Biblio

Thanks to their anonymity (pseudonymity) and elimination of trusted intermediaries, cryptocurrencies such as Bitcoin have created or stimulated growth in many businesses and communities. Unfortunately, some of these are criminal, e.g., money laundering, illicit marketplaces, and ransomware. Next-generation cryptocurrencies such as Ethereum will include rich scripting languages in support of smart contracts, programs that autonomously intermediate transactions. In this paper, we explore the risk of smart contracts fueling new criminal ecosystems. Specifically, we show how what we call criminal smart contracts (CSCs) can facilitate leakage of confidential information, theft of cryptographic keys, and various real-world crimes (murder, arson, terrorism). We show that CSCs for leakage of secrets (a la Wikileaks) are efficiently realizable in existing scripting languages such as that in Ethereum. We show that CSCs for theft of cryptographic keys can be achieved using primitives, such as Succinct Non-interactive ARguments of Knowledge (SNARKs), that are already expressible in these languages and for which efficient supporting language extensions are anticipated. We show similarly that authenticated data feeds, an emerging feature of smart contract systems, can facilitate CSCs for real-world crimes (e.g., property crimes). Our results highlight the urgency of creating policy and technical safeguards against CSCs in order to realize the promise of smart contracts for beneficial goals.

Smart contracts are programs that execute autonomously on blockchains. Their key envisioned uses (e.g. financial instruments) require them to consume data from outside the blockchain (e.g. stock quotes). Trustworthy data feeds that support a broad range of data requests will thus be critical to smart contract ecosystems. We present an authenticated data feed system called Town Crier (TC). TC acts as a bridge between smart contracts and existing web sites, which are already commonly trusted for non-blockchain applications. It combines a blockchain front end with a trusted hardware back end to scrape HTTPS-enabled websites and serve source-authenticated data to relying smart contracts. TC also supports confidentiality. It enables private data requests with encrypted parameters. Additionally, in a generalization that executes smart-contract logic within TC, the system permits secure use of user credentials to scrape access-controlled online data sources. We describe TC's design principles and architecture and report on an implementation that uses Intel's recently introduced Software Guard Extensions (SGX) to furnish data to the Ethereum smart contract system. We formally model TC and define and prove its basic security properties in the Universal Composibility (UC) framework. Our results include definitions and techniques of general interest relating to resource consumption (Ethereum's "gas" fee system) and TCB minimization. We also report on experiments with three example applications. We plan to launch TC soon as an online public service.

We study the strategic considerations of miners participating in the bitcoin's protocol. We formulate and study the stochastic game that underlies these strategic considerations. The miners collectively build a tree of blocks, and they are paid when they create a node (mine a block) which will end up in the path of the tree that is adopted by all. Since the miners can hide newly mined nodes, they play a game with incomplete information. Here we consider two simplified forms of this game in which the miners have complete information. In the simplest game the miners release every mined block immediately, but are strategic on which blocks to mine. In the second more complicated game, when a block is mined it is announced immediately, but it may not be released so that other miners cannot continue mining from it. A miner not only decides which blocks to mine, but also when to release blocks to other miners. In both games, we show that when the computational power of each miner is relatively small, their best response matches the expected behavior of the bitcoin designer. However, when the computational power of a miner is large, he deviates from the expected behavior, and other Nash equilibria arise.

Bitcoin provides two incentives for miners: block rewards and transaction fees. The former accounts for the vast majority of miner revenues at the beginning of the system, but it is expected to transition to the latter as the block rewards dwindle. There has been an implicit belief that whether miners are paid by block rewards or transaction fees does not affect the security of the block chain. We show that this is not the case. Our key insight is that with only transaction fees, the variance of the block reward is very high due to the exponentially distributed block arrival time, and it becomes attractive to fork a "wealthy" block to "steal" the rewards therein. We show that this results in an equilibrium with undesirable properties for Bitcoin's security and performance, and even non-equilibria in some circumstances. We also revisit selfish mining and show that it can be made profitable for a miner with an arbitrarily low hash power share, and who is arbitrarily poorly connected within the network. Our results are derived from theoretical analysis and confirmed by a new Bitcoin mining simulator that may be of independent interest.We discuss the troubling implications of our results for Bitcoin's future security and draw lessons for the design of new cryptocurrencies.

Motivated by the impossibility of achieving fairness in secure computation [Cleve, STOC 1986], recent works study a model of fairness in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty to every other party that did not receive the output. These works show how to design protocols for secure computation with penalties that guarantees that either fairness is guaranteed or that each honest party obtains a monetary penalty from the adversary. Protocols for this task are typically designed in an hybrid model where parties have access to a "claim-or-refund" transaction functionality denote FCR*. In this work, we obtain improvements on the efficiency of these constructions by amortizing the cost over multiple executions of secure computation with penalties. More precisely, for computational security parameter λ, we design a protocol that implements l = poly\vphantom\\(λ) instances of secure computation with penalties where the total number of calls to FCR* is independent of l.

Cryptocurrencies, such as Bitcoin and 250 similar alt-coins, embody at their core a blockchain protocol –- a mechanism for a distributed network of computational nodes to periodically agree on a set of new transactions. Designing a secure blockchain protocol relies on an open challenge in security, that of designing a highly-scalable agreement protocol open to manipulation by byzantine or arbitrarily malicious nodes. Bitcoin's blockchain agreement protocol exhibits security, but does not scale: it processes 3–7 transactions per second at present, irrespective of the available computation capacity at hand. In this paper, we propose a new distributed agreement protocol for permission-less blockchains called ELASTICO. ELASTICO scales transaction rates almost linearly with available computation for mining: the more the computation power in the network, the higher the number of transaction blocks selected per unit time. ELASTICO is efficient in its network messages and tolerates byzantine adversaries of up to one-fourth of the total computational power. Technically, ELASTICO uniformly partitions or parallelizes the mining network (securely) into smaller committees, each of which processes a disjoint set of transactions (or "shards"). While sharding is common in non-byzantine settings, ELASTICO is the first candidate for a secure sharding protocol with presence of byzantine adversaries. Our scalability experiments on Amazon EC2 with up to \$1, 600\$ nodes confirm ELASTICO's theoretical scaling properties.

Motivated by the impossibility of achieving fairness in secure computation [Cleve, STOC 1986], recent works study a model of fairness in which an adversarial party that aborts on receiving output is forced to pay a mutually predefined monetary penalty to every other party that did not receive the output. These works show how to design protocols for secure computation with penalties that tolerate an arbitrary number of corruptions. In this work, we improve the efficiency of protocols for secure computation with penalties in a hybrid model where parties have access to the "claim-or-refund" transaction functionality. Our first improvement is for the ladder protocol of Bentov and Kumaresan (Crypto 2014) where we improve the dependence of the script complexity of the protocol (which corresponds to miner verification load and also space on the blockchain) on the number of parties from quadratic to linear (and in particular, is completely independent of the underlying function). Our second improvement is for the see-saw protocol of Kumaresan et al. (CCS 2015) where we reduce the total number of claim-or-refund transactions and also the script complexity from quadratic to linear in the number of parties.

The blockchain emerges as an innovative tool which proves to be useful in a number of application scenarios. A number of large industrial players, such as IBM, Microsoft, Intel, and NEC, are currently investing in exploiting the blockchain in order to enrich their portfolio of products. A number of researchers and practitioners speculate that the blockchain technology can change the way we see a number of online applications today. Although it is still early to tell for sure, it is expected that the blockchain will stimulate considerable changes to a large number of products and will positively impact the digital experience of many individuals around the globe. In this tutorial, we overview, detail, and analyze the security provisions of Bitcoin and its underlying blockchain-effectively capturing recently reported attacks and threats in the system. Our contributions go beyond the mere analysis of reported vulnerabilities of Bitcoin; namely, we describe and evaluate a number of countermeasures to deter threats on the system-some of which have already been incorporated in the system. Recall that Bitcoin has been forked multiple times in order to fine-tune the consensus (i.e., the block generation time and the hash function), and the network parameters (e.g., the size of blocks). As such, the results reported in this tutorial are not only restricted to Bitcoin, but equally apply to a number of "altcoins" which are basically clones/forks of the Bitcoin source code. Given the increasing number of alternative blockchain proposals, this tutorial extracts the basic security lessons learnt from the Bitcoin system with the aim to foster better designs and analysis of next-generation secure blockchain currencies and technologies.

As DNS packet are mostly UDP-based, make it as a perfect tool for hackers to launch a well-known type of distributed denial of service (DDoS). The purpose of this attack is to saturate the DNS server availability and resources. This type of attack usually utilizes a large number of botnet and perform spoofing on the IP address of the targeted victim. We take a different approach for IP spoofing detection and mitigation strategies to protect the DNS server by utilizing Software Defined Networking (SDN). In this paper, we present CAuth, a novel mechanism that autonomously block the spoofing query packet while authenticate the legitimate query. By manipulating Openflow control message, we design a collaborative approach between client and server network. Whenever a server controller receives query packet, it will send an authentication packet back to the client network and later the client controller also replies via authentication packet back to the server controller. The server controller will only forward the query to the DNS server if it receives the replied authentication packet from the client. From the evaluation, CAuth instantly manage to block spoofing query packet while authenticate the legitimate query as soon as the mechanism started. Most notably, our mechanism designed with no changes in existing DNS application and Openflow protocol.

The state of network security today is quite abysmal. Security breaches and downtime of critical infrastructures continue to be the norm rather than the exception, despite the dramatic rise in spending on network security. Attackers today can easily leverage a distributed and programmable infrastructure of compromised machines (or botnets) to launch large-scale and sophisticated attack campaigns. In contrast, the defenders of our critical infrastructures are fundamentally crippled as they rely on fixed capacity, inflexible, and expensive hardware appliances deployed at designated "chokepoints". These primitive defense capabilities force defenders into adopting weak and static security postures configured for simple and known attacks, or otherwise risk user revolt, as they face unpleasant tradeoffs between false positives and false negatives. Unfortunately, attacks can easily evade these defenses; e.g., piggybacking on popular services (e.g., drive-by-downloads) and by overloading the appliances. Continuing along this trajectory means that attackers will always hold the upper hand as defenders are stifled by the inflexible and impotent tools in their arsenal. An overarching goal of my work is to change the dynamics of this attack-defense equation. Instead of taking a conventional approach of developing attack-specific defenses, I argue that we can leverage recent trends in software-defined networking and network functions virtualization to better empower defenders with the right tools and abstractions to tackle the constantly evolving attack landscape. To this end, I envision a new software-defined approach to network security, where we can rapidly develop and deploy novel in-depth defenses and dynamically customize the network's security posture to the current operating context. In this talk, I will give an overview of our recent work on the basic building blocks to enable this vision as well as some early security capabilities we have developed. Using anecdotes from this specific exercise, I will also try to highlight lessons and experiences in the overall research process (e.g., how to pick and formulate problems, the role of serendipity, and the benefits of finding ``bridges'' to other subdomains).

In many decades, due to fast growth of the World Wide Web, HTTP automated software/applications (auto-ware) are blooming for multiple purposes. Unfortunately, beside normal applications such as virus defining or operating system updating, auto-ware can also act as abnormal processes such as botnet, worms, virus, spywares, and advertising software (adware). Therefore, auto-ware, in a sense, consumes network bandwidth, and it might become internal security threats, auto-ware accessed domain/server also might be malicious one. Understanding about behaviour of HTTP auto-ware is beneficial for anomaly/malicious detection, the network management, traffic engineering and security. In this paper, HTTP auto-ware communication behaviour is analysed and modeled, from which a method in filtering out its domain/server is proposed. The filtered results can be used as a good resource for other security action purposes such as malicious domain/URL detection/filtering or investigation of HTTP malware from internal threats.

As today's networks become larger and more complex, the Distributed Denial of Service (DDoS) flooding attack threats may not only come from the outside of networks but also from inside, such as cloud computing network where exists multiple tenants possibly containing malicious tenants. So, the need of source-based defense mechanism against such attacks is pressing. In this paper, we mainly focus on the source-based defense mechanism against Botnet-based DDoS flooding attack through combining the power of Software-Defined Networking (SDN) and sample flow (sFlow) technology. Firstly, we defined a metric to measure the essential features of this kind attack which means distribution and collaboration. Then we designed a simple detection algorithm based on statistical inference model and response scheme through the abilities of SDN. Finally, we developed an application to realize our idea and also tested its effect on emulation network with real network traffic. The result shows that our mechanism could effectively detect DDoS flooding attack originated in SDN environment and identify attack flows for avoiding the harm of attack spreading to target or outside. We advocate the advantages of SDN in the area of defending DDoS attacks, because it is difficult and laborious to organize selfish and undisciplined traditional distributed network to confront well collaborative DDoS flooding attacks.

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.

Botnets have become one of the most significant cyber threats over the last decade. The diffusion of the "Internet of Things" and its for-profit exploitation, contributed to botnets spread and sophistication, thus providing real, efficient and profitable criminal cyber-services. Recent research on botnet detection focuses on traffic pattern-based detection, and on analyzing the network traffic generated by the infected hosts, in order to find behavioral patterns independent from the specific payloads, architectures and protocols. In this paper we address the periodic behavioral patterns of infected hosts communicating with their Command-and-Control servers. The main novelty introduced is related to the traffic analysis in the frequency domain without using the well-known Fast Fourier Transform. Moreover, the mentioned analysis is performed through the exploitation of the proxy logs, easily deployable on almost every real-world scenario, from enterprise networks to mobile devices.

Cognitive acoustic (CA) is emerging as a promising technique for spectrum-efficient Underwater Acoustic Networks (UANs). Due to the unique features of UANs, especially the long propagation delay, the busy terminal problem and large interference range, traditional spectrum decision methods used for radio networks need an overhaul to work efficiently in underwater environment. In this paper, we propose a dynamic spectrum decision algorithm called Receiver-viewed Dynamic Borrowing (RvDB) algorithm for Underwater Cognitive Acoustic Networks (UCANs) to improve the efficiency of spectrum utilization. RvDB algorithm is with the following features. Firstly, the spectrum resource is decided by receiver. Secondly, the receivers can borrow the idle spectrum resource from neighbouring nodes dynamically. Finally, the spectrum sensing is completed by control packets on control channel which is separated from data channels. Simulation results show that RvDB algorithm can greatly improve the performance on spectrum efficiency.

In this paper, a multi-channel medium access control (MAC) protocol is proposed to overcome the Large Interference Range Collision (LIRC) problem in underwater acoustic sensor networks (UWASNs), which has been known to occur when a handshaking based MAC protocol is jointly used with a power control. The proposed scheme divides the frequency band into two separate channels each used for control and data packets transmission. Considering the acoustic signal attenuation characteristics, higher frequency is used for data and lower frequency is used for control. And then, the data transmission power is controlled to escape the LIRC problem and simultaneously to save as much as possible. Furthermore with the separated channels, we can also reduce control-data packet collisions.

Distributed detection with data fusion has gained great attention in recent years. Collaborative detection improves the performance, and the optimal sensor deployment may change with time. It has been shown that with data fusion less sensors are needed to get the same detection ability when abundant sensors are deployed randomly. However, because of limitations on equipment number and deployment methods, fixed sensor locations may be preferred underwater. In this paper, we try to establish a theoretical framework for finding sensor positions to maximize the detection probability with a distributed sensor network. With joint data processing, detection performance is related to all the sensor locations; as sensor number grows, the optimization problem would become more difficult. To simplify the demonstration, we choose a 1-dimensional line deployment model and present the relevant numerical results.

Multiple-input multiple-output (MIMO) techniques have been the subject of increased attention for underwater acoustic communication for its ability to significantly improve the channel capabilities. Recently, an under-ice MIMO acoustic communication experiment was conducted in shallow water which differs from previous works in that the water column was covered by about 40 centimeters thick sea ice. In this experiment, high frequency MIMO signals centered at 10 kHz were transmitted from a two-element source array to a four-element vertical receive array at 1km range. The unique under-ice acoustic propagation environment in shallow water seems naturally separate data streams from different transducers, but there is still co-channel interference. Time reversal followed by a single channel decision feedback equalizer is used in this paper to compensate for the inter-symbol interference and co-channel interference. It is demonstrated that this simple receiver scheme is good enough to realize robust performance using fewer hydrophones (i.e. 2) without the explicit use of complex co-channel interference cancelation algorithms such as parallel interference cancelation or serial interference cancelation. Two channel estimation algorithms based on least square and least mean square are also studied for MIMO communications in this paper and their performance are compared using experimental data.

Currently, the coastal communication is mainly provided by satellite networks, which are expensive with low transmission rate and unable to support underwater communication efficiently. In this work, we propose a communication architecture for coastal network based on long term evolution (LTE) cellular networks in which a cellular network architecture is designed for the maritime communication scenario. Some key technologies of next-generation cellular networks such as device-to-device (D2D) and multiple input multiple output (MIMO) are integrated into the proposed architecture to support more efficient data transmission. In addition, over-water nodes aid the transmission of underwater network to improve the communication quality. With the proposed communication architecture, the coastal network can provide high-quality communication service to traffics with different quality-of-service (QoS) requirements.

The Earth is a water planet. The ocean is used for nature resource exploitation, fishery, etc., and it also plays critical roles in global climate regulation and transportation. Consequently, it is extremely important to keep track of its condition. And thus ocean observation systems have received increasing attentions.

In this work, a bi-directional transceiver with a maximum throughput of 24 kbps is presented. The spatio-temporal shallow water channel characteristics between a projector and a hydrophone array are analyzed in a seawater tank, and a methodology to maintain a 10−4 probability of bit error with prior knowledge of the channel statistics is proposed. Also, it is found that flow generated in the sea water provides a realistic representation of time-varying propagation conditions, particularly for the reverse link communication link at 22.5 kHz.

The local minima has been deemed as a challenging issue when designing routing protocols for 3D underwater sensor networks. Recently, harmonic potential field method has been used to tackle the issue of local minima which was also a major bottleneck in path planning and obstacle avoidance of robotics community. Inspired by this, this paper proposes a harmonic potential field based routing protocol for 3D underwater sensor networks with local minima. More specifically, the harmonic potential field is calculated using harmonic functions and Dirichlet boundary conditions are used for the local minima, sink(or seabuoy) and sending node. Numerical results show the effectiveness of the proposed routing protocol.

The ability to obtain reliable, long-term visual data in marine habitats has the potential to transform biological surveys of marine species. However, the underwater environment poses several challenges to visual monitoring: turbidity and light attenuation impede the range of optical sensors, biofouling clouds lenses and underwater housings, and marine species typically range over a large area, far outside of the range of a single camera sensor. Due to these factors, a continuously-recording or time-lapse visual sensor will not be gathering useful data the majority of the time, wasting battery life and filling limited onboard storage with useless images. These limitations make visual monitoring difficult in marine environments, but visual data is invaluable to biologists studying the behaviors and interactions of a species. This paper describes an acoustic-based, autonomous triggering approach to counter the current limitations of underwater visual sensing, and motivates the need for a distributed sensor network for underwater visual monitoring.

In this work, we develop an underwater relay network model for an unmanned cruise system. By introducing the underwater cruise, we analyze end-to-end outage performance for collecting data from a sensor node. Based on theoretical derivation of the outage probability, we further analyze the optimized location and data rate for relaying.

In this work, a magneto inductive (MI) link design is studied to achieve high speed transmission applied to a high density underwater network. For a small loop antenna, a design procedure is described to define the optimal operating frequency constrained on the system bandwidth and range. A coherent link is established between two nodes in a controlled underwater environment. For a small coil with radius of 5 cm, simulation results indicate that a range above 10 meters can be achieved in the low frequency spectrum spanning 10 kHz to 1 MHz. The design procedure is validated through measurements in seawater: a very high output SNR equal to 31.4 dB is realized at the output of the equalizer, and in these conditions a perfectly reliable 8-kbps link is demonstrated at a center frequency of 22.5 kHz.