Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks

Intrusion detection has become one of the most critical tasks in a wireless network to prevent service outages that can take long to fix. The sheer variety of anomalous events necessitates adopting cognitive anomaly detection methods instead of the traditional signature-based detection techniques. This paper proposes an anomaly detection methodology for wireless systems that is based on monitoring and analyzing radio frequency (RF) spectrum activities. Our detection technique leverages an existing solution for the video prediction problem, and uses it on image sequences generated from monitoring the wireless spectrum. The deep predictive coding network is trained with images corresponding to the normal behavior of the system, and whenever there is an anomaly, its detection is triggered by the deviation between the actual and predicted behavior. For our analysis, we use the images generated from the time-frequency spectrograms and spectral correlation functions of the received RF signal. We test our technique on a dataset which contains anomalies such as jamming, chirping of transmitters, spectrum hijacking, and node failure, and evaluate its performance using standard classifier metrics: detection ratio, and false alarm rate. Simulation results demonstrate that the proposed methodology effectively detects many unforeseen anomalous events in real time. We discuss the applications, which encompass industrial IoT, autonomous vehicle control and mission-critical communications services.Comment: 7 pages, 7 figures, Communications Workshop ICC'1

Lifeguard: Local Health Awareness for More Accurate Failure Detection

SWIM is a peer-to-peer group membership protocol with attractive scaling and robustness properties. However, slow message processing can cause SWIM to mark healthy members as failed (so called false positive failure detection), despite inclusion of a mechanism to avoid this. We identify the properties of SWIM that lead to the problem, and propose Lifeguard, a set of extensions to SWIM which consider that the local failure detector module may be at fault, via the concept of local health. We evaluate this approach in a precisely controlled environment and validate it in a real-world scenario, showing that it drastically reduces the rate of false positives. The false positive rate and detection time for true failures can be reduced simultaneously, compared to the baseline levels of SWIM

Space Flight LiDARs, Navigation & Science Instrument Implementations: Lasers, Optoelectronics, Integrated Photonics, Fiber Optic Subsystems and Components

For the past 25 years, the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center's Photonics Group in the Engineering Directorate has been substantially contributing to the flight design, development, production, testing and integration of many science and navigational instruments. The Moon to Mars initiative will rely heavily upon utilizing commercial technologies for instrumentation with aggressive schedule deadlines. The group has an extensive background in screening, qualifying, development and integration of commercial components for spaceflight applications. By remaining adaptable and employing a rigorous approach to component and instrument development, they have forged and fostered relationships with industry partners. They have been willing to communicate lessons learned in packaging, part construction, materials selection, testing, and other facets of the design and production process critical to implementation for high-reliability systems. As a result, this successful collaboration with industry vendors and component suppliers has enabled a history of mission success from the Moon to Mars (and beyond) while balancing cost, schedule, and risk postures. In cases where no commercial components exist, the group works closely with other teams at Goddard Space Flight Center and other NASA field centers to fabricate and produce flight hardware for science, remote sensing, and navigation applications. Summarized here is the last ten years of instrumentation development lessons learned and data collected from the subsystems down to the optoelectronic component level

Failure detection and repair of threads in CTAS

Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.Includes bibliographical references (p. 73).Reliable, error-free software is hard to come by, and this is especially true for newer, larger, or more complex programs. CTAS, an air traffic control tool, falls into this category, making it a good candidate for research on error compensation. Specifically, this thesis addresses the issue of thread crashes in one portion of CTAS. We reimplement the thread structure in question around a simpler problem, and develop a failure detector and an accompanying repair mechanism to monitor it. These add-on components provide the application with thread consistency by swiftly and transparently recovering from crashes, thereby yielding a more stable, self-sufficient, and generally more reliable operating environment.by Farid Jahanmir.M.Eng.and S.B

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

Report of the Attitude Control and Attitude Determination Panel

Failures and deficiencies in flight programs are reviewed and suggestions are made for avoiding them. The technology development problem areas considered are control configured vehicle design, gyros, solid state star sensors, control instrumentation, tolerant/accomodating control systems, large momentum exchange devices, and autonomous rendezvous and docking

CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the simulation pipeline of a typical experiment at the Large Hadron Collider (LHC) is the detailed modeling of the full complexity of physics processes that govern the motion and evolution of particle showers inside calorimeters. We introduce \textsc{CaloGAN}, a new fast simulation technique based on generative adversarial networks (GANs). We apply these neural networks to the modeling of electromagnetic showers in a longitudinally segmented calorimeter, and achieve speedup factors comparable to or better than existing full simulation techniques on CPU ($100\times$-$1000\times$) and even faster on GPU (up to $\sim10^5\times$). There are still challenges for achieving precision across the entire phase space, but our solution can reproduce a variety of geometric shower shape properties of photons, positrons and charged pions. This represents a significant stepping stone toward a full neural network-based detector simulation that could save significant computing time and enable many analyses now and in the future.Comment: 14 pages, 4 tables, 13 figures; version accepted by Physical Review D (PRD