Low-power emerging memristive designs towards secure hardware systems for applications in internet of things

Emerging memristive devices offer enormous advantages for applications such as non-volatile memories and in-memory computing (IMC), but there is a rising interest in using memristive technologies for security applications in the era of internet of things (IoT). In this review article, for achieving secure hardware systems in IoT, low-power design techniques based on emerging memristive technology for hardware security primitives/systems are presented. By reviewing the state-of-the-art in three highlighted memristive application areas, i.e. memristive non-volatile memory, memristive reconfigurable logic computing and memristive artificial intelligent computing, their application-level impacts on the novel implementations of secret key generation, crypto functions and machine learning attacks are explored, respectively. For the low-power security applications in IoT, it is essential to understand how to best realize cryptographic circuitry using memristive circuitries, and to assess the implications of memristive crypto implementations on security and to develop novel computing paradigms that will enhance their security. This review article aims to help researchers to explore security solutions, to analyze new possible threats and to develop corresponding protections for the secure hardware systems based on low-cost memristive circuit designs

Intrusion Detection: Embedded Software Machine Learning and Hardware Rules Based Co-Designs

Security of innovative technologies in future generation networks such as (Cyber Physical Systems (CPS) and Wi-Fi has become a critical universal issue for individuals, economy, enterprises, organizations and governments. The rate of cyber-attacks has increased dramatically, and the tactics used by the attackers are continuing to evolve and have become ingenious during the attacks. Intrusion Detection is one of the solutions against these attacks. One approach in designing an intrusion detection system (IDS) is software-based machine learning. Such approach can predict and detect threats before they result in major security incidents. Moreover, despite the considerable research in machine learning based designs, there is still a relatively small body of literature that is concerned with imbalanced class distributions from the intrusion detection system perspective. In addition, it is necessary to have an effective performance metric that can compare multiple multi-class as well as binary-class systems with respect to class distribution. Furthermore, the expectant detection techniques must have the ability to identify real attacks from random defects, ingrained defects in the design, misconfigurations of the system devices, system faults, human errors, and software implementation errors. Moreover, a lightweight IDS that is small, real-time, flexible and reconfigurable enough to be used as permanent elements of the system's security infrastructure is essential. The main goal of the current study is to design an effective and accurate intrusion detection framework with minimum features that are more discriminative and representative. Three publicly available datasets representing variant networking environments are adopted which also reflect realistic imbalanced class distributions as well as updated attack patterns. The presented intrusion detection framework is composed of three main modules: feature selection and dimensionality reduction, handling imbalanced class distributions, and classification. The feature selection mechanism utilizes searching algorithms and correlation based subset evaluation techniques, whereas the feature dimensionality reduction part utilizes principal component analysis and auto-encoder as an instance of deep learning. Various classifiers, including eight single-learning classifiers, four ensemble classifiers, one stacked classifier, and five imbalanced class handling approaches are evaluated to identify the most efficient and accurate one(s) for the proposed intrusion detection framework. A hardware-based approach to detect malicious behaviors of sensors and actuators embedded in medical devices, in which the safety of the patient is critical and of utmost importance, is additionally proposed. The idea is based on a methodology that transforms a device's behavior rules into a state machine to build a Behavior Specification Rules Monitoring (BSRM) tool for four medical devices. Simulation and synthesis results demonstrate that the BSRM tool can effectively identify the expected normal behavior of the device and detect any deviation from its normal behavior. The performance of the BSRM approach has also been compared with a machine learning based approach for the same problem. The FPGA module of the BSRM can be embedded in medical devices as an IDS and can be further integrated with the machine learning based approach. The reconfigurable nature of the FPGA chip adds an extra advantage to the designed model in which the behavior rules can be easily updated and tailored according to the requirements of the device, patient, treatment algorithm, and/or pervasive healthcare application

Adversarial Deep Learning and Security with a Hardware Perspective

Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep learning stands as a balancing force to ensure these developments remain grounded in the real-world and proceed along a responsible trajectory. Recently, the growth of deep learning has begun intersecting with the computer hardware domain to improve performance and efficiency for resource constrained application domains. The works investigated in this dissertation constitute our pioneering efforts in migrating adversarial deep learning into the hardware domain alongside its parent field of research

Null Convention Logic applications of asynchronous design in nanotechnology and cryptographic security

This dissertation presents two Null Convention Logic (NCL) applications of asynchronous logic circuit design in nanotechnology and cryptographic security. The first application is the Asynchronous Nanowire Reconfigurable Crossbar Architecture (ANRCA); the second one is an asynchronous S-Box design for cryptographic system against Side-Channel Attacks (SCA). The following are the contributions of the first application: 1) Proposed a diode- and resistor-based ANRCA (DR-ANRCA). Three configurable logic block (CLB) structures were designed to efficiently reconfigure a given DR-PGMB as one of the 27 arbitrary NCL threshold gates. A hierarchical architecture was also proposed to implement the higher level logic that requires a large number of DR-PGMBs, such as multiple-bit NCL registers. 2) Proposed a memristor look-up-table based ANRCA (MLUT-ANRCA). An equivalent circuit simulation model has been presented in VHDL and simulated in Quartus II. Meanwhile, the comparison between these two ANRCAs have been analyzed numerically. 3) Presented the defect-tolerance and repair strategies for both DR-ANRCA and MLUT-ANRCA. The following are the contributions of the second application: 1) Designed an NCL based S-Box for Advanced Encryption Standard (AES). Functional verification has been done using Modelsim and Field-Programmable Gate Array (FPGA). 2) Implemented two different power analysis attacks on both NCL S-Box and conventional synchronous S-Box. 3) Developed a novel approach based on stochastic logics to enhance the resistance against DPA and CPA attacks. The functionality of the proposed design has been verified using an 8-bit AES S-box design. The effects of decision weight, bitstream length, and input repetition times on error rates have been also studied. Experimental results shows that the proposed approach enhances the resistance to against the CPA attack by successfully protecting the hidden key --Abstract, page iii

ENERGY RESILIENCE IMPACT OF SUPPLY CHAIN NETWORK DISRUPTION TO MILITARY MICROGRIDS

The ability to provide uninterrupted power to military installations is paramount in executing a country's national defense strategy. Microgrid architectures increase installation energy resilience through redundant local generation sources and the capability for grid independence. However, deliberate attacks from near-peer competitors can disrupt the associated supply chain network, thereby affecting mission-critical loads. Utilizing an integrated discrete-time Markov chain and dynamic Bayesian network approach, we investigate disruption propagation throughout a supply chain network and quantify its mission impact on an islanded microgrid. We propose a novel methodology and an associated metric we term "energy resilience impact" to identify and address supply-chain disruption risks to energy security. A case study of a fictional military installation is presented to demonstrate how installation energy managers can adopt this methodology for the design and improvement of military microgrids.Outstanding ThesisLieutenant, United States NavyApproved for public release. Distribution is unlimited

Reliability, Safety, and Performance for Two Aerospace Revolutions - UAS/ODM and Commercial Deep Space

Aerospace is in the midst of a renaissance, expanding on both the air and space side into major new commercial areas including unmanned air systems (UAS), on demand mobility (ODM), personal air vehicles (PAV), and commercial deep space. These new areas require, in the initial planning, consideration of new safety, reliability, and in some cases, enabling performance approaches for viability. For example, due to their huge numbers, if current accident rates prevail, UAS/ODM/PAV aircraft could crash at an unacceptable rate, causing life and property damage. Also, if humans in commercial space activities have serious health issues and/or there are unacceptable rocket viability issues/crash rates, these new, major markets (order of 1 trillion dollars per year) could be rapidly curtailed until agreeable and effective changes are instituted, producing additional expense, delay, and reduced revenue. This report addresses such safety and reliability issues and includes: performance enhancement possibilities such as an enabling Air Traffic Control System (ATC), crash proof vehicles, increased range for aero, space debris removal, and human health for space

Healthcare systems protection: All-in-one cybersecurity approach

Cyber risks are increasingly widespread as healthcare organizations play a defining role in society. Several studies have revealed an increase in cybersecurity threats in the industry, which should concern us all. When it comes to cybersecurity, the consequences can be felt throughout the organization, from the smallest processes to the overall ability of the organization to function. Typically, a cyberattack results in the disclosure of confidential information that undermines your competitive advantage and overall trust. Healthcare as a critical sector has, like many other sectors, a late bet on its transformation to cybersecurity across the board. This dissertation reinforces this need by presenting a value-added solution that helps strengthen the internal processes of healthcare units, enabling their primary mission of saving lives while ensuring the confidentiality and security of patient and institutional data. The solution is presented as a technological composite that translates into a methodology and innovative artifact for integration, monitoring, and security of critical medical infrastructures based on operational use cases. The approach that involves people, processes, and technology is based on a model that foresees the evaluation of potential assets for integration and monitoring, as well as leveraging the efficiency in responding to security incidents with the formal development of a process and mechanisms for alert and resolution of exposure and attack scenarios. On a technical level, the artifact relies on the integration of a medical image archiving system (PACS) into a SIEM to validate application logs that are linked to rules to map anomalous behaviors that trigger the incident management process on an IHS platform with custom-developed features. The choice for integration in the validation prototype of the PACS system is based not only on its importance in the orchestration of activities in the organization of a health institution, but also with the recent recommendations of various cybersecurity agencies and organizations for the importance of their protection in response to the latest trends in cyberattacks. In line with the results obtained, this approach will have full applicability in a real operational context, following the latest practices and technologies in the sector.Os riscos cibernéticos estão cada vez mais difundidos à medida que as organizações de cuidados de saúde desempenham um papel determinante na sociedade. Vários estudos revelaram um aumento das ameaças de cibersegurança no setor, o que nos deve preocupar a todos. Quando se trata de cibersegurança, as consequências podem ser sentidas em toda a organização, desde os mais pequenos processos até à sua capacidade global de funcionamento. Normalmente, um ciberataque resulta na divulgação de informações confidenciais que colocam em causa a sua vantagem competitiva e a confiança geral. O healthcare como setor crítico apresenta, como muitos outros setores, uma aposta tardia na sua transformação para a cibersegurança de forma generalizada. Esta dissertação reforça esta necessidade apresentando uma solução de valor acrescentado que ajuda a potenciar os processos internos das unidades de saúde possibilitando a sua missão principal de salvar vidas, aumentando a garantia de confidencialidade e segurança dos dados dos pacientes e instituições. A solução apresenta-se como um compósito tecnológico que se traduz numa metodologia e artefacto de inovação para integração, monitorização e segurança de infraestruturas médicas críticas baseado em use cases de operação. A abordagem que envolve pessoas, processos e tecnologia assenta num modelo que prevê a avaliação de potenciais ativos para integração e monitorização, como conta alavancar a eficiência na resposta a incidentes de segurança com o desenvolvimento formal de um processo e mecanismos para alerta e resolução de cenários de exposição e ataque. O artefacto, a nível tecnológico, conta com a integração do sistema de arquivo de imagem médica (PACS) num SIEM para validação de logs aplicacionais que estão associados a regras que mapeiam comportamentos anómalos que originam o despoletar do processo de gestão de incidentes numa plataforma IHS com funcionalidades desenvolvidas à medida. A escolha para integração no protótipo de validação do sistema PACS tem por base não só a sua importância na orquestração de atividades na orgânica duma instituição de saúde, mas também com as recentes recomendações de várias agências e organizações de cibersegurança para a importância da sua proteção em resposta às últimas tendências de ciberataques. Em linha com os resultados auscultados, esta abordagem terá total aplicabilidade em contexto real de operação, seguindo as mais recentes práticas e tecnologias no sector

Statistical Methods for Detection and Mitigation of the Effect of Different Types of Cyber-Attacks and Inconsistencies in Electrical Design Parameters in a Real World Distribution System

In the present grid real time control systems are the energy management systems and distribution management systems that utilize measurements from real-time units (RTUs) and Supervisory Control and Data Acquisition (SCADA). The SCADA systems are designed to operate on isolated, private networks without even basic security features which are now being migrated to modern IP-based communications providing near real time information from measuring and controlling units. To function brain (SCADA) properly heart (RTUs) should provide necessary response thereby creating a coupling which makes SCADA systems as targets for cyber-attacks to cripple either part of the electric transmission grid or fully shut down (create blackout) the grid. Cyber-security research for a distribution grid is a topic yet to be addressed. To date firewalls and classic signature-based intrusion detection systems have provided access control and awareness of suspicious network traffic but typically have not offered any real-time detection and defense solutions for electric distribution grids.;This thesis work not only addresses the cyber security modeling, detection and prevention but also addresses model inconsistencies for effectively utilizing and controlling distribution management systems. Inconsistencies in the electrical design parameters of the distribution network or cyber-attack conditions may result in failing of the automated operations or distribution state estimation process which might lead the system to a catastrophic condition or give erroneous solutions for the probable problems. This research work also develops a robust and reliable voltage controller based on Multiple Linear Regression (MLR) to maintain the voltage profile in a smart distribution system under cyber-attacks and model inconsistencies. The developed cyber-attack detection and mitigation algorithms have been tested on IEEE 13 node and 600+ node real American electric distribution systems modeled in Electric Power Research Institute\u27s (EPRI) OpenDSS software

A Strategic Roadmap for the Manufacturing Industry to Implement Industry 4.0

Industry 4.0 (also referred to as digitization of manufacturing) is characterized by cyber physical systems, automation, and data exchange. It is no longer a future trend and is being employed worldwide by manufacturing organizations, to gain benefits of improved performance, reduced inefficiencies, and lower costs, while improving flexibility. However, the implementation of Industry 4.0 enabling technologies is a difficult task and becomes even more challenging without any standardized approach. The barriers include, but are not limited to, lack of knowledge, inability to realistically quantify the return on investment, and lack of a skilled workforce. This study presents a systematic and content-centric literature review of Industry 4.0 enabling technologies, to highlight their impact on the manufacturing industry. It also provides a strategic roadmap for the implementation of Industry 4.0, based on lean six sigma approaches. The basis of the roadmap is the design for six sigma approach for the development of a new process chain, followed by a continuous improvement plan. The reason for choosing lean six sigma is to provide manufacturers with a sense of familiarity, as they have been employing these principles for removing waste and reducing variability. Major reasons for the rejection of Industry 4.0 implementation methodologies by manufactures are fear of the unknown and resistance to change, whereas the use of lean six sigma can mitigate them. The strategic roadmap presented in this paper can offer a holistic view of phases that manufacturers should undertake and the challenges they might face in their journey toward Industry 4.0 transition

phenomenological simulators of critical infrastructures

The objective of this chapter is to introduce and discuss the main phenomenological approaches that have been used within the CI M&S area. Phenomenological models are used to analyse the organizational phenomena of the society considering its complexity (finance, mobility, health) and the interactions among its different components. Within CI MA&S, different modelling approaches have been proposed and used as, for example, physical simulators (e.g. power flow simulators for electrical networks). Physical simulators are used to predict the behaviour of the physical system (the technological network) under different conditions. As an example, electrical engineers use different kind of simulators during planning and managing of network activities for different purposes: (1) power flow simulators for the evaluation of electrical network configuration changes (that can be both deliberate changes or results from of the effects of accidents and/or attacks) and contingency analysis, (2) real time simulators for the design of protection devices and new controllers. For the telecommunication domain one mat resort to network traffic simulators as for example ns2/ns3 codes that allow the simulation of telecommunication networks (wired/wireless) at packet switching level and evaluate its performances. Single domains simulators can be federated to analyse the interactions among different domains. In contrast, phenomenological simulators use more abstract data and models for the interaction among the different components of the system. The chapter will describe the main characteristic of some of the main simulation approaches resulting from the ENEA and UBC efforts in the CIP and Complexity Science field