Side-channel attacks and countermeasures in the design of secure IC's devices for cryptographic applications

Abstract--- A lot of devices which are daily used have to guarantee the retention of sensible data. Sensible data are ciphered by a secure key by which only the key holder can get the data. For this reason, to protect the cipher key against possible attacks becomes a main issue. The research activities in hardware cryptography are involved in finding new countermeasures against various attack scenarios and, in the same time, in studying new attack methodologies. During the PhD, three different logic families to counteract Power Analysis were presented and a novel class of attacks was studied. Moreover, two different activities related to Random Numbers Generators have been addressed

SoK: Design Tools for Side-Channel-Aware Implementations

Side-channel attacks that leak sensitive information through a computing device's interaction with its physical environment have proven to be a severe threat to devices' security, particularly when adversaries have unfettered physical access to the device. Traditional approaches for leakage detection measure the physical properties of the device. Hence, they cannot be used during the design process and fail to provide root cause analysis. An alternative approach that is gaining traction is to automate leakage detection by modeling the device. The demand to understand the scope, benefits, and limitations of the proposed tools intensifies with the increase in the number of proposals. In this SoK, we classify approaches to automated leakage detection based on the model's source of truth. We classify the existing tools on two main parameters: whether the model includes measurements from a concrete device and the abstraction level of the device specification used for constructing the model. We survey the proposed tools to determine the current knowledge level across the domain and identify open problems. In particular, we highlight the absence of evaluation methodologies and metrics that would compare proposals' effectiveness from across the domain. We believe that our results help practitioners who want to use automated leakage detection and researchers interested in advancing the knowledge and improving automated leakage detection

Power Profile Obfuscation using RRAMs to Counter DPA Attacks

Side channel attacks, such as Differential Power Analysis (DPA), denote a special class of attacks in which sensitive key information is unveiled through information extracted from the physical device executing a cryptographic algorithm. This information leakage, known as side channel information, occurs from computations in a non-ideal system composed of electronic devices such as transistors. Power dissipation is one classic side channel source, which relays information of the data being processed. DPA uses statistical analysis to identify data-dependent correlations in sets of power measurements. Countermeasures against DPA focus on hiding or masking techniques at different levels of design abstraction and are typically associated with high power and area cost. Emerging technologies such as Resistive Random Access Memory (RRAM), offer unique opportunities to mitigate DPAs with their inherent memristor device characteristics such as variability in write time, ultra low power (0.1-3 pJ/bit), and high density (4F2). In this research, an RRAM based architecture is proposed to mitigate the DPA attacks by obfuscating the power profile. Specifically, a dual RRAM based memory module masks the power dissipation of the actual transaction by accessing both the data and its complement from the memory in tandem. DPA attack resiliency for a 128-bit AES cryptoprocessor using RRAM and CMOS memory modules is compared against baseline CMOS only technology. In the proposed AES architecture, four single port RRAM memory units store the intermediate state of the encryption. The correlation between the state data and sets of power measurement is masked due to power dissipated from inverse data access on dual RRAM memory. A customized simulation framework is developed to design the attack scenarios using Synopsys and Cadence tool suites, along with a Hamming weight DPA attack module. The attack mounted on a baseline CMOS architecture is successful and the full key is recovered. However, DPA attacks mounted on the dual CMOS and RRAM based AES cryptoprocessor yielded unsuccessful results with no keys recovered, demonstrating the resiliency of the proposed architecture against DPA attacks

Offloading cryptographic services to the SIM card in smartphones

Smartphones have achieved ubiquitous presence in people’s everyday life as communication, entertainment and work tools. Touch screens and a variety of sensors offer a rich experience and make applications increasingly diverse, complex and resource demanding. Despite their continuous evolution and enhancements, mobile devices are still limited in terms of battery life, processing power, storage capacity and network bandwidth. Computation offloading stands out among the efforts to extend device capabilities and face the growing gap between demand and availability of resources. As most popular technologies, mobile devices are attractive targets for malicious at- tackers. They usually store sensitive private data of their owners and are increasingly used for security sensitive activities such as online banking or mobile payments. While computation offloading introduces new challenges to the protection of those assets, it is very uncommon to take security and privacy into account as the main optimization objectives of this technique. Mobile OS security relies heavily on cryptography. Available hardware and software cryptographic providers are usually designed to resist software attacks. This kind of protection is not enough when physical control over the device is lost. Secure elements, on the other hand, include a set of protections that make them physically tamper-resistant devices. This work proposes a computation offloading technique that prioritizes enhancing security capabilities in mobile phones by offloading cryptographic operations to the SIM card, the only universally present secure element in those devices. Our contributions include an architecture for this technique, a proof-of-concept prototype developed under Android OS and the results of a performance evaluation that was conducted to study its execution times and battery consumption. Despite some limitations, our approach proves to be a valid alternative to enhance security on any smartphone.Los smartphones están omnipresentes en la vida cotidiana de las personas como herramientas de comunicación, entretenimiento y trabajo. Las pantallas táctiles y una variedad de sensores ofrecen una experiencia superior y hacen que las aplicaciones sean cada vez más diversas, complejas y demanden más recursos. A pesar de su continua evolución y mejoras, los dispositivos móviles aún están limitados en duración de batería, poder de procesamiento, capacidad de almacenamiento y ancho de banda de red. Computation offloading se destaca entre los esfuerzos para ampliar las capacidades del dispositivo y combatir la creciente brecha entre demanda y disponibilidad de recursos. Como toda tecnología popular, los smartphones son blancos atractivos para atacantes maliciosos. Generalmente almacenan datos privados y se utilizan cada vez más para actividades sensibles como banca en línea o pagos móviles. Si bien computation offloading presenta nuevos desafíos al proteger esos activos, es muy poco común tomar seguridad y privacidad como los principales objetivos de optimización de dicha técnica. La seguridad del SO móvil depende fuertemente de la criptografía. Los servicios criptográficos por hardware y software disponibles suelen estar diseñados para resistir ataques de software, protección insuficiente cuando se pierde el control físico sobre el dispositivo. Los elementos seguros, en cambio, incluyen un conjunto de protecciones que los hacen físicamente resistentes a la manipulación. Este trabajo propone una técnica de computation offloading que prioriza mejorar las capacidades de seguridad de los teléfonos móviles descargando operaciones criptográficas a la SIM, único elemento seguro universalmente presente en los mismos. Nuestras contribuciones incluyen una arquitectura para esta técnica, un prototipo de prueba de concepto desarrollado bajo Android y los resultados de una evaluación de desempeño que estudia tiempos de ejecución y consumo de batería. A pesar de algunas limitaciones, nuestro enfoque demuestra ser una alternativa válida para mejorar la seguridad en cualquier smartphone

Industrial networks and IIoT: Now and future trends

Connectivity is the one word summary for Industry 4.0 revolution. The importance of Internet of Things (IoT) and Industrial IoT (IIoT) have been increased dramatically with the rise of industrialization and industry 4.0. As new opportunities bring their own challenges, with the massive interconnected devices of the IIoT, cyber security of those networks and privacy of their users have become an important aspect. Specifically, intrusion detection for industrial networks (IIoT) has great importance. For instance, it is a key factor in improving the safe operation of the smart grid systems yet protecting the privacy of the consumers at the same time. In the same manner, data streaming is a valid option when the analysis is to be pushed from the cloud to the fog for industrial networks to provide agile response, since it brings the advantage of fast action on intrusion detection and also can buy time for intrusion mitigation. In order to dive deep in industrial networks, basic ground needs to be settled. Hence, this chapter serves in this manner, by presenting basic and emerging technologies along with ideas and discussions: First, an introduction of semiconductor evolution is provided along with the up-to-date hi-tech wired/wireless communication solutions for industrial networks. This is followed by a thorough representation of future trends in industrial environments. More importantly, enabling technologies for industrial networks is also presented. Finally, the chapter is concluded with a summary of the presentations along with future projections of IIoT networks

Optimising a defence-aware threat modelling diagram incorporating a defence-in-depth approach for the internet-of-things

Modern technology has proliferated into just about every aspect of life while improving the quality of life. For instance, IoT technology has significantly improved over traditional systems, providing easy life, time-saving, financial saving, and security aspects. However, security weaknesses associated with IoT technology can pose a significant threat to the human factor. For instance, smart doorbells can make household life easier, save time, save money, and provide surveillance security. Nevertheless, the security weaknesses in smart doorbells could be exposed to a criminal and pose a danger to the life and money of the household. In addition, IoT technology is constantly advancing and expanding and rapidly becoming ubiquitous in modern society. In that case, increased usage and technological advancement create security weaknesses that attract cybercriminals looking to satisfy their agendas. Perfect security solutions do not exist in the real world because modern systems are continuously improving, and intruders frequently attempt various techniques to discover security flaws and bypass existing security control in modern systems. In that case, threat modelling is a great starting point in understanding the threat landscape of the system and its weaknesses. Therefore, the threat modelling field in computer science was significantly improved by implementing various frameworks to identify threats and address them to mitigate them. However, most mature threat modelling frameworks are implemented for traditional IT systems that only consider software-related weaknesses and do not address the physical attributes. This approach may not be practical for IoT technology because it inherits software and physical security weaknesses. However, scholars employed mature threat modelling frameworks such as STRIDE on IoT technology because mature frameworks still include security concepts that are significant for modern technology. Therefore, mature frameworks cannot be ignored but are not efficient in addressing the threat associated with modern systems. As a solution, this research study aims to extract the significant security concept of matured threat modelling frameworks and utilise them to implement robust IoT threat modelling frameworks. This study selected fifteen threat modelling frameworks from among researchers and the defence-in-depth security concept to extract threat modelling techniques. Subsequently, this research study conducted three independent reviews to discover valuable threat modelling concepts and their usefulness for IoT technology. The first study deduced that integration of threat modelling approach software-centric, asset-centric, attacker-centric and data-centric with defence-in-depth is valuable and delivers distinct benefits. As a result, PASTA and TRIKE demonstrated four threat modelling approaches based on a classification scheme. The second study deduced the features of a threat modelling framework that achieves a high satisfaction level toward defence-in-depth security architecture. Under evaluation criteria, the PASTA framework scored the highest satisfaction value. Finally, the third study deduced IoT systematic threat modelling techniques based on recent research studies. As a result, the STRIDE framework was identified as the most popular framework, and other frameworks demonstrated effective capabilities valuable to IoT technology. Respectively, this study introduced Defence-aware Threat Modelling (DATM), an IoT threat modelling framework based on the findings of threat modelling and defence-in-depth security concepts. The steps involved with the DATM framework are further described with figures for better understatement. Subsequently, a smart doorbell case study is considered for threat modelling using the DATM framework for validation. Furthermore, the outcome of the case study was further assessed with the findings of three research studies and validated the DATM framework. Moreover, the outcome of this thesis is helpful for researchers who want to conduct threat modelling in IoT environments and design a novel threat modelling framework suitable for IoT technology

Emerging Security Threats in Modern Digital Computing Systems: A Power Management Perspective

Design of computing systems — from pocket-sized smart phones to massive cloud based data-centers — have one common daunting challenge : minimizing the power consumption. In this effort, power management sector is undergoing a rapid and profound transformation to promote clean and energy proportional computing. At the hardware end of system design, there is proliferation of specialized, feature rich and complex power management hardware components. Similarly, in the software design layer complex power management suites are growing rapidly. Concurrent to this development, there has been an upsurge in the integration of third-party components to counter the pressures of shorter time-to-market. These trends collectively raise serious concerns about trust and security of power management solutions. In recent times, problems such as overheating, performance degradation and poor battery life, have dogged the mobile devices market, including the infamous recall of Samsung Note 7. Power outage in the data-center of a major airline left innumerable passengers stranded, with thousands of canceled flights costing over 100 million dollars. This research examines whether such events of unintentional reliability failure, can be replicated using targeted attacks by exploiting the security loopholes in the complex power management infrastructure of a computing system. At its core, this research answers an imminent research question: How can system designers ensure secure and reliable operation of third-party power management units? Specifically, this work investigates possible attack vectors, and novel non-invasive detection and defense mechanisms to safeguard system against malicious power attacks. By a joint exploration of the threat model and techniques to seamlessly detect and protect against power attacks, this project can have a lasting impact, by enabling the design of secure and cost-effective next generation hardware platforms