Know Your Enemy: Stealth Configuration-Information Gathering in SDN

Software Defined Networking (SDN) is a network architecture that aims at providing high flexibility through the separation of the network logic from the forwarding functions. The industry has already widely adopted SDN and researchers thoroughly analyzed its vulnerabilities, proposing solutions to improve its security. However, we believe important security aspects of SDN are still left uninvestigated. In this paper, we raise the concern of the possibility for an attacker to obtain knowledge about an SDN network. In particular, we introduce a novel attack, named Know Your Enemy (KYE), by means of which an attacker can gather vital information about the configuration of the network. This information ranges from the configuration of security tools, such as attack detection thresholds for network scanning, to general network policies like QoS and network virtualization. Additionally, we show that an attacker can perform a KYE attack in a stealthy fashion, i.e., without the risk of being detected. We underline that the vulnerability exploited by the KYE attack is proper of SDN and is not present in legacy networks. To address the KYE attack, we also propose an active defense countermeasure based on network flows obfuscation, which considerably increases the complexity for a successful attack. Our solution offers provable security guarantees that can be tailored to the needs of the specific network under consideratio

Index to 1984 NASA Tech Briefs, volume 9, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1984 Tech B Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Application of advanced technology to space automation

Automated operations in space provide the key to optimized mission design and data acquisition at minimum cost for the future. The results of this study strongly accentuate this statement and should provide further incentive for immediate development of specific automtion technology as defined herein. Essential automation technology requirements were identified for future programs. The study was undertaken to address the future role of automation in the space program, the potential benefits to be derived, and the technology efforts that should be directed toward obtaining these benefits

SignalGuru: Leveraging mobile phones for collaborative traffic signal schedule advisory

While traffic signals are necessary to safely control competing flows of traffic, they inevitably enforce a stop-and-go movement pattern that increases fuel consumption, reduces traffic flow and causes traffic jams. These side effects can be alleviated by providing drivers and their onboard computational devices (e.g., vehicle computer, smartphone) with information about the schedule of the traffic signals ahead. Based on when the signal ahead will turn green, drivers can then adjust speed so as to avoid coming to a complete halt. Such information is called Green Light Optimal Speed Advisory (GLOSA). Alternatively, the onboard computational device may suggest an efficient detour that will save the driver from stops and long waits at red lights ahead. This paper introduces and evaluates SignalGuru, a novel software service that relies solely on a collection of mobile phones to detect and predict the traffic signal schedule, enabling GLOSA and other novel applications. Our SignalGuru leverages windshield-mounted phones to opportunistically detect current traffic signals with their cameras, collaboratively communicate and learn traffic signal schedule patterns, and predict their future schedule. Results from two deployments of SignalGuru, using iPhones in cars in Cambridge (MA, USA) and Singapore, show that traffic signal schedules can be predicted accurately. On average, SignalGuru comes within 0.66s, for pre-timed traffic signals and within 2.45s, for traffic-adaptive traffic signals. Feeding SignalGuru's predicted traffic schedule to our GLOSA application, our vehicle fuel consumption measurements show savings of 20.3%, on average.National Science Foundation (U.S.). (Grant number CSR-EHS-0615175)Singapore-MIT Alliance for Research and Technology Center. Future Urban Mobilit

Gryphon M^3 system: integration of MEMS for flight control

By using distributed arrays of micro-actuators as effectors, micro-sensors to detect the optimal actuation location, and microelectronics to provide close loop feedback decisions, a low power control system has been developed for controlling a UAV. Implementing the Microsensors, Microactuators, and Microelectronics leads to what is known as a M^3 (M-cubic) system. This project involves demonstrating the concept of using small actuators (approximately micron-millimeter scale) to provide large control forces for a large-scale system (approximately meter scale) through natural flow amplification phenomenon. This is theorized by using fluid separation phenomenon, vortex evolution, and vortex symmetry on a delta wing aircraft. By using MEMS actuators to control leading edge vortex separation and growth, a desired aerodynamic force can be produced about the aircraft for flight control. Consequently, a MEMS shear stress sensor array was developed for detecting the leading edge separation line where leading edge vortex flow separation occurs. By knowing the leading edge separation line, a closely coupled micro actuation from the effectors can cause the required separation that leads to vortex control. A robust and flexible balloon type actuator was developed using pneumatic pressure as the actuation force. Recently, efforts have started to address the most elusive problem of amplified distributed control (ADC) through data mining algorithms. Preliminary data mining results are promising and this part of the research is ongoing. All wind tunnel data used the baseline 56.5 degree(s) sweepback delta wing with root chord of 31.75 cm

Analytical modelling of in-situ layer-wise defect detection in 3D Printed parts: Additive Manufacturing

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This study analyses a software algorithm developed on MATLAB, which can be used to examine fused filament fabrication-based 3D printed materials for porosity and other defects that might affect the mechanical property of the final component under manufacture or the general aesthetic quality of a product. An in-depth literature review into the 3D printed materials reveals a rapidly increasing trend in its application in the industrial sector. Hence the quality of manufactured products cannot be compromised. Despite much research found to be done on this subject, there is still little or no work reported on porosity or defect detection in 3D printed components during (real-time) or after manufacturing operation. The algorithm developed in this study is tested for two different 3-D object geometry and the same filament color. The results showed that the algorithm effectively detected the presence or absence of defects in a 3D printed part geometry and filament colors. Hence, this technique can be generalized to a considerable range of 3-D printer geometries, which solve material wastages by spotting defects during the workpieces layer-wise manufacturing process, thereby improving the economic advantages of additive manufacturing

Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

From 3D Models to 3D Prints: an Overview of the Processing Pipeline

Due to the wide diffusion of 3D printing technologies, geometric algorithms for Additive Manufacturing are being invented at an impressive speed. Each single step, in particular along the Process Planning pipeline, can now count on dozens of methods that prepare the 3D model for fabrication, while analysing and optimizing geometry and machine instructions for various objectives. This report provides a classification of this huge state of the art, and elicits the relation between each single algorithm and a list of desirable objectives during Process Planning. The objectives themselves are listed and discussed, along with possible needs for tradeoffs. Additive Manufacturing technologies are broadly categorized to explicitly relate classes of devices and supported features. Finally, this report offers an analysis of the state of the art while discussing open and challenging problems from both an academic and an industrial perspective.Comment: European Union (EU); Horizon 2020; H2020-FoF-2015; RIA - Research and Innovation action; Grant agreement N. 68044