Biblio

Keys for symmetric cryptography are usually stored in RAM and therefore susceptible to various attacks, ranging from simple buffer overflows to leaks via cold boot, DMA or side channels. A common approach to mitigate such attacks is to move the keys to an external cryptographic token. For low-throughput applications like asymmetric signature generation, the performance of these tokens is sufficient. For symmetric, data-intensive use cases, like disk encryption on behalf of the host, the connecting interface to the token often is a serious bottleneck. In order to overcome this problem, we present CoKey, a novel concept for partially moving symmetric cryptography out of the host into a trusted detachable token. CoKey combines keys from both entities and securely encrypts initialization vectors on the token which are then used in the cryptographic operations on the host. This forces host and token to cooperate during the whole encryption and decryption process. Our concept strongly and efficiently binds encrypted data on the host to the specific token used for their encryption, while still allowing for fast operation. We implemented the concept using Linux hosts and the USB armory, a USB thumb drive sized ARM computer, as detachable crypto token. Our detailed performance evaluation shows that our prototype is easily fast enough even for data-intensive and performance-critical use cases like full disk encryption, thus effectively improving security for symmetric cryptography in a usable way.

We study coding schemes for multiparty interactive communication over synchronous networks that suffer from stochastic noise, where each bit is independently flipped with probability ε. We analyze the minimal overhead that must be added by the coding scheme in order to succeed in performing the computation despite the noise. Our main result is a lower bound on the communication of any noise-resilient protocol over a synchronous star network with n-parties (where all parties communicate in every round). Specifically, we show a task that can be solved by communicating T bits over the noise-free network, but for which any protocol with success probability of 1-o(1) must communicate at least Ω(T log n / log log n) bits when the channels are noisy. By a 1994 result of Rajagopalan and Schulman, the slowdown we prove is the highest one can obtain on any topology, up to a log log n factor. We complete our lower bound with a matching coding scheme that achieves the same overhead; thus, the capacity of (synchronous) star networks is Θ(log log n / log n). Our bounds prove that, despite several previous coding schemes with rate Ω(1) for certain topologies, no coding scheme with constant rate Ω(1) exists for arbitrary n-party noisy networks.

We study coding schemes for multiparty interactive communication over synchronous networks that suffer from stochastic noise, where each bit is independently flipped with probability ε. We analyze the minimal overhead that must be added by the coding scheme in order to succeed in performing the computation despite the noise. Our main result is a lower bound on the communication of any noise-resilient protocol over a synchronous star network with n-parties (where all parties communicate in every round). Specifically, we show a task that can be solved by communicating T bits over the noise-free network, but for which any protocol with success probability of 1-o(1) must communicate at least Ω(T log n / log log n) bits when the channels are noisy. By a 1994 result of Rajagopalan and Schulman, the slowdown we prove is the highest one can obtain on any topology, up to a log log n factor. We complete our lower bound with a matching coding scheme that achieves the same overhead; thus, the capacity of (synchronous) star networks is Θ(log log n / log n). Our bounds prove that, despite several previous coding schemes with rate Ω(1) for certain topologies, no coding scheme with constant rate Ω(1) exists for arbitrary n-party noisy networks.

In most adaptive video streaming systems adaptation decisions rely solely on the available network resources. As the content of a video has a large influence on the perception of quality our belief is that this is not sufficient. Thus, we have proposed a support service for content-aware video adaptation on mobile devices: Video Adaptation Service (VAS). Based on the content of a streamed video, the adaptation process is improved by setting a target quality level for a session based on an objective video quality metric. In this work, we demonstrate VAS and its advantages of a reduced data traffic by only streaming the lowest video representation which is necessary to reach a desired quality. By leveraging the content properties of a video stream, the system is able to keep a stable video quality and at the same time reduce the network load.

Critical infrastructure such as the power grid has become increasingly complex. The addition of computing elements to traditional physical components increases complexity and hampers insight into how elements in the system interact with each other. The result is an infrastructure where operational mistakes, some of which cannot be distinguished from attacks, are more difficult to prevent and have greater potential impact, such as leaking sensitive information to the operator or attacker. In this paper, we present CPAC, a cyber-physical access control solution to manage complexity and mitigate threats in cyber-physical environments, with a focus on the electrical smart grid. CPAC uses information flow analysis based on mathematical models of the physical grid to generate policies enforced through verifiable logic. At the device side, CPAC combines symbolic execution with lightweight dynamic execution monitoring to allow non-intrusive taint analysis on programmable logic controllers in realtime. These components work together to provide a realtime view of all system elements, and allow for more robust and finer-grained protections than any previous solution to securing the grid. We implement a prototype of CPAC using Bachmann PLCs and evaluate several real-world incidents that demonstrate its scalability and effectiveness. The policy checking for a nation-wide grid is less than 150 ms, faster than existing solutions. We additionally show that CPAC can analyze potential component failures for arbitrary component failures, far beyond the capabilities of currently deployed systems. CPAC thus provides a solution to secure the modern smart grid from operator mistakes or insider attacks, maintain operational privacy, and support N - x contingencies.

Mobile applications - or apps - are one of the main reasons for the unprecedented success smart phones and tablets have experienced over the last decade. Apps are the main interfaces that users deal with when engaging in online banking, checking travel itineraries, or browsing their social network profiles while on the go. Previous research has studied various aspects of mobile application security including data leakage and privilege escalation through confused deputy attacks. However, the vast majority of mobile application research targets Google's Android platform. Few research papers analyze iOS applications and those that focus on the Apple environment perform their analysis on comparatively small datasets (i.e., thousands in iOS vs. hundreds of thousands in Android). As these smaller datasets call into question how representative the gained results are, we propose, implement, and evaluate CRiOS, a fully-automated system that allows us to amass comprehensive datasets of iOS applications which we subject to large-scale analysis. To advance academic research into the iOS platform and its apps, we plan on releasing CRiOS as an open source project. We also use CRiOS to aggregate a dataset of 43,404 iOS applications. Equipped with this dataset we analyze the collected apps to identify third-party libraries that are common among many applications. We also investigate the network communication endpoints referenced by the applications with respect to the endpoints' correct use of TLS/SSL certificates. In summary, we find that the average iOS application consists of 60.2% library classes and only 39.8% developer-authored content. Furthermore, we find that 9.32% of referenced network connection endpoints either entirely omit to cryptographically protect network communications or present untrustworthy SSL certificates.

We propose a new security paradigm that makes cross-layer personalization a premier component in the design of security solutions for computer infrastructure and situational awareness. This paradigm is based on the observation that computer systems have a personalized usage profile that depends on the user and his activities. Further, it spans the various layers of abstraction that make up a computer system, as if the user embedded his own DNA into the computer system. To realize such a paradigm, we discuss the design of a comprehensive and cross-layer profiling approach, which can be adopted to boost the effectiveness of various security solutions, e.g., malware detection, insider attacker prevention and continuous authentication. The current state-of-the-art in computer infrastructure defense solutions focuses on one layer of operation with deployments coming in a "one size fits all" format, without taking into account the unique way people use their computers. The key novelty of our proposal is the cross-layer personalization, where we derive the distinguishable behaviors from the intelligence of three layers of abstraction. First, we combine intelligence from: a) the user layer, (e.g., mouse click patterns); b) the operating system layer; c) the network layer. Second, we develop cross-layer personalized profiles for system usage. We will limit our scope to companies and organizations, where computers are used in a more routine and one-on-one style, before we expand our research to personally owned computers. Our preliminary results show that just the time accesses in user web logs are already sufficient to distinguish users from each other,with users of the same demographics showing similarities in their profiles. Our goal is to challenge today's paradigm for anomaly detection that seems to follow a monoculture and treat each layer in isolation. We also discuss deployment, performance overhead, and privacy issues raised by our paradigm.

One of the main concerns for smartphone users is the quality of apps they download. Before installing any app from the market, users first check its rating and reviews. However, these ratings are not computed by experts and most times are not associated with malicious behavior. In this work, we present an IDS/rating system based on a game theoretic model with crowdsourcing. Our results show that, with minor control over the error in categorizing users and the fraction of experts in the crowd, our system provides proper ratings while flagging all malicious apps.

Energy management systems (EMS) are used to control energy usage in buildings and campuses, by employing technologies such as supervisory control and data acquisition (SCADA) and building management systems (BMS), in order to provide reliable energy supply and maximise user comfort while minimising energy usage. Historically, EMS systems were installed when potential security threats were only physical. Nowadays, EMS systems are connected to the building network and as a result directly to the outside world. This extends the attack surface to potential sophisticated cyber-attacks, which adversely impact EMS operation, resulting in service interruption and downstream financial implications. Currently, the security systems that detect attacks operate independently to those which deploy resiliency policies and use very basic methods. We propose a novel EMS cyber-physical-security framework that executes a resilient policy whenever an attack is detected using security analytics. In this framework, both the resilient policy and the security analytics are driven by EMS data, where the physical correlations between the data-points are identified to detect outliers and then the control loop is closed using an estimated value in place of the outlier. The framework has been tested using a reduced order model of a real EMS site.

The usual approach to security for cloud-hosted applications is strong separation. However, it is often the case that the same data is used by different applications, particularly given the increase in data-driven (`big data' and IoT) applications. We argue that access control for the cloud should no longer be application-specific but should be data-centric, associated with the data that can flow between applications. Indeed, the data may originate outside cloud services from diverse sources such as medical monitoring, environmental sensing etc. Information Flow Control (IFC) potentially offers data-centric, system-wide data access control. It has been shown that IFC can be provided at operating system level as part of a PaaS offering, with an acceptable overhead. In this paper we consider how IFC can be integrated with application-specific access control, transparently from application developers, while building from simple IFC primitives, access control policies that align with the data management obligations of cloud providers and tenants.

We consider the following natural generalization of Binary Search: in a given undirected, positively weighted graph, one vertex is a target. The algorithm’s task is to identify the target by adaptively querying vertices. In response to querying a node q, the algorithm learns either that q is the target, or is given an edge out of q that lies on a shortest path from q to the target. We study this problem in a general noisy model in which each query independently receives a correct answer with probability p textgreater 1/2 (a known constant), and an (adversarial) incorrect one with probability 1 − p. Our main positive result is that when p = 1 (i.e., all answers are correct), log2 n queries are always sufficient. For general p, we give an (almost information-theoretically optimal) algorithm that uses, in expectation, no more than (1 − δ) logn/1 − H(p) + o(logn) + O(log2 (1/δ)) queries, and identifies the target correctly with probability at leas 1 − δ. Here, H(p) = −(p logp + (1 − p) log(1 − p)) denotes the entropy. The first bound is achieved by the algorithm that iteratively queries a 1-median of the nodes not ruled out yet; the second bound by careful repeated invocations of a multiplicative weights algorithm. Even for p = 1, we show several hardness results for the problem of determining whether a target can be found using K queries. Our upper bound of log2 n implies a quasipolynomial-time algorithm for undirected connected graphs; we show that this is best-possible under the Strong Exponential Time Hypothesis (SETH). Furthermore, for directed graphs, or for undirected graphs with non-uniform node querying costs, the problem is PSPACE-complete. For a semi-adaptive version, in which one may query r nodes each in k rounds, we show membership in Σ2k−1 in the polynomial hierarchy, and hardness for Σ2k−5.

This paper proposes the Digital Public Service Innovation Framework that extends the "standard" provision of digital public services according to the emerging, enhanced, transactional and connected stages underpinning the United Nations Global e-Government Survey, with seven example "innovations" in digital public service delivery – transparent, participatory, anticipatory, personalized, co-created, context-aware and context-smart. Unlike the "standard" provisions, innovations in digital public service delivery are open-ended – new forms may continuously emerge in response to new policy demands and technological progress, and are non-linear – one innovation may or may not depend on others. The framework builds on the foundations of public sector innovation and Digital Government Evolution model. In line with the latter, the paper equips each innovation with sharp logical characterization, body of research literature and real-life cases from around the world to simultaneously serve the illustration and validation goals. The paper also identifies some policy implications of the framework, covering a broad range of issues from infrastructure, capacity, eco-system and partnerships, to inclusion, value, channels, security, privacy and authentication.

Enormous amount of educational data has been accumulated through Massive Open Online Courses (MOOCs), as well as commercial and non-commercial learning platforms. This is in addition to the educational data released by US government since 2012 to facilitate disruption in education by making data freely available. The high volume, variety and velocity of collected data necessitate use of big data tools and storage systems such as distributed databases for storage and Apache Spark for analysis. This tutorial will introduce researchers and faculty to real-world applications involving data mining and predictive analytics in learning sciences. In addition, the tutorial will introduce statistics required to validate and accurately report results. Topics will cover how big data is being used to transform education. Specifically, we will demonstrate how exploratory data analysis, data mining, predictive analytics, machine learning, and visualization techniques are being applied to educational big data to improve learning and scale insights driven from millions of student's records. The tutorial will be held over a half day and will be hands on with pre-posted material. Due to the interdisciplinary nature of work, the tutorial appeals to researchers from a wide range of backgrounds including big data, predictive analytics, learning sciences, educational data mining, and in general, those interested in how big data analytics can transform learning. As a prerequisite, attendees are required to have familiarity with at least one programming language.

Failing to properly isolate components in the same address space has resulted in a substantial amount of vulnerabilities. Enforcing the least privilege principle for memory accesses can selectively isolate software components to restrict attack surface and prevent unintended cross-component memory corruption. However, the boundaries and interactions between software components are hard to reason about and existing approaches have failed to stop attackers from exploiting vulnerabilities caused by poor isolation. We present the secure memory views (SMV) model: a practical and efficient model for secure and selective memory isolation in monolithic multithreaded applications. SMV is a third generation privilege separation technique that offers explicit access control of memory and allows concurrent threads within the same process to partially share or fully isolate their memory space in a controlled and parallel manner following application requirements. An evaluation of our prototype in the Linux kernel (TCB textless 1,800 LOC) shows negligible runtime performance overhead in real-world applications including Cherokee web server (textless 0.69%), Apache httpd web server (textless 0.93%), and Mozilla Firefox web browser (textless 1.89%) with at most 12 LOC changes.

Software development is often accompanied by security audits such as penetration tests, usually performed on behalf of the software vendor. In penetration tests security experts identify entry points for attacks in a software product. Many development teams undergo such audits for the first time if their product is attacked or faces new security concerns. The audits often serve as an eye-opener for development teams: they realize that security requires much more attention. However, there is a lack of clarity with regard to what lasting benefits developers can reap from penetration tests. We report from a one-year study of a penetration test run at a major software vendor, and describe how a software development team managed to incorporate the test findings. Results suggest that penetration tests improve developers' security awareness, but that long-lasting enhancements of development practices are hampered by a lack of dedicated security stakeholders and if security is not properly reflected in the communicative and collaborative structures of the organization.

In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to Łukasiewicz logic. The MaxSAT problem for a set of formulae Φ is the problem of finding an assignment to the variables in Φ that satisfies the maximum number of formulae. Three possible solutions (encodings) are proposed to the new problem: (1) Disjunctive Linear Relations (DLRs), (2)Mixed Integer Linear Programming (MILP) and (3)Weighted Constraint Satisfaction Problem (WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have numerous applications in optimization problems that involve vagueness.

Modern static bug finding tools are complex. They typically consist of hundreds of thousands of lines of code, and most of them are wedded to one language (or even one compiler). This complexity makes the systems hard to understand, hard to debug, and hard to retarget to new languages, thereby dramatically limiting their scope. This paper reduces checking system complexity by addressing a fundamental assumption, the assumption that checkers must depend on a full-blown language specification and compiler front end. Instead, our program checkers are based on drastically incomplete language grammars ("micro-grammars") that describe only portions of a language relevant to a checker. As a result, our implementation is tiny-roughly 2500 lines of code, about two orders of magnitude smaller than a typical system. We hope that this dramatic increase in simplicity will allow people to use more checkers on more systems in more languages. We implement our approach in μchex, a language-agnostic framework for writing static bug checkers. We use it to build micro-grammar based checkers for six languages (C, the C preprocessor, C++, Java, JavaScript, and Dart) and find over 700 errors in real-world projects.

Human mobility is one of the key topics to be considered in the networks of the future, both by industrial and research communities that are already focused on multidisciplinary applications and user-centric systems. If the rapid proliferation of networks and high-tech miniature sensors makes this reality possible, the ever-growing complexity of the metrics and parameters governing such systems raises serious issues in terms of privacy, security and computing capability. In this demonstration, we show a new system, able to estimate a user's mobility profile based on anonymized and lightweight smartphone data. In particular, this system is composed of (1) a web analytics platform, able to analyze multimodal sensing traces and improve our understanding of complex mobility patterns, and (2) a smartphone application, able to show a user's profile generated locally in the form of a spider graph. In particular, this application uses anonymized and privacy-friendly data and methods, obtained thanks to the combination of Wi-Fi traces, activity detection and graph theory, made available independent of any personal information. A video showing the different interfaces to be presented is available online.