Results from the Prognostic Analysis Completed on the NASA EUVE Satellite to Measure Equipment Mission Life
Measuring Remaining Usable Life
Calculating Remaining Usable Life
Measuring Mission Life
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AbstractThis paper addresses the research conducted at U.C. Berkeley Space Sciences Laboratory, Center for Extreme Ultra Violet Astrophysics between 1994 and 1995 on the NASA EUVE ion-orbit satellite. It includes the results from conducting a scientific analysis called a prognostic analysis completed on all satellite subsystem equipment. A prognostic analysis uses equipment analog telemetry to measure equipment remaining usable life. The analysis relates equipment transient behavior, often referred to as "cannot duplicates" in a variety of industries caused from accelerated aging to the equipment end-of-life with certainty. The analysis was confirmed by using proprietary, pattern recognition software by Lockheed Martin personnel Lockheed Martin personnel completed an exploration into the application of statistical pattern recognition methods to identify the behavior caused from accelerated aging that experts in probability reliability analysis claims cannot exist. Both visual and statistical methods were successful in detecting suspect accelerated aging and this behavior was related to equipment end of life with certainty. The long-term objective of this research was to confirm that satellite subsystem equipment failures could be predicted so that satellite subsystem and payload engineering personnel could be allocated for only the time that equipment failures were predicted to occur, lowering the cost of mission operations. This research concluded that satellite subsystem equipment remaining usable life could be measured and equipment failures could be predicted with certainty so that engineering support for mission operations could be greatly reduced.
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An Anomaly Behavior Analysis Methodology for the Internet of Things: Design, Analysis, and EvaluationPacheco Ramirez, Jesus Horacio (The University of Arizona., 2017)Advances in mobile and pervasive computing, social network technologies and the exponential growth in Internet applications and services will lead to the development of the Internet of Things (IoT). The IoT services will be a key enabling technology to the development of smart infrastructures that will revolutionize the way we do business, manage critical services, and how we secure, protect, and entertain ourselves. Large-scale IoT applications, such as critical infrastructures (e.g., smart grid, smart transportation, smart buildings, etc.) are distributed systems, characterized by interdependence, cooperation, competition, and adaptation. The integration of IoT premises with sensors, actuators, and control devices allows smart infrastructures to achieve reliable and efficient operations, and to significantly reduce operational costs. However, with the use of IoT, we are experiencing grand challenges to secure and protect such advanced information services due to the significant increase in the attack surface. The interconnections between a growing number of devices expose the vulnerability of IoT applications to attackers. Even devices which are intended to operate in isolation are sometimes connected to the Internet due to careless configuration or to satisfy special needs (e.g., they need to be remotely managed). The security challenge consists of identifying accurately IoT devices, promptly detect vulnerabilities and exploitations of IoT devices, and stop or mitigate the impact of cyberattacks. An Intrusion Detection System (IDS) is in charge of monitoring the behavior of protected systems and is looking for malicious activities or policy violations in order to produce reports to a management station or even perform proactive countermeasures against the detected threat. Anomaly behavior detection is a technique that aims at creating models for the normal behavior of the network and detects any significant deviation from normal operations. With the ability to detect new and novel attacks, the anomaly detection is a promising IDS technique that is actively pursued by researchers. Since each IoT application has its own specification, it is hard to develop a single IDS which works properly for all IoT layers. A better approach is to design customized intrusion detection engines for different layers and then aggregate the analysis results from these engines. On the other hand, it would be cumbersome and takes a lot of effort and knowledge to manually extract the specification of each system. So it will be appropriate to formulate our methodology based on machine learning techniques which can be applied to produce efficient detection engines for different IoT applications. In this dissertation we aim at formalizing a general methodology to perform anomaly behavior analysis for IoT. We first introduce our IoT architecture for smart infrastructures that consists of four layers: end nodes (devices), communications, services, and application. Then we show our multilayer IoT security framework and IoT architecture that consists of five planes: function specification or model plane, attack surface plane, impact plane, mitigation plane, and priority plane. We then present a methodology to develop a general threat model in order to recognize the vulnerabilities in each layer and the possible countermeasures that can be deployed to mitigate their exploitation. In this scope, we show how to develop and deploy an anomaly behavior analysis based intrusion detection system (ABA-IDS) to detect anomalies that might be triggered by attacks against devices, protocols, information or services in our IoT framework. We have evaluated our approach by launching several cyberattacks (e.g. Sensor Impersonation, Replay, and Flooding attacks) against our testbeds developed at the University of Arizona Center for Cloud and Autonomic Computing. The results show that our approach can be used to deploy effective security mechanisms to protect the normal operations of smart infrastructures integrated to the IoT. Moreover, our approach can detect known and unknown attacks against IoT with high detection rate and low false alarms.
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