Perfect Secrecy Systems Immune to Spoofing Attacks

We present novel perfect secrecy systems that provide immunity to spoofing attacks under equiprobable source probability distributions. On the theoretical side, relying on an existence result for $t$-designs by Teirlinck, our construction method constructively generates systems that can reach an arbitrary high level of security. On the practical side, we obtain, via cyclic difference families, very efficient constructions of new optimal systems that are onefold secure against spoofing. Moreover, we construct, by means of $t$-designs for large values of $t$, the first near-optimal systems that are 5- and 6-fold secure as well as further systems with a feasible number of keys that are 7-fold secure against spoofing. We apply our results furthermore to a recently extended authentication model, where the opponent has access to a verification oracle. We obtain this way novel perfect secrecy systems with immunity to spoofing in the verification oracle model.Comment: 10 pages (double-column); to appear in "International Journal of Information Security

Cyber Security of Critical Infrastructures

Critical infrastructures are vital assets for public safety, economic welfare, and the national security of countries. The vulnerabilities of critical infrastructures have increased with the widespread use of information technologies. As Critical National Infrastructures are becoming more vulnerable to cyber-attacks, their protection becomes a significant issue for organizations as well as nations. The risks to continued operations, from failing to upgrade aging infrastructure or not meeting mandated regulatory regimes, are considered highly significant, given the demonstrable impact of such circumstances. Due to the rapid increase of sophisticated cyber threats targeting critical infrastructures with significant destructive effects, the cybersecurity of critical infrastructures has become an agenda item for academics, practitioners, and policy makers. A holistic view which covers technical, policy, human, and behavioural aspects is essential to handle cyber security of critical infrastructures effectively. Moreover, the ability to attribute crimes to criminals is a vital element of avoiding impunity in cyberspace. In this book, both research and practical aspects of cyber security considerations in critical infrastructures are presented. Aligned with the interdisciplinary nature of cyber security, authors from academia, government, and industry have contributed 13 chapters. The issues that are discussed and analysed include cybersecurity training, maturity assessment frameworks, malware analysis techniques, ransomware attacks, security solutions for industrial control systems, and privacy preservation methods

Mustererkennungsbasierte Verteidgung gegen gezielte Angriffe

The speed at which everything and everyone is being connected considerably outstrips the rate at which effective security mechanisms are introduced to protect them. This has created an opportunity for resourceful threat actors which have specialized in conducting low-volume persistent attacks through sophisticated techniques that are tailored to specific valuable targets. Consequently, traditional approaches are rendered ineffective against targeted attacks, creating an acute need for innovative defense mechanisms. This thesis aims at supporting the security practitioner in bridging this gap by introducing a holistic strategy against targeted attacks that addresses key challenges encountered during the phases of detection, analysis and response. The structure of this thesis is therefore aligned to these three phases, with each one of its central chapters taking on a particular problem and proposing a solution built on a strong foundation on pattern recognition and machine learning. In particular, we propose a detection approach that, in the absence of additional authentication mechanisms, allows to identify spear-phishing emails without relying on their content. Next, we introduce an analysis approach for malware triage based on the structural characterization of malicious code. Finally, we introduce MANTIS, an open-source platform for authoring, sharing and collecting threat intelligence, whose data model is based on an innovative unified representation for threat intelligence standards based on attributed graphs. As a whole, these ideas open new avenues for research on defense mechanisms and represent an attempt to counteract the imbalance between resourceful actors and society at large.In unserer heutigen Welt sind alle und alles miteinander vernetzt. Dies bietet mächtigen Angreifern die Möglichkeit, komplexe Verfahren zu entwickeln, die auf spezifische Ziele angepasst sind. Traditionelle Ansätze zur Bekämpfung solcher Angriffe werden damit ineffektiv, was die Entwicklung innovativer Methoden unabdingbar macht. Die vorliegende Dissertation verfolgt das Ziel, den Sicherheitsanalysten durch eine umfassende Strategie gegen gezielte Angriffe zu unterstützen. Diese Strategie beschäftigt sich mit den hauptsächlichen Herausforderungen in den drei Phasen der Erkennung und Analyse von sowie der Reaktion auf gezielte Angriffe. Der Aufbau dieser Arbeit orientiert sich daher an den genannten drei Phasen. In jedem Kapitel wird ein Problem aufgegriffen und eine entsprechende Lösung vorgeschlagen, die stark auf maschinellem Lernen und Mustererkennung basiert. Insbesondere schlagen wir einen Ansatz vor, der eine Identifizierung von Spear-Phishing-Emails ermöglicht, ohne ihren Inhalt zu betrachten. Anschliessend stellen wir einen Analyseansatz für Malware Triage vor, der auf der strukturierten Darstellung von Code basiert. Zum Schluss stellen wir MANTIS vor, eine Open-Source-Plattform für Authoring, Verteilung und Sammlung von Threat Intelligence, deren Datenmodell auf einer innovativen konsolidierten Graphen-Darstellung für Threat Intelligence Stardards basiert. Wir evaluieren unsere Ansätze in verschiedenen Experimenten, die ihren potentiellen Nutzen in echten Szenarien beweisen. Insgesamt bereiten diese Ideen neue Wege für die Forschung zu Abwehrmechanismen und erstreben, das Ungleichgewicht zwischen mächtigen Angreifern und der Gesellschaft zu minimieren

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Security protocols suite for machine-to-machine systems

Nowadays, the great diffusion of advanced devices, such as smart-phones, has shown that there is a growing trend to rely on new technologies to generate and/or support progress; the society is clearly ready to trust on next-generation communication systems to face today’s concerns on economic and social fields. The reason for this sociological change is represented by the fact that the technologies have been open to all users, even if the latter do not necessarily have a specific knowledge in this field, and therefore the introduction of new user-friendly applications has now appeared as a business opportunity and a key factor to increase the general cohesion among all citizens. Within the actors of this technological evolution, wireless machine-to-machine (M2M) networks are becoming of great importance. These wireless networks are made up of interconnected low-power devices that are able to provide a great variety of services with little or even no user intervention. Examples of these services can be fleet management, fire detection, utilities consumption (water and energy distribution, etc.) or patients monitoring. However, since any arising technology goes together with its security threats, which have to be faced, further studies are necessary to secure wireless M2M technology. In this context, main threats are those related to attacks to the services availability and to the privacy of both the subscribers’ and the services providers’ data. Taking into account the often limited resources of the M2M devices at the hardware level, ensuring the availability and privacy requirements in the range of M2M applications while minimizing the waste of valuable resources is even more challenging. Based on the above facts, this Ph. D. thesis is aimed at providing efficient security solutions for wireless M2M networks that effectively reduce energy consumption of the network while not affecting the overall security services of the system. With this goal, we first propose a coherent taxonomy of M2M network that allows us to identify which security topics deserve special attention and which entities or specific services are particularly threatened. Second, we define an efficient, secure-data aggregation scheme that is able to increase the network lifetime by optimizing the energy consumption of the devices. Third, we propose a novel physical authenticator or frame checker that minimizes the communication costs in wireless channels and that successfully faces exhaustion attacks. Fourth, we study specific aspects of typical key management schemes to provide a novel protocol which ensures the distribution of secret keys for all the cryptographic methods used in this system. Fifth, we describe the collaboration with the WAVE2M community in order to define a proper frame format actually able to support the necessary security services, including the ones that we have already proposed; WAVE2M was funded to promote the global use of an emerging wireless communication technology for ultra-low and long-range services. And finally sixth, we provide with an accurate analysis of privacy solutions that actually fit M2M-networks services’ requirements. All the analyses along this thesis are corroborated by simulations that confirm significant improvements in terms of efficiency while supporting the necessary security requirements for M2M networks

Modélisation formelle des systèmes de détection d'intrusions

L’écosystème de la cybersécurité évolue en permanence en termes du nombre, de la diversité, et de la complexité des attaques. De ce fait, les outils de détection deviennent inefficaces face à certaines attaques. On distingue généralement trois types de systèmes de détection d’intrusions : détection par anomalies, détection par signatures et détection hybride. La détection par anomalies est fondée sur la caractérisation du comportement habituel du système, typiquement de manière statistique. Elle permet de détecter des attaques connues ou inconnues, mais génère aussi un très grand nombre de faux positifs. La détection par signatures permet de détecter des attaques connues en définissant des règles qui décrivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La détection hybride repose sur plusieurs méthodes de détection incluant celles sus-citées. Elle présente l’avantage d’être plus précise pendant la détection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de règles de reconnaissance d’attaques. Le nombre d’attaques potentielles étant très grand, ces bases de règles deviennent rapidement difficiles à gérer et à maintenir. De plus, l’expression de règles avec état dit stateful est particulièrement ardue pour reconnaître une séquence d’événements. Dans cette thèse, nous proposons une approche stateful basée sur les diagrammes d’état-transition algébriques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de représenter de façon graphique et modulaire une spécification, ce qui facilite la maintenance et la compréhension des règles. Nous étendons la notation ASTD avec de nouvelles fonctionnalités pour représenter des attaques complexes. Ensuite, nous spécifions plusieurs attaques avec la notation étendue et exécutons les spécifications obtenues sur des flots d’événements à l’aide d’un interpréteur pour identifier des attaques. Nous évaluons aussi les performances de l’interpréteur avec des outils industriels tels que Snort et Zeek. Puis, nous réalisons un compilateur afin de générer du code exécutable à partir d’une spécification ASTD, capable d’identifier de façon efficiente les séquences d’événements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity, and the complexity of cyber attacks. Generally, we have three types of Intrusion Detection System (IDS) : anomaly-based detection, signature-based detection, and hybrid detection. Anomaly detection is based on the usual behavior description of the system, typically in a static manner. It enables detecting known or unknown attacks but also generating a large number of false positives. Signature based detection enables detecting known attacks by defining rules that describe known attacker’s behavior. It needs a good knowledge of attacker behavior. Hybrid detection relies on several detection methods including the previous ones. It has the advantage of being more precise during detection. Tools like Snort and Zeek offer low level languages to represent rules for detecting attacks. The number of potential attacks being large, these rule bases become quickly hard to manage and maintain. Moreover, the representation of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular representation of a specification, that facilitates maintenance and understanding of rules. We extend the ASTD notation with new features to represent complex attacks. Next, we specify several attacks with the extended notation and run the resulting specifications on event streams using an interpreter to identify attacks. We also evaluate the performance of the interpreter with industrial tools such as Snort and Zeek. Then, we build a compiler in order to generate executable code from an ASTD specification, able to efficiently identify sequences of events

Defensa en profundidad en sistemas de control de accesos mediante autenticación continua

La seguridad de los sistemas de información depende, en gran medida, de que el proceso de control de accesos funcione correctamente. Pero, en los modelos clásicos, la identidad del operador sólo se autentica en momentos puntuales. Tras décadas de implantación de dispositivos móviles en la sociedad [2], se encuentran presentes en prácticamente todos los procesos de negocio, pero estos activos sufren de debilidades en la gestión de su seguridad: no se ubican en perímetros de red bien definidos y bastionables, son más susceptibles de ser robados, etc.; y en un modelo clásico de control de accesos, una vez iniciada la sesión, careceríamos de medidas para combatir estas amenazas. Activar el proceso de autenticación periódicamente sería molesto y contraproducente, pero mediante biometría conductual (i.e., caracterizando la identidad de un usuario por cómo se comporta con el sistema), sí podría implementarse un sistema que validase la identidad del operador sin interferir en su sesión de trabajo: un sistema de autenticación continua. En esta tesis se aborda cómo la autenticación continua puede ayudar a mitigar los riesgos comentados, convirtiéndose en una tecnología diferenciadora al implantar medidas de defensa en profundidad en los sistemas de control de accesos. Al no existir un criterio claro para definir la autenticación continua, en primer lugar se ha desarrollado un estudio sistemático de la literatura, que permite caracterizar este área de investigación. En el segundo artículo se plantea un caso de uso, donde se refuerza la seguridad de un sistema distribuido aplicando principios de la autenticación continua; evidenciando al mismo tiempo las carencias de los sistemas dinámicos, y acotando la definición de autenticación continua. Finalmente, se estudia, experimentalmente, el rendimiento de 7 algoritmos supervisados de clasificación en el ámbito de la autenticación continua. Este estudio, junto con los resultados previos, sirve de soporte a la toma de decisiones en la implantación de la autenticación continua. Fija una base homogénea de conocimiento, que permite comparar las particularidades de estos algoritmos en el procesado de datos de biometría conductual, y discute su utilidad en función de los requisitos del sistema de control de accesos. Esta tesis evidencia que el uso de autenticación continua contribuye a la defensa en profundidad de los sistemas de control de accesos, especialmente, aunque exclusivamente, a la de aquellos con un operador cuya sesión de trabajo debe ser autenticada

Security in Distributed, Grid, Mobile, and Pervasive Computing

This book addresses the increasing demand to guarantee privacy, integrity, and availability of resources in networks and distributed systems. It first reviews security issues and challenges in content distribution networks, describes key agreement protocols based on the Diffie-Hellman key exchange and key management protocols for complex distributed systems like the Internet, and discusses securing design patterns for distributed systems. The next section focuses on security in mobile computing and wireless networks. After a section on grid computing security, the book presents an overview of security solutions for pervasive healthcare systems and surveys wireless sensor network security

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods