishes fit-for-purpose mathematical representations of the PUFs mentioned above, which adequately reflect the physical behavior of these primitives. To this end, notions and formalizations that are already familiar to the ML theory world are reintroduced in order to give a better understanding of why, how, and to what extent ML attacks against PUFs can be feasible in practice. Second, the book explores polynomial time ML algorithms, which can learn the PUFs under the appropriate representation. More importantly, in contrast to previous ML approaches, the framework presented here ensures not only the accuracy of the model mimicking the behavior of the PUF, but also the delivery of such a model. Besides off-the-shelf ML algorithms, the book applies a set of algorithms hailing from the field of property testing, which can help to evaluate the security of PUFs. They serve as a “toolbox”, from which PUF designers and manufacturers can choose the indicators most relevant for their requirements. Last but not least, on the basis of learning theory concepts, the book explicitly states that the PUF families cannot be considered as an ultimate solution to the problem of insecure ICs. As such, it provides essential insights into both academic research on and the design and manufacturing of PUFs.

Download or read book Physically Unclonable Functions written by Basel Halak and published by Springer. This book was released on 2018-04-18 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the design principles of physically unclonable functions (PUFs) and how these can be employed in hardware-based security applications, in particular, the book provides readers with a comprehensive overview of security threats and existing countermeasures. This book has many features that make it a unique source for students, engineers and educators, including more than 80 problems and worked exercises, in addition to, approximately 200 references, which give extensive direction for further reading.

Download or read book Towards Hardware Intrinsic Security written by Ahmad-Reza Sadeghi and published by Springer Science & Business Media. This book was released on 2010-11-03 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware-intrinsic security is a young field dealing with secure secret key storage. By generating the secret keys from the intrinsic properties of the silicon, e.g., from intrinsic Physical Unclonable Functions (PUFs), no permanent secret key storage is required anymore, and the key is only present in the device for a minimal amount of time. The field is extending to hardware-based security primitives and protocols such as block ciphers and stream ciphers entangled with the hardware, thus improving IC security. While at the application level there is a growing interest in hardware security for RFID systems and the necessary accompanying system architectures. This book brings together contributions from researchers and practitioners in academia and industry, an interdisciplinary group with backgrounds in physics, mathematics, cryptography, coding theory and processor theory. It will serve as important background material for students and practitioners, and will stimulate much further research and development.

Download or read book Security Privacy and Trust in Modern Data Management written by Milan Petkovic and published by Springer Science & Business Media. This book was released on 2007-06-12 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: The vision of ubiquitous computing and ambient intelligence describes a world of technology which is present anywhere, anytime in the form of smart, sensible devices that communicate with each other and provide personalized services. However, open interconnected systems are much more vulnerable to attacks and unauthorized data access. In the context of this threat, this book provides a comprehensive guide to security and privacy and trust in data management.

Download or read book Statistical Trend Analysis of Physically Unclonable Functions written by Behrouz Zolfaghari and published by CRC Press. This book was released on 2021-03-25 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Physically Unclonable Functions (PUFs) translate unavoidable variations in certain parameters of materials, waves, or devices into random and unique signals. They have found many applications in the Internet of Things (IoT), authentication systems, FPGA industry, several other areas in communications and related technologies, and many commercial products. Statistical Trend Analysis of Physically Unclonable Functions first presents a review on cryptographic hardware and hardware-assisted cryptography. The review highlights PUF as a mega trend in research on cryptographic hardware design. Afterwards, the authors present a combined survey and research work on PUFs using a systematic approach. As part of the survey aspect, a state-of-the-art analysis is presented as well as a taxonomy on PUFs, a life cycle, and an established ecosystem for the technology. In another part of the survey, the evolutionary history of PUFs is examined, and strategies for further research in this area are suggested. In the research side, this book presents a novel approach for trend analysis that can be applied to any technology or research area. In this method, a text mining tool is used which extracts 1020 keywords from the titles of the sample papers. Then, a classifying tool classifies the keywords into 295 meaningful research topics. The popularity of each topic is then numerically measured and analyzed over the course of time through a statistical analysis on the number of research papers related to the topic as well as the number of their citations. The authors identify the most popular topics in four different domains; over the history of PUFs, during the recent years, in top conferences, and in top journals. The results are used to present an evolution study as well as a trend analysis and develop a roadmap for future research in this area. This method gives an automatic popularity-based statistical trend analysis which eliminates the need for passing personal judgments about the direction of trends, and provides concrete evidence to the future direction of research on PUFs. Another advantage of this method is the possibility of studying a whole lot of existing research works (more than 700 in this book). This book will appeal to researchers in text mining, cryptography, hardware security, and IoT.

Download or read book Physically Unclonable Functions written by Roel Maes and published by Springer Science & Business Media. This book was released on 2013-11-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Physically unclonable functions (PUFs) are innovative physical security primitives that produce unclonable and inherent instance-specific measurements of physical objects; in many ways they are the inanimate equivalent of biometrics for human beings. Since they are able to securely generate and store secrets, they allow us to bootstrap the physical implementation of an information security system. In this book the author discusses PUFs in all their facets: the multitude of their physical constructions, the algorithmic and physical properties which describe them, and the techniques required to deploy them in security applications. The author first presents an extensive overview and classification of PUF constructions, with a focus on so-called intrinsic PUFs. He identifies subclasses, implementation properties, and design techniques used to amplify submicroscopic physical distinctions into observable digital response vectors. He lists the useful qualities attributed to PUFs and captures them in descriptive definitions, identifying the truly PUF-defining properties in the process, and he also presents the details of a formal framework for deploying PUFs and similar physical primitives in cryptographic reductions. The author then describes a silicon test platform carrying different intrinsic PUF structures which was used to objectively compare their reliability, uniqueness, and unpredictability based on experimental data. In the final chapters, the author explains techniques for PUF-based entity identification, entity authentication, and secure key generation. He proposes practical schemes that implement these techniques, and derives and calculates measures for assessing different PUF constructions in these applications based on the quality of their response statistics. Finally, he presents a fully functional prototype implementation of a PUF-based cryptographic key generator, demonstrating the full benefit of using PUFs and the efficiency of the processing techniques described. This is a suitable introduction and reference for security researchers and engineers, and graduate students in information security and cryptography.

Download or read book Deep Learning for Computational Problems in Hardware Security written by Pranesh Santikellur and published by Springer Nature. This book was released on 2022-09-15 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book discusses a broad overview of traditional machine learning methods and state-of-the-art deep learning practices for hardware security applications, in particular the techniques of launching potent "modeling attacks" on Physically Unclonable Function (PUF) circuits, which are promising hardware security primitives. The volume is self-contained and includes a comprehensive background on PUF circuits, and the necessary mathematical foundation of traditional and advanced machine learning techniques such as support vector machines, logistic regression, neural networks, and deep learning. This book can be used as a self-learning resource for researchers and practitioners of hardware security, and will also be suitable for graduate-level courses on hardware security and application of machine learning in hardware security. A stand-out feature of the book is the availability of reference software code and datasets to replicate the experiments described in the book.

Download or read book Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity written by Lobo, Victor and published by IGI Global. This book was released on 2022-06-24 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: The growth of innovative cyber threats, many based on metamorphosing techniques, has led to security breaches and the exposure of critical information in sites that were thought to be impenetrable. The consequences of these hacking actions were, inevitably, privacy violation, data corruption, or information leaking. Machine learning and data mining techniques have significant applications in the domains of privacy protection and cybersecurity, including intrusion detection, authentication, and website defacement detection, that can help to combat these breaches. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity provides machine and deep learning methods for analysis and characterization of events regarding privacy and anomaly detection as well as for establishing predictive models for cyber attacks or privacy violations. It provides case studies of the use of these techniques and discusses the expected future developments on privacy and cybersecurity applications. Covering topics such as behavior-based authentication, machine learning attacks, and privacy preservation, this book is a crucial resource for IT specialists, computer engineers, industry professionals, privacy specialists, security professionals, consultants, researchers, academicians, and students and educators of higher education.

Download or read book Cryptographic Hardware and Embedded Systems CHES 2016 written by Benedikt Gierlichs and published by Springer. This book was released on 2016-08-03 with total page 649 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 18th International Conference on Cryptographic Hardware and Embedded Systems, CHES 2016, held in Santa Barbara, CA, USA, in August 2016. The 30 full papers presented in this volume were carefully reviewed and selected from 148 submissions. They were organized in topical sections named: side channel analysis; automotive security; invasive attacks; side channel countermeasures; new directions; software implementations; cache attacks; physical unclonable functions; hardware implementations; and fault attacks.

Download or read book Physically Unclonable Functions PUFs written by Christian Wachsmann and published by Morgan & Claypool Publishers. This book was released on 2014-12-01 with total page 93 pages. Available in PDF, EPUB and Kindle. Book excerpt: Today, embedded systems are used in many security-critical applications, from access control, electronic tickets, sensors, and smart devices (e.g., wearables) to automotive applications and critical infrastructures. These systems are increasingly used to produce and process both security-critical and privacy-sensitive data, which bear many security and privacy risks. Establishing trust in the underlying devices and making them resistant to software and hardware attacks is a fundamental requirement in many applications and a challenging, yet unsolved, task. Solutions solely based on software can never ensure their own integrity and trustworthiness while resource-constraints and economic factors often prevent the integration of sophisticated security hardware and cryptographic co-processors. In this context, Physically Unclonable Functions (PUFs) are an emerging and promising technology to establish trust in embedded systems with minimal hardware requirements. This book explores the design of trusted embedded systems based on PUFs. Specifically, it focuses on the integration of PUFs into secure and efficient cryptographic protocols that are suitable for a variety of embedded systems. It exemplarily discusses how PUFs can be integrated into lightweight device authentication and attestation schemes, which are popular and highly relevant applications of PUFs in practice. For the integration of PUFs into secure cryptographic systems, it is essential to have a clear view of their properties. This book gives an overview of different approaches to evaluate the properties of PUF implementations and presents the results of a large scale security analysis of different PUF types implemented in application-specific integrated circuits (ASICs). To analyze the security of PUF-based schemes as is common in modern cryptography, it is necessary to have a security framework for PUFs and PUF-based systems. In this book, we give a flavor of the formal modeling of PUFs that is in its beginning and that is still undergoing further refinement in current research. The objective of this book is to provide a comprehensive overview of the current state of secure PUF-based cryptographic system design and the related challenges and limitations. Table of Contents: Preface / Introduction / Basics of Physically Unclonable Functions / Attacks on PUFs and PUF-based Systems / Advanced PUF Concepts / PUF Implementations and Evaluation / PUF-based Cryptographic Protocols / Security Model for PUF-based Systems / Conclusion / Terms and Abbreviations / Bibliography / Authors' Biographies

Download or read book Machine Learning for Embedded System Security written by Basel Halak and published by Springer Nature. This book was released on 2022-04-22 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprehensively covers the state-of-the-art security applications of machine learning techniques. The first part explains the emerging solutions for anti-tamper design, IC Counterfeits detection and hardware Trojan identification. It also explains the latest development of deep-learning-based modeling attacks on physically unclonable functions and outlines the design principles of more resilient PUF architectures. The second discusses the use of machine learning to mitigate the risks of security attacks on cyber-physical systems, with a particular focus on power plants. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities.

Download or read book Crypto and AI written by Behrouz Zolfaghari and published by Springer Nature. This book was released on 2023-11-14 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book studies the intersection between cryptography and AI, highlighting the significant cross-impact and potential between the two technologies. The authors first study the individual ecosystems of cryptography and AI to show the omnipresence of each technology in the ecosystem of the other one. Next, they show how these technologies have come together in collaborative or adversarial ways. In the next section, the authors highlight the coevolution being formed between cryptography and AI. Throughout the book, the authors use evidence from state-of-the-art research to look ahead at the future of the crypto-AI dichotomy. The authors explain how they anticipate that quantum computing will join the dichotomy in near future, augmenting it to a trichotomy. They verify this through two case studies highlighting another scenario wherein crypto, AI and quantum converge. The authors study current trends in chaotic image encryption as well as information-theoretic cryptography and show how these trends lean towards quantum-inspired artificial intelligence (QiAI). After concluding the discussions, the authors suggest future research for interested researchers.

Download or read book Introduction to Hardware Security and Trust written by Mohammad Tehranipoor and published by Springer Science & Business Media. This book was released on 2011-09-22 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the foundations for understanding hardware security and trust, which have become major concerns for national security over the past decade. Coverage includes security and trust issues in all types of electronic devices and systems such as ASICs, COTS, FPGAs, microprocessors/DSPs, and embedded systems. This serves as an invaluable reference to the state-of-the-art research that is of critical significance to the security of, and trust in, modern society’s microelectronic-supported infrastructures.

Download or read book Embedded Machine Learning for Cyber Physical IoT and Edge Computing written by Sudeep Pasricha and published by Springer Nature. This book was released on 2023-11-01 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent advances towards the goal of enabling efficient implementation of machine learning models on resource-constrained systems, covering different application domains. The focus is on presenting interesting and new use cases of applying machine learning to innovative application domains, exploring the efficient hardware design of efficient machine learning accelerators, memory optimization techniques, illustrating model compression and neural architecture search techniques for energy-efficient and fast execution on resource-constrained hardware platforms, and understanding hardware-software codesign techniques for achieving even greater energy, reliability, and performance benefits.

Download or read book Machine Learning for Cybersecurity Cookbook written by Emmanuel Tsukerman and published by Packt Publishing Ltd. This book was released on 2019-11-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to apply modern AI to create powerful cybersecurity solutions for malware, pentesting, social engineering, data privacy, and intrusion detection Key FeaturesManage data of varying complexity to protect your system using the Python ecosystemApply ML to pentesting, malware, data privacy, intrusion detection system(IDS) and social engineeringAutomate your daily workflow by addressing various security challenges using the recipes covered in the bookBook Description Organizations today face a major threat in terms of cybersecurity, from malicious URLs to credential reuse, and having robust security systems can make all the difference. With this book, you'll learn how to use Python libraries such as TensorFlow and scikit-learn to implement the latest artificial intelligence (AI) techniques and handle challenges faced by cybersecurity researchers. You'll begin by exploring various machine learning (ML) techniques and tips for setting up a secure lab environment. Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. As you progress, you'll build self-learning, reliant systems to handle cybersecurity tasks such as identifying malicious URLs, spam email detection, intrusion detection, network protection, and tracking user and process behavior. Later, you'll apply generative adversarial networks (GANs) and autoencoders to advanced security tasks. Finally, you'll delve into secure and private AI to protect the privacy rights of consumers using your ML models. By the end of this book, you'll have the skills you need to tackle real-world problems faced in the cybersecurity domain using a recipe-based approach. What you will learnLearn how to build malware classifiers to detect suspicious activitiesApply ML to generate custom malware to pentest your securityUse ML algorithms with complex datasets to implement cybersecurity conceptsCreate neural networks to identify fake videos and imagesSecure your organization from one of the most popular threats – insider threatsDefend against zero-day threats by constructing an anomaly detection systemDetect web vulnerabilities effectively by combining Metasploit and MLUnderstand how to train a model without exposing the training dataWho this book is for This book is for cybersecurity professionals and security researchers who are looking to implement the latest machine learning techniques to boost computer security, and gain insights into securing an organization using red and blue team ML. This recipe-based book will also be useful for data scientists and machine learning developers who want to experiment with smart techniques in the cybersecurity domain. Working knowledge of Python programming and familiarity with cybersecurity fundamentals will help you get the most out of this book.

Download or read book Real Time Applications of Machine Learning in Cyber Physical Systems written by Easwaran, Balamurugan and published by IGI Global. This book was released on 2022-03-11 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technological advancements of recent decades have reshaped the way people socialize, work, learn, and ultimately live. The use of cyber-physical systems (CPS) specifically have helped people lead their lives with greater control and freedom. CPS domains have great societal significance, providing crucial assistance in industries ranging from security to healthcare. At the same time, machine learning (ML) algorithms are known for being substantially efficient, high performing, and have become a real standard due to greater accessibility, and now more than ever, multidisciplinary applications of ML for CPS have become a necessity to help uncover constructive solutions for real-world problems. Real-Time Applications of Machine Learning in Cyber-Physical Systems provides a relevant theoretical framework and the most recent empirical findings on various real-time applications of machine learning in cyber-physical systems. Covering topics like intrusion detection systems, predictive maintenance, and seizure prediction, this book is an essential resource for researchers, machine learning professionals, independent researchers, scholars, scientists, libraries, and academicians.

Download or read book Advances in Knowledge Discovery and Data Mining written by Kamal Karlapalem and published by Springer Nature. This book was released on 2021-05-08 with total page 865 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.