Security and Privacy for Modern Wireless Communication Systems

The aim of this reprint focuses on the latest protocol research, software/hardware development and implementation, and system architecture design in addressing emerging security and privacy issues for modern wireless communication networks. Relevant topics include, but are not limited to, the following: deep-learning-based security and privacy design; covert communications; information-theoretical foundations for advanced security and privacy techniques; lightweight cryptography for power constrained networks; physical layer key generation; prototypes and testbeds for security and privacy solutions; encryption and decryption algorithm for low-latency constrained networks; security protocols for modern wireless communication networks; network intrusion detection; physical layer design with security consideration; anonymity in data transmission; vulnerabilities in security and privacy in modern wireless communication networks; challenges of security and privacy in node–edge–cloud computation; security and privacy design for low-power wide-area IoT networks; security and privacy design for vehicle networks; security and privacy design for underwater communications networks

Secure identity management in structured peer-to-peer (P2P) networks

Structured Peer-to-Peer (P2P) networks were proposed to solve routing problems of big distributed infrastructures. But the research community has been questioning their security for years. Most prior work in security services was focused on secure routing, reputation systems, anonymity, etc. However, the proper management of identities is an important prerequisite to provide most of these security services. The existence of anonymous nodes and the lack of a centralized authority capable of monitoring (and/or punishing) nodes make these systems more vulnerable against selfish or malicious behaviors. Moreover, these improper usages cannot be faced only with data confidentiality, nodes authentication, non-repudiation, etc. In particular, structured P2P networks should follow the following secure routing primitives: (1) secure maintenance of routing tables, (2) secure routing of messages, and (3) secure identity assignment to nodes. But the first two problems depend in some way on the third one. If nodes’ identifiers can be chosen by users without any control, these networks can have security and operational problems. Therefore, like any other network or service, structured P2P networks require a robust access control to prevent potential attackers joining the network and a robust identity assignment system to guarantee their proper operation. In this thesis, firstly, we analyze the operation of the current structured P2P networks when managing identities in order to identify what security problems are related to the nodes’ identifiers within the overlay, and propose a series of requirements to be accomplished by any generated node ID to provide more security to a DHT-based structured P2P network. Secondly, we propose the use of implicit certificates to provide more security and to exploit the improvement in bandwidth, storage and performance that these certificates present compared to explicit certificates, design three protocols to assign nodes’ identifiers avoiding the identified problems, while maintaining user anonymity and allowing users’ traceability. Finally, we analyze the operation of the most used mechanisms to distribute revocation data in the Internet, with special focus on the proposed systems to work in P2P networks, and design a new mechanism to distribute revocation data more efficiently in a structured P2P network.Las redes P2P estructuradas fueron propuestas para solventar problemas de enrutamiento en infraestructuras de grandes dimensiones pero su nivel de seguridad lleva años siendo cuestionado por la comunidad investigadora. La mayor parte de los trabajos que intentan mejorar la seguridad de estas redes se han centrado en proporcionar encaminamiento seguro, sistemas de reputación, anonimato de los usuarios, etc. Sin embargo, la adecuada gestión de las identidades es un requisito sumamente importante para proporcionar los servicios mencionados anteriormente. La existencia de nodos anónimos y la falta de una autoridad centralizada capaz de monitorizar (y/o penalizar) a los nodos hace que estos sistemas sean más vulnerables que otros a comportamientos maliciosos por parte de los usuarios. Además, esos comportamientos inadecuados no pueden ser detectados proporcionando únicamente confidencialidad de los datos, autenticación de los nodos, no repudio, etc. Las redes P2P estructuradas deberían seguir las siguientes primitivas de enrutamiento seguro: (1) mantenimiento seguro de las tablas de enrutamiento, (2) enrutamiento seguro de los mensajes, and (3) asignación segura de las identidades. Pero la primera de los dos primitivas depende de alguna forma de la tercera. Si las identidades de los nodos pueden ser elegidas por sus usuarios sin ningún tipo de control, muy probablemente aparecerán muchos problemas de funcionamiento y seguridad. Por lo tanto, de la misma forma que otras redes y servicios, las redes P2P estructuradas requieren de un control de acceso robusto para prevenir la presencia de atacantes potenciales, y un sistema robusto de asignación de identidades para garantizar su adecuado funcionamiento. En esta tesis, primero de todo analizamos el funcionamiento de las redes P2P estructuradas basadas en el uso de DHTs (Tablas de Hash Distribuidas), cómo gestionan las identidades de sus nodos, identificamos qué problemas de seguridad están relacionados con la identificación de los nodos y proponemos una serie de requisitos para generar identificadores de forma segura. Más adelante proponemos el uso de certificados implícitos para proporcionar más seguridad y explotar las mejoras en consumo de ancho de banda, almacenamiento y rendimiento que proporcionan estos certificados en comparación con los certificados explícitos. También hemos diseñado tres protocolos de asignación segura de identidades, los cuales evitan la mayor parte de los problemas identificados mientras mantienen el anonimato de los usuarios y la trazabilidad. Finalmente hemos analizado el funcionamiento de la mayoría de los mecanismos utilizados para distribuir datos de revocación en Internet, con especial interés en los sistemas propuestos para operar en redes P2P, y hemos diseñado un nuevo mecanismo para distribuir datos de revocación de forma más eficiente en redes P2P estructuradas.Postprint (published version

Crowdsourcing atop blockchains

Traditional crowdsourcing systems, such as Amazon\u27s Mechanical Turk (MTurk), though once acquiring great economic successes, have to fully rely on third-party platforms to serve between the requesters and the workers for basic utilities. These third-parties have to be fully trusted to assist payments, resolve disputes, protect data privacy, manage user authentications, maintain service online, etc. Nevertheless, tremendous real-world incidents indicate how elusive it is to completely trust these platforms in reality, and the reduction of such over-reliance becomes desirable. In contrast to the arguably vulnerable centralized approaches, a public blockchain is a distributed and transparent global consensus computer that is highly robust. The blockchain is usually managed and replicated by a large-scale peer-to-peer network collectively, thus being much more robust to be fully trusted for correctness and availability. It, therefore, becomes enticing to build novel crowdsourcing applications atop blockchains to reduce the over-trust on third-party platforms. However, this new fascinating technology also brings about new challenges, which were never that severe in the conventional centralized setting. The most serious issue is that the blockchain is usually maintained in the public Internet environment with a broader attack surface open to anyone. This not only causes serious privacy and security issues, but also allows the adversaries to exploit the attack surface to hamper more basic utilities. Worse still, most existing blockchains support only light on-chain computations, and the smart contract executed atop the decentralized consensus computer must be simple, which incurs serious feasibility problems. In reality, the privacy/security issue and the feasibility problem even restrain each other and create serious tensions to hinder the broader adoption of blockchain. The dissertation goes through the non-trivial challenges to realize secure yet still practical decentralization (for urgent crowdsourcing use-cases), and lay down the foundation for this line of research. In sum, it makes the next major contributions. First, it identifies the needed security requirements in decentralized knowledge crowdsourcing (e.g., data privacy), and initiates the research of private decentralized crowdsourcing. In particular, the confidentiality of solicited data is indispensable to prevent free-riders from pirating the others\u27 submissions, thus ensuring the quality of solicited knowledge. To this end, a generic private decentralized crowdsourcing framework is dedicatedly designed, analyzed, and implemented. Furthermore, this dissertation leverages concretely efficient cryptographic design to reduce the cost of the above generic framework. It focuses on decentralizing the special use-case of Amazon MTurk, and conducts multiple specific-purpose optimizations to remove needless generality to squeeze performance. The implementation atop Ethereum demonstrates a handling cost even lower than MTurk. In addition, it focuses on decentralized crowdsourcing of computing power for specific machine learning tasks. It lets a requester place deposits in the blockchain to recruit some workers for a designated (randomized) programs. If and only if these workers contribute their resources to compute correctly, they would earn well-deserved payments. For these goals, a simple yet still useful incentive mechanism is developed atop the blockchain to deter rational workers from cheating. Finally, the research initiates the first systematic study on crowdsourcing blockchains\u27 full nodes to assist superlight clients (e.g., mobile phones and IoT devices) to read the blockchain\u27s records. This dissertation presents a novel generic solution through the powerful lens of game-theoretic treatments, which solves the long-standing open problem of designing generic superlight clients for all blockchains

Data Hiding and Its Applications

Data hiding techniques have been widely used to provide copyright protection, data integrity, covert communication, non-repudiation, and authentication, among other applications. In the context of the increased dissemination and distribution of multimedia content over the internet, data hiding methods, such as digital watermarking and steganography, are becoming increasingly relevant in providing multimedia security. The goal of this book is to focus on the improvement of data hiding algorithms and their different applications (both traditional and emerging), bringing together researchers and practitioners from different research fields, including data hiding, signal processing, cryptography, and information theory, among others

Democracy Enhancing Technologies: Toward deployable and incoercible E2E elections

End-to-end verifiable election systems (E2E systems) provide a provably correct tally while maintaining the secrecy of each voter's ballot, even if the voter is complicit in demonstrating how they voted. Providing voter incoercibility is one of the main challenges of designing E2E systems, particularly in the case of internet voting. A second challenge is building deployable, human-voteable E2E systems that conform to election laws and conventions. This dissertation examines deployability, coercion-resistance, and their intersection in election systems. In the course of this study, we introduce three new election systems, (Scantegrity, Eperio, and Selections), report on two real-world elections using E2E systems (Punchscan and Scantegrity), and study incoercibility issues in one deployed system (Punchscan). In addition, we propose and study new practical primitives for random beacons, secret printing, and panic passwords. These are tools that can be used in an election to, respectively, generate publicly verifiable random numbers, distribute the printing of secrets between non-colluding printers, and to covertly signal duress during authentication. While developed to solve specific problems in deployable and incoercible E2E systems, these techniques may be of independent interest

Dealing With Misbehavior In Distributed Systems: A Game-Theoretic Approach

Most distributed systems comprise autonomous entities interacting with each other to achieve their objectives. These entities behave selfishly when making decisions. This behavior may result in strategical manipulation of the protocols thus jeopardizing the system wide goals. Micro-economics and game theory provides suitable tools to model such interactions. We use game theory to model and study three specific problems in distributed systems. We study the problem of sharing the cost of multicast transmissions and develop mechanisms to prevent cheating in such settings. We study the problem of antisocial behavior in a scheduling mechanism based on the second price sealed bid auction. We also build models using extensive form games to analyze the interactions of the attackers and the defender in a security game involving honeypots. Multicast cost sharing is an important problem and very few distributed strategyproof mechanisms exist to calculate the costs shares of the users. These mechanisms are susceptible to manipulation by rational nodes. We propose a faithful mechanism which uses digital signatures and auditing to catch and punish the cheating nodes. Such mechanism will incur some overhead. We deployed the proposed and existing mechanisms on planet-lab to experimentally analyze the overhead and other relevant economic properties of the proposed and existing mechanisms. In a second price sealed bid auction, even though the bids are sealed, an agent can infer the private values of the winning bidders, if the auction is repeated for related items. We study this problem from the perspective of a scheduling mechanism and develop an antisocial strategy which can be used by an agent to inflict losses on the other agents. In a security system attackers and defender(s) interact with each other. Examples of such systems are the honeynets which are used to map the activities of the attackers to gain valuable insight about their behavior. The attackers want to evade the honeypots while the defenders want them to attack the honeypots. These interesting interactions form the basis of our research where we develop a model used to analyze the interactions of an attacker and a honeynet system

A Blockchain-Based Retribution Mechanism for Collaborative Intrusion Detection

Collaborative intrusion detection approach uses the shared detection signature between the collaborative participants to facilitate coordinated defense. In the context of collaborative intrusion detection system (CIDS), however, there is no research focusing on the efficiency of the shared detection signature. The inefficient detection signature costs not only the IDS resource but also the process of the peer-to-peer (P2P) network. In this paper, we therefore propose a blockchain-based retribution mechanism, which aims to incentivize the participants to contribute to verifying the efficiency of the detection signature in terms of certain distributed consensus. We implement a prototype using Ethereum blockchain, which instantiates a token-based retribution mechanism and a smart contract-enabled voting-based distributed consensus. We conduct a number of experiments built on the prototype, and the experimental results demonstrate the effectiveness of the proposed approach

Robustness of Image-Based Malware Analysis

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a variety of classification algorithms including k-nearest neighbors, random forest, support vector machine, and multi-layer perceptron. We find that gist descriptors are more robust than CNNs, with respect to the obfuscation techniques that we consider