Biblio

For single-owner multi-user wireless sensor networks, there is the demand to implement the user privacy-preserving access control protocol in WSNs. Firstly, we propose a new access control protocol based on an efficient attribute-based signature. In the protocol, users need to pay for query, and the protocol achieves fine-grained access control and privacy protection. Then, the protocol is analyzed in detail. Finally, the comparison of protocols indicates that our scheme is more efficient. Our scheme not only protects the privacy of users and achieves fine-grained access control, but also provides the query command validation with low overhead. The scheme can better satisfy the access control requirements of wireless sensor networks.

Mobile code distribution relies on digital signatures to guarantee code authenticity. Unfortunately, standard signature schemes are not well suited for use in conjunction with program transformation techniques, such as aspect-oriented programming. With these techniques, code development is performed in sequence by multiple teams of programmers. This is fundamentally different from traditional single-developer/ single-user models, where users can verify end-to-end (i.e., developer-to-user) authenticity of the code using digital signatures. To address this limitation, we introduce FLEX, a flexible code authentication framework for mobile applications. FLEX allows semi-trusted intermediaries to modify mobile code without invalidating the developer's signature, as long as the modification complies with a "contract" issued by the developer. We introduce formal definitions for secure code modification, and show that our instantiation of FLEX is secure under these definitions. Although FLEX can be instantiated using any language, we design AMJ–a novel programming language that supports code annotations–and implement a FLEX prototype based on our new language.

The deterministic nature of existing routing protocols has resulted into an ossified Internet with static and predictable network routes. This gives persistent attackers (e.g. eavesdroppers and DDoS attackers) plenty of time to study the network and identify the vulnerable (critical) links to plan devastating and stealthy attacks. Recently, Moving Target Defense (MTD) based approaches have been proposed to to defend against DoS attacks. However, MTD based approaches for route mutation are oriented towards re-configuring the parameters in Local Area Networks (LANs), and do not provide any protection against infrastructure level attacks, which inherently limits their use for mission critical services over the Internet infrastructure. To cope with these issues, we extend the current routing architecture to consider end-hosts as routing elements, and present a formal method based agile defense mechanism to embed resiliency in the existing cyber infrastructure. The major contributions of this paper include: (1) formalization of efficient and resilient End to End (E2E) reachability problem as a constraint satisfaction problem, which identifies the potential end-hosts to reach a destination while satisfying resilience and QoS constraints, (2) design and implementation of a novel decentralized End Point Route Mutation (EPRM) protocol, and (3) design and implementation of planning algorithm to minimize the overlap between multiple flows, for the sake of maximizing the agility in the system. Our PlanetLab based implementation and evaluation validates the correctness, effectiveness and scalability of the proposed approach.

This paper presents a framework for (1) generating variants of known attacks, (2) replaying attack variants in an isolated environment and, (3) validating detection capabilities of attack detection techniques against the variants. Our framework facilitates reproducible security experiments. We generated 648 variants of three real-world attacks (observed at the National Center for Supercomputing Applications at the University of Illinois). Our experiment showed the value of generating attack variants by quantifying the detection capabilities of three detection methods: a signature-based detection technique, an anomaly-based detection technique, and a probabilistic graphical model-based technique.

We propose a modular framework which deploys state-of-the art techniques in dynamic pattern matching as well as machine learning algorithms for Big Data predictive and be-havioural analytics to detect threats and attacks in Managed File Transfer and collaboration platforms. We leverage the use of the kill chain model by looking for indicators of compromise either for long-term attacks as Advanced Persistent Threats, zero-day attacks or DDoS attacks. The proposed engine can act complimentary to existing security services as SIEMs, IDS, IPS and firewalls.

The proliferation of wearable devices, e.g., smartwatches and activity trackers, with embedded sensors has already shown its great potential on monitoring and inferring human daily activities. This paper reveals a serious security breach of wearable devices in the context of divulging secret information (i.e., key entries) while people accessing key-based security systems. Existing methods of obtaining such secret information relies on installations of dedicated hardware (e.g., video camera or fake keypad), or training with labeled data from body sensors, which restrict use cases in practical adversary scenarios. In this work, we show that a wearable device can be exploited to discriminate mm-level distances and directions of the user's fine-grained hand movements, which enable attackers to reproduce the trajectories of the user's hand and further to recover the secret key entries. In particular, our system confirms the possibility of using embedded sensors in wearable devices, i.e., accelerometers, gyroscopes, and magnetometers, to derive the moving distance of the user's hand between consecutive key entries regardless of the pose of the hand. Our Backward PIN-Sequence Inference algorithm exploits the inherent physical constraints between key entries to infer the complete user key entry sequence. Extensive experiments are conducted with over 5000 key entry traces collected from 20 adults for key-based security systems (i.e. ATM keypads and regular keyboards) through testing on different kinds of wearables. Results demonstrate that such a technique can achieve 80% accuracy with only one try and more than 90% accuracy with three tries, which to our knowledge, is the first technique that reveals personal PINs leveraging wearable devices without the need for labeled training data and contextual information.

Lattice-based cryptography offers some of the most attractive primitives believed to be resistant to quantum computers. Following increasing interest from both companies and government agencies in building quantum computers, a number of works have proposed instantiations of practical post-quantum key exchange protocols based on hard problems in ideal lattices, mainly based on the Ring Learning With Errors (R-LWE) problem. While ideal lattices facilitate major efficiency and storage benefits over their non-ideal counterparts, the additional ring structure that enables these advantages also raises concerns about the assumed difficulty of the underlying problems. Thus, a question of significant interest to cryptographers, and especially to those currently placing bets on primitives that will withstand quantum adversaries, is how much of an advantage the additional ring structure actually gives in practice. Despite conventional wisdom that generic lattices might be too slow and unwieldy, we demonstrate that LWE-based key exchange is quite practical: our constant time implementation requires around 1.3ms computation time for each party; compared to the recent NewHope R-LWE scheme, communication sizes increase by a factor of 4.7x, but remain under 12 KiB in each direction. Our protocol is competitive when used for serving web pages over TLS; when partnered with ECDSA signatures, latencies increase by less than a factor of 1.6x, and (even under heavy load) server throughput only decreases by factors of 1.5x and 1.2x when serving typical 1 KiB and 100 KiB pages, respectively. To achieve these practical results, our protocol takes advantage of several innovations. These include techniques to optimize communication bandwidth, dynamic generation of public parameters (which also offers additional security against backdoors), carefully chosen error distributions, and tight security parameters.

At the core of its nature, security is a highly contextual and dynamic challenge. However, current security policy approaches are usually static, and slow to adapt to ever-changing requirements, let alone catching up with reality. In a 2012 Sophos survey, it was stated that a unique malware is created every half a second. This gives a glimpse of the unsustainable nature of a global problem, any improvement in terms of closing the "time window to adapt" would be a significant step forward. To exacerbate the situation, a simple change in threat and attack vector or even an implementation of the so-called "bring-your-own-device" paradigm will greatly change the frequency of changed security requirements and necessary solutions required for each new context. Current security policies also typically overlook the direct and indirect costs of implementation of policies. As a result, technical teams often fail to have the ability to justify the budget to the management, from a business risk viewpoint. This paper considers both the adaptive and cost-benefit aspects of security, and introduces a novel context-aware technique for designing and implementing adaptive, optimized security policies. Our approach leverages the capabilities of stochastic programming models to optimize security policy planning, and our preliminary results demonstrate a promising step towards proactive, context-aware security policies.

The overarching vision of social machines is to facilitate social processes by having computers provide administrative support. We conceive of a social machine as a sociotechnical system (STS): a software-supported system in which autonomous principals such as humans and organizations interact to exchange information and services. Existing approaches for social machines emphasize the technical aspects and inadequately support the meanings of social processes, leaving them informally realized in human interactions. We posit that a fundamental rethinking is needed to incorporate accountability, essential for addressing the openness of the Web and the autonomy of its principals. We introduce Interaction-Oriented Software Engineering (IOSE) as a paradigm expressly suited to capturing the social basis of STSs. Motivated by promoting openness and autonomy, IOSE focuses not on implementation but on social protocols, specifying how social relationships, characterizing the accountability of the concerned parties, progress as they interact. Motivated by providing computational support, IOSE adopts the accountability representation to capture the meaning of a social machine's states and transitions.

We demonstrate IOSE via examples drawn from healthcare. We reinterpret the classical software engineering (SE) principles for the STS setting and show how IOSE is better suited than traditional software engineering for supporting social processes. The contribution of this paper is a new paradigm for STSs, evaluated via conceptual analysis.

In this era of information explosion, conflicts are often encountered when information is provided by multiple sources. Traditional truth discovery task aims to identify the truth the most trustworthy information, from conflicting sources in different scenarios. In this kind of tasks, truth is regarded as a fixed value or a set of fixed values. However, in a number of real-world cases, objective truth existence cannot be ensured and we can only identify single or multiple reliable facts from opinions. Different from traditional truth discovery task, we address this uncertainty and introduce the concept of trustworthy opinion of an entity, treat it as a random variable, and use its distribution to describe consistency or controversy, which is particularly difficult for data which can be numerically measured, i.e. quantitative information. In this study, we focus on the quantitative opinion, propose an uncertainty-aware approach called Kernel Density Estimation from Multiple Sources (KDEm) to estimate its probability distribution, and summarize trustworthy information based on this distribution. Experiments indicate that KDEm not only has outstanding performance on the classical numeric truth discovery task, but also shows good performance on multi-modality detection and anomaly detection in the uncertain-opinion setting.

We construct the first fully succinct garbling scheme for RAM programs, assuming the existence of indistinguishability obfuscation for circuits and one-way functions. That is, the size, space requirements, and runtime of the garbled program are the same as those of the input program, up to poly-logarithmic factors and a polynomial in the security parameter. The scheme can be used to construct indistinguishability obfuscators for RAM programs with comparable efficiency, at the price of requiring sub-exponential security of the underlying primitives. In particular, this opens the door to obfuscated computations that are sublinear in the length of their inputs. The scheme builds on the recent schemes of Koppula-Lewko-Waters and Canetti-Holmgren-Jain-Vaikuntanathan [STOC 15]. A key technical challenge here is how to combine the fixed-prefix technique of KLW, which was developed for deterministic programs, with randomized Oblivious RAM techniques. To overcome that, we develop a method for arguing about the indistinguishability of two obfuscated randomized programs that use correlated randomness. Along the way, we also define and construct garbling schemes that offer only partial protection. These may be of independent interest.

To ensure the authenticity and integrity, data are traditionally signed by digital signatures, which will be invalidated by any processing of the data. With the vast amount of data generated every day, it is however desirable to allow flexible processing of the signed data via applying computations or functions on them, without losing the authenticity. Signatures can also serve as credentials for access control, which appears in many aspects of life, ranging from unlocking security gates of buildings, to virtual access of data by computer programs. With the prolific use of Internet-of-Things (IoT), everything is getting connected together. There is an emerging need for more versatile credentials to secure new application scenarios, for instance, assigning different credentials to different devices, such that they can authenticate and cooperate with each other to jointly perform some computation tasks. To realize the above, we envision a general framework called functional credentials. Functional credentials allow multiple entities to (jointly) issue, combine, delegate, present, verify, escrow, and decrypt different forms of credentials, by operating on the associated "cryptographic objects" including secret keys, attributes, ciphertexts, and auxiliary data (e.g., pseudonym, expiry date, or policies for combination / delegation / revocation). Instantiating this framework with different functions can provide a spectrum of solutions for securing IoT. This talk covers both the practical applications and theoretic foundations. I will first motivate the versatility of functional credentials by case studies on IoT, which identify the need of new credential systems. I will then formulate the definition of functional credentials. Finally, I will share some initial ideas in realizing functional credentials, and discuss the obstacles ahead.

A new dataset is presented composed of music identification matches from Gracenote, a leading global music metadata company. Matches from January 1, 2014 to December 31, 2014 have been curated and made available as a public dataset called Gracenote Music Identification 2014, or GNMID14, at the following address: https://developer.gracenote.com/mid2014. This collection is the first significant music identification dataset and one of the largest music related datasets available containing more than 110M matches in 224 countries for 3M unique tracks, and 509K unique artists. It features geotemporal information (i.e. country and match date), genre and mood metadata. In this paper, we characterize the dataset and demonstrate its utility for Information Retrieval (IR) research.

In this paper, we propose a new approach to diagnosing problems in complex distributed systems. Our approach is based on the insight that many of the trickiest problems are anomalies. For instance, in a network, problems often affect only a small fraction of the traffic (e.g., perhaps a certain subnet), or they only manifest infrequently. Thus, it is quite common for the operator to have “examples” of both working and non-working traffic readily available – perhaps a packet that was misrouted, and a similar packet that was routed correctly. In this case, the cause of the problem is likely to be wherever the two packets were treated differently by the network. We present the design of a debugger that can leverage this information using a novel concept that we call differential provenance. Differential provenance tracks the causal connections between network states and state changes, just like classical provenance, but it can additionally perform root-cause analysis by reasoning about the differences between two provenance trees. We have built a diagnostic tool that is based on differential provenance, and we have used our tool to debug a number of complex, realistic problems in two scenarios: software-defined networks and MapReduce jobs. Our results show that differential provenance can be maintained at relatively low cost, and that it can deliver very precise diagnostic information; in many cases, it can even identify the precise root cause of the problem.

After more than a decade of research, web application security continues to be a challenge and the backend database the most appetizing target. The paper proposes preventing injection attacks against the database management system (DBMS) behind web applications by embedding protections in the DBMS itself. The motivation is twofold. First, the approach of embedding protections in operating systems and applications running on top of them has been effective to protect this software. Second, there is a semantic mismatch between how SQL queries are believed to be executed by the DBMS and how they are actually executed, leading to subtle vulnerabilities in prevention mechanisms. The approach – SEPTIC – was implemented in MySQL and evaluated experimentally with web applications written in PHP and Java/Spring. In the evaluation SEPTIC has shown neither false negatives nor false positives, on the contrary of alternative approaches, causing also a low performance overhead in the order of 2.2%.

The orthodox paradigm to defend against automated social-engineering attacks in large-scale socio-technical systems is reactive and victim-agnostic. Defenses generally focus on identifying the attacks/attackers (e.g., phishing emails, social-bot infiltrations, malware offered for download). To change the status quo, we propose to identify, even if imperfectly, the vulnerable user population, that is, the users that are likely to fall victim to such attacks. Once identified, information about the vulnerable population can be used in two ways. First, the vulnerable population can be influenced by the defender through several means including: education, specialized user experience, extra protection layers and watchdogs. In the same vein, information about the vulnerable population can ultimately be used to fine-tune and reprioritize defense mechanisms to offer differentiated protection, possibly at the cost of additional friction generated by the defense mechanism. Secondly, information about the user population can be used to identify an attack (or compromised users) based on differences between the general and the vulnerable population. This paper considers the implications of the proposed paradigm on existing defenses in three areas (phishing of user credentials, malware distribution and socialbot infiltration) and discusses how using knowledge of the vulnerable population can enable more robust defenses.

Cyber Threat Intelligence (CTI) sharing facilitates a comprehensive understanding of adversary activity and enables enterprise networks to prioritize their cyber defense technologies. To that end, we introduce HogMap, a novel software-defined infrastructure that simplifies and incentivizes collaborative measurement and monitoring of cyber-threat activity. HogMap proposes to transform the cyber-threat monitoring landscape by integrating several novel SDN-enabled capabilities: (i) intelligent in-place filtering of malicious traffic, (ii) dynamic migration of interesting and extraordinary traffic and (iii) a software-defined marketplace where various parties can opportunistically subscribe to and publish cyber-threat intelligence services in a flexible manner. We present the architectural vision and summarize our preliminary experience in developing and operating an SDN-based HoneyGrid, which spans three enterprises and implements several of the enabling capabilities (e.g., traffic filtering, traffic forwarding and connection migration). We find that SDN technologies greatly simplify the design and deployment of such globally distributed and elastic HoneyGrids.

Proofs of Data Possession/Retrievability (PoDP/PoR) schemes are essential to cloud storage services, since they can increase clients' confidence on the integrity and availability of their data. The majority of PoDP/PoR schemes are constructed from homomorphic linear authentication (HLA) schemes, which decrease the price of communication between the client and the server. In this paper, a new subclass of authentication codes, named ε-authentication codes, is proposed, and a modular construction of HLA schemes from ε-authentication codes is presented. We prove that the security notions of HLA schemes are closely related to the size of the authenticator/tag space and the successful probability of impersonation attacks (with non-zero source states) of the underlying ε-authentication codes. We show that most of HLA schemes used for the PoDP/PoR schemes are instantiations of our modular construction from some ε-authentication codes. Following this line, an algebraic-curves-based ε-authentication code yields a new HLA scheme.

The surprising success of cryptocurrencies has led to a surge of interest in deploying large scale, highly robust, Byzantine fault tolerant (BFT) protocols for mission-critical applications, such as financial transactions. Although the conventional wisdom is to build atop a (weakly) synchronous protocol such as PBFT (or a variation thereof), such protocols rely critically on network timing assumptions, and only guarantee liveness when the network behaves as expected. We argue these protocols are ill-suited for this deployment scenario. We present an alternative, HoneyBadgerBFT, the first practical asynchronous BFT protocol, which guarantees liveness without making any timing assumptions. We base our solution on a novel atomic broadcast protocol that achieves optimal asymptotic efficiency. We present an implementation and experimental results to show our system can achieve throughput of tens of thousands of transactions per second, and scales to over a hundred nodes on a wide area network. We even conduct BFT experiments over Tor, without needing to tune any parameters. Unlike the alternatives, HoneyBadgerBFT simply does not care about the underlying network.