Biblio

Time and cost are the most critical performance metrics for computer systems including embedded system, especially for the battery-based embedded systems, such as PC, mainframe computer, and smart phone. Most of the previous work focuses on saving energy in a deterministic way by taking the average or worst scenario into account. However, such deterministic approaches usually are inappropriate in modeling energy consumption because of uncertainties in conditional instructions on processors and time-varying external environments. Through studying the relationship between energy consumption, execution time and completion probability of tasks on heterogeneous multi-core architectures this paper proposes an optimal energy efficiency and system performance model and the OTHAP (Optimizing Task Heterogeneous Assignment with Probability) algorithm to address the Processor and Voltage Assignment with Probability (PVAP) problem of data-dependent aperiodic tasks in real-time embedded systems, ensuring that all the tasks can be done under the time constraint with areal-time embedded systems guaranteed probability. We adopt a task DAG (Directed Acyclic Graph) to model the PVAP problem. We first use a processor scheduling algorithm to map the task DAG onto a set of voltage-variable processors, and then use our dynamic programming algorithm to assign a proper voltage to each task and The experimental results demonstrate our approach outperforms state-of-the-art algorithms in this field (maximum improvement of 24.6%).

Ideally, minimizing the flow completion time (FCT) requires millions of priorities supported by the underlying network so that each flow has its unique priority. However, in production datacenters, the available switch priority queues for flow scheduling are very limited (merely 2 or 3). This practical constraint seriously degrades the performance of previous approaches. In this paper, we introduce Explicit Priority Notification (EPN), a novel scheduling mechanism which emulates fine-grained priorities (i.e., desired priorities or DP) using only two switch priority queues. EPN can support various flow scheduling disciplines with or without flow size information. We have implemented EPN on commodity switches and evaluated its performance with both testbed experiments and extensive simulations. Our results show that, with flow size information, EPN achieves comparable FCT as pFabric that requires clean-slate switch hardware. And EPN also outperforms TCP by up to 60.5% if it bins the traffic into two priority queues according to flow size. In information-agnostic setting, EPN outperforms PIAS with two priority queues by up to 37.7%. To the best of our knowledge, EPN is the first system that provides millions of priorities for flow scheduling with commodity switches.

We address the problem of object tracking in an underwater acoustic sensor network in which distributed nodes measure the strength of field generated by moving objects, encode the measurements into digital data packets, and transmit the packets to a fusion center in a random access manner. We allow for imperfect communication links, where information packets may be lost due to noise and collisions. The packets that are received correctly are used to estimate the objects' trajectories by employing an extended Kalman Filter, where provisions are made to accommodate a randomly changing number of obseravtions in each iteration. An adaptive rate control scheme is additionally applied to instruct the sensor nodes on how to adjust their transmission rate so as to improve the location estimation accuracy and the energy efficiency of the system. By focusing explicitly on the objects' locations, rather than working with a pre-specified grid of potential locations, we resolve the spatial quantization issues associated with sparse identification methods. Finally, we extend the method to address the possibility of objects entering and departing the observation area, thus improving the scalability of the system and relaxing the requirement for accurate knowledge of the objects' initial locations. Performance is analyzed in terms of the mean-squared localization error and the trade-offs imposed by the limited communication bandwidth.

We consider the problem of enabling robust range estimation of eigenvalue decomposition (EVD) algorithm for a reliable fixed-point design. The simplicity of fixed-point circuitry has always been so tempting to implement EVD algorithms in fixed-point arithmetic. Working towards an effective fixed-point design, integer bit-width allocation is a significant step which has a crucial impact on accuracy and hardware efficiency. This paper investigates the shortcomings of the existing range estimation methods while deriving bounds for the variables of the EVD algorithm. In light of the circumstances, we introduce a range estimation approach based on vector and matrix norm properties together with a scaling procedure that maintains all the assets of an analytical method. The method could derive robust and tight bounds for the variables of EVD algorithm. The bounds derived using the proposed approach remain same for any input matrix and are also independent of the number of iterations or size of the problem. Some benchmark hyperspectral data sets have been used to evaluate the efficiency of the proposed technique. It was found that by the proposed range estimation approach, all the variables generated during the computation of Jacobi EVD is bounded within ±1.

We show how to securely obfuscate conjunctions, which are functions f(x1,...,xn) = ∧i∈I yi where I ⊆ [n] and each literal yi is either just xi or ¬ xi e.g., f(xi,...,x\_n) = xi ⊆ ¬ x3 ⊆ ¬ x7 ... ⊆ x\\textbackslashvphantom\n-1. Whereas prior work of Brakerski and Rothblum (CRYPTO 2013) showed how to achieve this using a non-standard object called cryptographic multilinear maps, our scheme is based on an "entropic" variant of the Ring Learning with Errors (Ring LWE) assumption. As our core tool, we prove that hardness assumptions on the recent multilinear map construction of Gentry, Gorbunov and Halevi (TCC 2015) can be established based on entropic Ring LWE. We view this as a first step towards proving the security of additional mutlilinear map based constructions, and in particular program obfuscators, under standard assumptions. Our scheme satisfies virtual black box (VBB) security, meaning that the obfuscated program reveals nothing more than black-box access to f as an oracle, at least as long as (essentially) the conjunction is chosen from a distribution having sufficient entropy.

The harsh environment in the water has imposed challenges for underwater sensor networks (USNs), which collect the sensed data from the underwater sensors to the sink on land. The time-varying underwater acoustic channel has low band-width and high bit error rate, which leads to low data collection efficiency. Furthermore, the heterogeneous model of USNs that uses acoustic communications under the water and wireless communication above the water makes it difficult in efficient routing and forwarding for data collection. To this end, we propose a novel on-surface wireless-assisted opportunistic routing (SurOpp) for USNs. SurOpp deploys multiple buoy nodes on surface and includes all of them in the forwarding candidates to form a receive diversity. The opportunities of reception and forwarding in buoy nodes are exploited to improve the end-to-end transmissions. SurOpp also adopts rateless codes in the source to achieve opportunistic reception in the sink. The cooperation of both opportunistic reception in the buoys and the sink further decreases the messages of control overhead. The wireless interface in the buoy undertakes all the message exchanges in forwarding coordination to compensate the bandwidth limit of the acoustic channel. Simulations in NS3 show that SurOpp outperforms the traditional routing and existing opportunistic routing in terms of packet delivery ratio, end-to-end delay and energy consumption.

In the Serious Game Operation Digital Chameleon red and blue teams develop attack and defense strategies to explore IT-Security of Critical Infrastructures as part of an IT-Security training. Operation Digital Chameleon is the training game of the IT-Security Matchplay series in the IT-Security for Critical Infrastructure research program funded by BMBF. We present the design of Operation Digital Chameleon in its current form as well as results from game \#3. We analyze the potential and innovation capability of Operation Digital Chameleon as an Open Innovation method for the domain of IT-Security of Critical Infrastructures. We find that Operation Digital Chamaeleon facilitates creativity, opens the process of IT-Security strategy development and –despite being designed for training purposes – opens the process to explore innovative attack vectors.

(No abstract.)

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.

To match the signatures of malicious traffic across packet boundaries, network-intrusion detection (and prevention) systems (NIDS) typically perform pattern matching after flow reassembly or packet reordering. However, this may lead to the need for large packet buffers, making detection vulnerable to denial-of-service (DoS) attacks, whereby attackers exhaust the buffer capacity by sending long sequences of out-of-order packets. While researchers have proposed solutions for exact-match patterns, regular-expression matching on out-of-order packets is still an open problem. Specifically, a key challenge is the matching of complex sub-patterns (such as repetitions of wildcards matched at the boundary between packets). Our proposed approach leverages the insight that various segments matching the same repetitive sub-pattern are logically equivalent to the regular-expression matching engine, and thus, inter-changing them would not affect the final result. In this paper, we present O3FA, a new finite automata-based, deep packet-inspection engine to perform regular-expression matching on out-of-order packets without requiring flow reassembly. O3FA consists of a deterministic finite automaton (FA) coupled with a set of prefix-/suffix-FA, which allows processing out-of-order packets on the fly. We present our design, optimization, and evaluation for the O3FA engine. Our experiments show that our design requires 20x-4000x less buffer space than conventional buffering-and-reassembling schemes on various datasets and that it can process packets in real-time, i.e., without reassembly.

Touch input promises intuitive interactions with digital content as it employs our experience of manipulating physical objects: digital content can be rotated, scaled, and translated using direct manipulation gestures. However, the reliance on analog also confines the scope of direct physical manipulation: the physical world provides no mechanism to interact with digital abstract content. As such, applications on touchscreen devices either only include limited functionalities or fallback on the traditional form-filling paradigm, which is tedious, slow, and error prone for touch input. My research focuses on designing a new UI framework to enable complex functionalities on touch screen devices by expanding direct physical manipulation to abstract content via objectification. I present two research projects, objectification of attributes and selection, which demonstrate considerable promises.

We explore the use of a new way to log into a web service, such as email or social media. Using on-demand biometrics, users sign in from a browser on a computer using just their name, which sends a request to their phone for approval. Users approve this request by authenticating on their phone using their fingerprint, which completes the login in the browser. On-demand biometrics thus replace passwords or temporary access codes found in two-step verification with the ease of use of biometrics. We present the results of an interview study on the use of on-demand biometrics with a live login backend. Participants perceived our system as convenient and fast to use and also expressed their trust in fingerprint authentication to keep their accounts safe. We motivate the design of on-demand biometrics, present an analysis of participants' use and responses around general account security and authentication, and conclude with implications for designing fast and easy cross-device authentication.

Connection setup in software-defined networks (SDN) requires considerable amounts of processing, communication, and memory resources. Attackers can target SDN controllers with simple attacks to cause denial of service. We proposed a defense mechanism based on a proof-of-work protocol. The key characteristics of this protocol, namely its one-way operation, its requirement for freshness in proofs of work, its adjustable difficulty, its ability to work with multiple network providers, and its use of existing TCP/IP header fields, ensure that this approach can be used in practice.

Network architectures and applications are becoming increasingly complex. Several approaches to automatically enforce configurations on devices, applications and services have been proposed, such as Policy-Based Network Management (PBNM). However, the management of enforced configurations in production environments (e.g. data center) is a crucial and complex task. For example, updates on firewall configuration to change a set of rules. Although this task is fundamental for complex systems, few effective solutions have been proposed for monitoring and managing enforced configurations. This work proposes a novel approach to monitor and manage enforced configurations in production environments. The main contributions of this paper are a formal model to identify/ generate traffic flows and to verify the enforced configurations; and a slim and transparent framework to perform the policy assessment. We have implemented and validated our approach in a virtual environment in order to evaluate different scenarios. The results demonstrate that the prototype is effective and has good performance, therefore our model can be effectively used to analyse several types of IT infrastructures. A further interesting result is that our approach is complementary to PBNM.

Mobile Devices are part of our lives and we store a lot of private information on it as well as use services that handle sensitive information (e.g. mobile health apps). Whenever users install an application on their smartphones they have to decide whether to trust the applications and share private and sensitive data with at least the developer-owned services. But almost all modern apps not only transmit data to the developer owned servers but also send information to advertising-, analyzing and tracking partners. This paper presents an approach for a "privacy- proxy" which enables to filter unwanted data traffic to third party services without installing additional applications on the smartphone. It is based on a firewall using a black list of tracking- and analyzing networks which is automatically updated on a daily basis. The proof of concept has been implemented with open source components on a Raspberry Pi.

While compilers offer a fair trade-off between productivity and executable performance in single-threaded execution, their optimizations remain fragile when addressing compute-intensive code for parallel architectures with deep memory hierarchies. Moreover, these optimizations operate as black boxes, impenetrable for the user, leaving them with no alternative to time-consuming and error-prone manual optimization in cases where an imprecise cost model or a weak analysis resulted in a bad optimization decision. To address this issue, we propose a technique allowing to automatically translate an arbitrary polyhedral optimization, used internally by loop-level optimization frameworks of several modern compilers, into a sequence of comprehensible syntactic transformations as long as this optimization focuses on scheduling loop iterations. This approach opens the black box of the polyhedral frameworks enabling users to examine, refine, replay and even design complex optimizations semi-automatically in partnership with the compiler.

The Smart Grid control systems need to be protected from internal attacks within the perimeter. In Smart Grid, the Intelligent Electronic Devices (IEDs) are resource-constrained devices that do not have the ability to provide security analysis and protection by themselves. And the commonly used industrial control system protocols offer little security guarantee. To guarantee security inside the system, analysis and inspection of both internal network traffic and device status need to be placed close to IEDs to provide timely information to power grid operators. For that, we have designed a unique, extensible and efficient operation-level traffic analyzer framework. The timing evaluation of the analyzer overhead confirms efficiency under Smart Grid operational traffic.

Securing visible light communication (VLC) systems on the physical layer promises to prevent against a variety of attacks. Recent work shows that the adaption of existing legacy radio wave physical layer security (PLS) mechanisms is possible with minor changes. Yet, many adaptations open new vulnerabilities due to distinct propagation characteristics of visible light. A common understanding of threats arising from various attacker capabilities is missing. We specify a new attacker model for visible light physical layer attacks and evaluate the applicability of existing PLS approaches. Our results show that many attacks are not considered in current solutions.