ITC/USA 2006 Conference Proceedings / The Forty-Second Annual International Telemetering Conference and Technical Exhibition / October 23-26, 2006 / Town and Country Resort & Convention Center, San Diego, California / In most current PCM based telemetry systems, an instrumentation engineer manually creates the sampling format. This time consuming and tedious process typically involves manually placing each measurement into the format at the proper sampling rate. The telemetry industry is now moving towards Ethernet-based systems comprised of multiple autonomous data acquisition units, which share a single global time source. The architecture of these network systems greatly simplifies the task of implementing an automatic format generator. Automatic format generation eliminates much of the effort required to create a sampling format because the instrumentation engineer only has to specify the desired sampling rate for each measurement. The system handles the task of organizing the format to comply with the specified sampling rates. This paper examines the issues involved in designing an automatic format generator for a network data acquisition system.

Thesis (M.S.)--Rochester Institute of Technology, 1984. / Typescript. Includes bibliographical references (p. 71).

International Telemetering Conference Proceedings / October 20-23, 2003 / Riviera Hotel and Convention Center, Las Vegas, Nevada / There is growing interest in the airborne instrumentation community to adopt commercial standards to obtain scalable data rates, standards based interoperability, and utilization of Commercial Off The Shelf (COTS) products to reduce system costs. However, there has been few such data acquisition systems developed to date. L-3 Telemetry East has developed a prototype called the Network Data Acquisition System (NetDAS), which is based on the 10/100 Base-T Ethernet standard, TCP/UDP/IP network protocols and an industrial Ethernet switch. NetDAS has added network capability to the legacy MPC-800 telemetry system by replacing the existing formatter module with a formatter/controller based on a COTS CPU module and a custom designed bridge module. NetDAS has demonstrated transmission bit rates as high as 20 Mbps from a single unit using UDP/IP and an Ethernet switch. The NetDAS system has also demonstrated scalable and distributed architecture.

International Telemetering Conference Proceedings / October 28-31, 1996 / Town and Country Hotel and Convention Center, San Diego, California / The challenge being faced today in the Department of Defense is to find ways to improve the systems acquisition process. One area needing improvement is to eliminate surprises in unexpected test data which add cost and time to developing the system. This amounts to eliminating errors in all phases of a system’s lifecycle. In a perfect world, the ideal systems acquisition process would result in a perfect system. Flawless testing of a perfect system would result in predicted test results 100% of the time. However, such close fidelity between predicted behavior and real behavior has never occurred. Until this ideal level of boredom in testing occurs, testing will remain a critical part of the acquisition process. Given the indispensability of testing, the goal to reduce the cost of flight tests is well worth pursuing. Reducing test cost equates to reducing open air test hours, our most costly budget item. It also means planning, implementing and controlling test cycles more efficiently. We are working on methods to set up test missions faster, and analyze, evaluate, and report on the test data more quickly, including unexpected results. This paper explores the moving focus concept, one method that shows promise in our pursuit of the goal of reducing test costs. The moving focus concept permits testers to change the data they collect and view during a test, interactively, in real-time. This allows testers who are receiving unexpected test results to change measurement subsets and explore the problem or pursue other test scenarios.

Time and again we have seen the Internet grow and evolve at an unprecedented scale. The number of online users in 1995 was 40 million but in 2020, number of online devices are predicted to reach 50 billion, which would be 7 times the human population on earth. Up until now, the revolution was in the digital world. But now, the revolution is happening in the physical world that we live in; IoT devices are employed in all sorts of environments like domestic houses, hospitals, industrial spaces, nuclear plants etc., Since they are employed in a lot of mission-critical or even life-critical environments, their security and reliability are of paramount importance because compromising them can lead to grave consequences. IoT devices are, by nature, different from conventional Internet connected devices like laptops, smart phones etc., They have small memory, limited storage, low processing power etc., They also operate with little to no human intervention. Hence it becomes very important to understand IoT devices better. How do they behave in a network? How different are they from traditional Internet connected devices? Can they be identified from their network traffic? Is it possible for anyone to identify them just by looking at the network data that leaks outside the network, without even joining the network? That is the aim of this thesis. To the best of our knowledge, no study has collected data from outside the network, without joining the network, with the intention of finding out if IoT devices can be identified from this data. We also identify parameters that classify IoT and non-IoT devices. Then we do manual grouping of similar devices and then do the grouping automatically, using clustering algorithms. This will help in grouping devices of similar nature and create a profile for each kind of device.

No description available.

Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. This thesis proposes a novel self-supervised deep learning framework CAAD for anomaly detection in wireless communication systems. Specifically, CAAD employs powerful adversarial learning and contrastive learning techniques to learn effective representations of normal and anomalous behavior in wireless networks. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84%. Additionally, CAAD is augmented with the ability to systematically incorporate expert feedback through a novel contrastive learning feedback loop to improve the learned representations and thereby reduce prediction uncertainty (CAAD-EF). CAAD-EF is a novel, holistic and widely applicable solution to anomaly detection. / Master of Science / Anomaly detection is a technique that can be used to detect if there is any abnormal behavior in data. It is a ubiquitous and a challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these networks is imperative for smooth functioning of society. Anomaly detection in such communication networks is essential in ensuring security. This thesis proposes a novel framework CAAD for anomaly detection in wireless communication systems. Rigorous performance comparisons of CAAD with several state-of-the-art anomaly detection techniques has been conducted and verified that CAAD yields a mean performance improvement of 92.84% over state-of-the-art anomaly detection models. Additionally, CAAD is augmented with the ability to incorporate feedback from experts about whether a sample is normal or anomalous through a novel feedback loop (CAAD-EF). CAAD-EF is a novel, holistic and a widely applicable solution to anomaly detection.

No description available.

<div>Networks and point processes provide flexible tools for representing and modeling complex dependencies in data arising from various social and physical domains. Graphs, or networks, encode relational dependencies between entities, while point processes characterize temporal or spatial interactions among events.</div><div><br></div><div>In the first part of this dissertation, we consider dynamic network data (such as communication networks) in which links connecting pairs of nodes appear continuously over time. We propose latent space point process models to capture two different aspects of the data: (i) communication occurs at a higher rate between individuals with similar latent attributes (i.e., homophily); and (ii) individuals tend to reciprocate communications from others, but in a varied manner. Our framework marries ideas from point process models, including Poisson and Hawkes processes, with ideas from latent space models of static networks. We evaluate our models on several real-world datasets and show that a dual latent space model, which accounts for heterogeneity in both homophily and reciprocity, significantly improves performance in various link prediction and network embedding tasks.</div><div><br></div><div>In the second part of this dissertation, we develop nonparametric goodness-of-fit tests for discrete distributions and point processes that contain intractable normalization constants, providing the first generally applicable and computationally feasible approaches under those circumstances. Specifically, we propose and characterize Stein operators for discrete distributions, and construct a general Stein operator for point processes using the Papangelou conditional intensity function. Based on the proposed Stein operators, we establish kernelized Stein discrepancy measures for discrete distributions and point processes, which enable us to develop nonparametric goodness-of-fit tests for un-normalized density/intensity functions. We apply the kernelized Stein discrepancy tests to discrete distributions (including network models) as well as temporal and spatial point processes. Our experiments demonstrate that the proposed tests typically outperform two-sample tests based on the maximum mean discrepancy, which, unlike our goodness-of-fit tests, assume the availability of exact samples from the null model.</div><div><br></div>

With the advancement of the Internet and related technologies, many traditional crimes have made the leap to digital environments. The successes of data mining in a wide variety of disciplines have given birth to crime analysis. Traditional crime analysis is mainly focused on understanding crime patterns, however, it is unsuitable for identifying and monitoring emerging crimes. The true nature of crime remains buried in unstructured content that represents the hidden story behind the data. User feedback leaves valuable traces that can be utilised to measure the quality of various aspects of products or services and can also be used to detect, infer, or predict crimes. Like any application of data mining, the data must be of a high quality standard in order to avoid erroneous conclusions. This thesis presents a methodology and practical experiments towards discovering whether (i) user feedback can be harnessed and processed for crime intelligence, (ii) criminal associations, structures, and roles can be inferred among entities involved in a crime, and (iii) methods and standards can be developed for measuring, predicting, and comparing the quality level of social data instances and samples. It contributes to the theory, design and development of a novel framework for crime intelligence and algorithm for the estimation of social data quality by innovatively adapting the methods of monitoring water contaminants. Several experiments were conducted and the results obtained revealed the significance of this study in mining social data for crime intelligence and in developing social data quality filters and decision support systems.