The Role of Physical Layer Security in IoT: A Novel Perspective

This paper deals with the problem of securing the configuration phase of an Internet of Things (IoT) system. The main drawbacks of current approaches are the focus on specific techniques and methods, and the lack of a cross layer vision of the problem. In a smart environment, each IoT device has limited resources and is often battery operated with limited capabilities (e.g., no keyboard). As a consequence, network security must be carefully analyzed in order to prevent security and privacy issues. In this paper, we will analyze the IoT threats, we will propose a security framework for the device initialization and we will show how physical layer security can effectively boost the security of IoT systems

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP

The Internet plays a crucial role in today\u27s social and political movements by facilitating the free circulation of speech, information, and ideas; democracy and human rights throughout the world critically depend on preserving and bolstering the Internet\u27s openness. Consequently, repressive regimes, totalitarian governments, and corrupt corporations regulate, monitor, and restrict the access to the Internet, which is broadly known as Internet \emph{censorship}. Most countries are improving the internet infrastructures, as a result they can implement more advanced censoring techniques. Also with the advancements in the application of machine learning techniques for network traffic analysis have enabled the more sophisticated Internet censorship. In this thesis, We take a close look at the main pillars of internet censorship, we will introduce new defense and attacks in the internet censorship literature. Internet censorship techniques investigate users’ communications and they can decide to interrupt a connection to prevent a user from communicating with a specific entity. Traffic analysis is one of the main techniques used to infer information from internet communications. One of the major challenges to traffic analysis mechanisms is scaling the techniques to today\u27s exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. We aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \emph{compressive traffic analysis}. Moreover, we show that, unfortunately, traffic analysis attacks can be conducted on Anonymity systems with drastically higher accuracies than before by leveraging emerging learning mechanisms. We particularly design a system, called \deepcorr, that outperforms the state-of-the-art by significant margins in correlating network connections. \deepcorr leverages an advanced deep learning architecture to \emph{learn} a flow correlation function tailored to complex networks. Also to be able to analyze the weakness of such approaches we show that an adversary can defeat deep neural network based traffic analysis techniques by applying statistically undetectable \emph{adversarial perturbations} on the patterns of live network traffic. We also design techniques to circumvent internet censorship. Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. We propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to \emph{all} previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it \emph{downstream-only} decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Then, we propose to use game theoretic approaches to model the arms races between the censors and the censorship circumvention tools. This will allow us to analyze the effect of different parameters or censoring behaviors on the performance of censorship circumvention tools. We apply our methods on two fundamental problems in internet censorship. Finally, to bring our ideas to practice, we designed a new censorship circumvention tool called \name. \name aims at increasing the collateral damage of censorship by employing a ``mass\u27\u27 of normal Internet users, from both censored and uncensored areas, to serve as circumvention proxies

Security and Privacy Issues in Wireless Mesh Networks: A Survey

This book chapter identifies various security threats in wireless mesh network (WMN). Keeping in mind the critical requirement of security and user privacy in WMNs, this chapter provides a comprehensive overview of various possible attacks on different layers of the communication protocol stack for WMNs and their corresponding defense mechanisms. First, it identifies the security vulnerabilities in the physical, link, network, transport, application layers. Furthermore, various possible attacks on the key management protocols, user authentication and access control protocols, and user privacy preservation protocols are presented. After enumerating various possible attacks, the chapter provides a detailed discussion on various existing security mechanisms and protocols to defend against and wherever possible prevent the possible attacks. Comparative analyses are also presented on the security schemes with regards to the cryptographic schemes used, key management strategies deployed, use of any trusted third party, computation and communication overhead involved etc. The chapter then presents a brief discussion on various trust management approaches for WMNs since trust and reputation-based schemes are increasingly becoming popular for enforcing security in wireless networks. A number of open problems in security and privacy issues for WMNs are subsequently discussed before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the author's previous submission in arXiv submission: arXiv:1102.1226. There are some text overlaps with the previous submissio

Physical Layer Security of Short Packet Communications

This dissertation aims to conduct research on security issues of 5G wireless networks, which are vulnerable to external security threats while supporting services for a massive number of users and devices. In practical wireless communication systems, the communication is subject to overhearing by external eavesdroppers due to the broadcast nature of the wireless medium. Physical layer security (PLS) shows promise as a viable option for securing future communication systems because it utilizes channel characteristics to hide transmitted messages from possible adversaries without depending on traditional cryptographic solutions. However, 5G systems are expected to support various traffic types, including short packet transmission, which results in new challenges in terms of security. Particularly, short packet transmission introduces a penalty on the secrecy capacity, which is the rate of secure communication between authorized parties in the presence of an adversary. It is well-known that PLS is based on the assumption that transmission happens with a maximum rate reliably and securely when the blocklengths are sufficiently large. In the literature, limited studies focus on PLS for short packet communications (SPC) and the performance analysis of secure SPC remains an open problem. Our goal is to study large-scale networks, but first, as a simple case, secure communication of a wiretap channel under the attack of an active eavesdropper, with two capabilities, namely half-duplex and full-duplex, is investigated. It appears that an active eavesdropper is more harmful to the secrecy throughput than a passive one, and the full-duplex eavesdropper (Eve) is more dangerous than a half-duplex Eve. Indeed, the performance is measured in terms of average secrecy throughput and theoretical approximations are validated through Monte Carlo simulations throughout all the contributions of the dissertation. Second, the wiretap channel model with multiple passive eavesdroppers is explored to shed light on a more realistic scenario in large-scale wireless networks. Although an increased number of antennas can lead to higher average secrecy throughput, achieving higher secrecy throughput is more effectively accomplished by increasing the transmission rates. As a final contribution, the previous wiretap channel setting is extended by adding multiple receivers. The security performance against colluding and non-colluding attackers is thoroughly examined. According to our results, it is more advantageous for eavesdroppers to collude and they are more powerful when their number increases. And we conclude the dissertation with a discussion of future work

Design and Implementation of Algorithms for Traffic Classification

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead

Behavioral Mimicry Covert Communication

Covert communication refers to the process of communicating data through a channel that is neither designed, nor intended to transfer information. Traditionally, covert channels are considered as security threats in computer systems and a great deal of attention has been given to countermeasures for covert communication schemes. The evolution of computer networks led the communication community to revisit the concept of covert communication not only as a security threat but also as an alternative way of providing security and privacy to communication networks. In fact, the heterogeneous structure of computer networks and the diversity of communication protocols provide an appealing setting for covert channels. This dissertation is an exploration on a novel design methodology for undetectable and robust covert channels in communication networks. Our new design methodology is based on the concept of behavioral mimicry in computer systems. The objective is to design a covert transmitter that has enough degrees of freedom to behave like an ordinary transmitter and react normally to unpredictable network events, yet it has the ability to modulate a covert message over its behavioral fingerprints in the network. To this end, we argue that the inherent randomness in communication protocols and network environments is the key in finding the proper medium for network covert channels. We present a few examples on how random behaviors in communication protocols lead to discovery of suitable shared resources for covert channels. The proposed design methodology is tested on two new covert communication schemes, one is designed for wireless networks and the other one is optimized for public communication networks (e.g., Internet). Each design is accompanied by a comprehensive analysis from undetectability, achievable covert rate and reliability perspectives. In particular, we introduced turbo covert channels, a family of extremely robust model-based timing covert channels that achieve provable polynomial undetectability in public communication networks. This means that the covert channel is undetectable against any polynomial-time statistical test that analyzes samples of the covert traffic and the legitimate traffic of the network. Target applications for the proposed covert communication schemes are discussed including detailed practical scenarios in which the proposed channels can be implemented