Tracking Cyber Adversaries with Adaptive Indicators of Compromise

A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary in the future. Over time, the adversary will change tactics, techniques, and procedures (TTPs), which will also change the data generated. If the IOCs are not kept up-to-date with the adversary's new TTPs, the adversary will no longer be detected once all of the IOCs become invalid. Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular expressions (regexes), up-to-date with a dynamic adversary. Our framework solves the TTK problem in an automated, cyclic fashion to bracket a previously discovered adversary. This tracking is accomplished through a data-driven approach of self-adapting a given model based on its own detection capabilities. In our initial experiments, we found that the true positive rate (TPR) of the adaptive solution degrades much less significantly over time than the naive solution, suggesting that self-updating the model allows the continued detection of positives (i.e., adversaries). The cost for this performance is in the false positive rate (FPR), which increases over time for the adaptive solution, but remains constant for the naive solution. However, the difference in overall detection performance, as measured by the area under the curve (AUC), between the two methods is negligible. This result suggests that self-updating the model over time should be done in practice to continue to detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science & Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas, Nevada, US

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN

Flight Qualified Micro Sun Sensor

A prototype small, lightweight micro Sun sensor (MSS) has been flight qualified as part of the attitude-determination system of a spacecraft or for Mars surface operations. The MSS has previously been reported at a very early stage of development in NASA Tech Briefs, Vol. 28, No. 1 (January 2004). An MSS is essentially a miniature multiple-pinhole electronic camera combined with digital processing electronics that functions analogously to a sundial. A micromachined mask containing a number of microscopic pinholes is mounted in front of an active-pixel sensor (APS). Electronic circuits for controlling the operation of the APS, readout from the pixel photodetectors, and analog-to-digital conversion are all integrated onto the same chip along with the APS. The digital processing includes computation of the centroids of the pinhole Sun images on the APS. The spacecraft computer has the task of converting the Sun centroids into Sun angles utilizing a calibration polynomial. The micromachined mask comprises a 500-micron-thick silicon wafer, onto which is deposited a 57-nm-thick chromium adhesion- promotion layer followed by a 200-nm-thick gold light-absorption layer. The pinholes, 50 microns in diameter, are formed in the gold layer by photolithography. The chromium layer is thin enough to be penetrable by an amount of Sunlight adequate to form measurable pinhole images. A spacer frame between the mask and the APS maintains a gap of .1 mm between the pinhole plane and the photodetector plane of the APS. To minimize data volume, mass, and power consumption, the digital processing of the APS readouts takes place in a single field-programmable gate array (FPGA). The particular FPGA is a radiation- tolerant unit that contains .32,000 gates. No external memory is used so the FPGA calculates the centroids in real time as pixels are read off the APS with minimal internal memory. To enable the MSS to fit into a small package, the APS, the FPGA, and other components are mounted on a single two-sided board following chip-on-board design practice

Marketing: A comparison of academic theory and practices of Utah businesses

honors thesisDavid Eccles School of BusinessMarketingWilliam L. MooreBruce F. BairdThe purpose of this study is to compare two different views of marketing, marketing from an academic view point and marketing as it is practiced in the business community. The first effort of this study was to develop a theory of the way marketing should be practiced as established by academicians. This theoretic framework is used as a basis of comparison by which the marketing practices of various Utah business are analyzed

Flight Qualified Micro Sun Sensor for Mars Applications

A Right qualified micro sun sensor is being developed and flight qualified for future Man missions. The micro sun sensor, which Is basically a small pinhole camera, consists of a small mask with pinholes, placed on top of an image detector. Images of the sun are formed on the image detector when the sun illuminates the mask. Image processing is performed in the sun sensor that outputs sun centroids

Possible barriers to enrollment in substance abuse treatment among a diverse sample of Asian Americans and Pacific Islanders: Opinions of treatment clients

This mixed methods study examined motivations and barriers to substance abuse treatment entry and treatment continuation among Asian American and Pacific Islander (AAPI) substance users. AAPI substance users (N = 61) were recruited from substance abuse treatment programs in California and Hawaii. Semi-structured interviews and interviewer-administered surveys assessed barriers and facilitators to entering substance abuse treatment. Barriers included peer pressure, family influences, and face loss concerns. Facilitators included peer support, involvement in the criminal justice system, a perceived need for treatment, and culturally competent substance abuse treatment services. Family and peer influences may act as both facilitators and impediments. AAPI substance using populations face many of the same individual-level and structural and systems barriers to entry to treatment as other substance using populations. However, similar to other racial/ethnic minority groups, it is important to address cultural differences and develop culturally competent substance abuse treatments for the AAPI population