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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

JTAG-based PLC memory acquisition framework for industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLC) are the embedded devices that directly control and monitor critical industrial infrastructure processes such as nuclear plants and power grid stations. Cyberattacks often target PLCs to sabotage a physical process. A memory forensic analysis of a suspect PLC can answer questions about an attack, including compromised firmware and manipulation of PLC control logic code and I/O devices. Given physical access to a PLC, collecting forensic information from the PLC memory at the hardware-level is risky and challenging. It may cause the PLC to crash or hang since PLCs have proprietary, legacy hardware with heterogeneous architecture. This paper addresses this research problem and proposes a novel JTAG (Joint Test Action Group)-based framework, Kyros, for reliable PLC memory acquisition. Kyros systematically creates a JTAG profile of a PLC through hardware assessment, JTAG pins identification, memory map creation, and optimizing acquisition parameters. It also facilitates the community of interest (such as ICS owners, operators, and vendors) to develop the JTAG profiles of PLCs. Further, we present a case study of Kyros implementation over Allen-Bradley 1756-A10/B to help understand the framework's application on a real-world PLC used in industry settings. The sample PLC memory dumps are shared with the research community to facilitate further research.

Rais, Muhammad Haris↗
DOE OSTI2022

The Evolution of Volatile Memory Forensics

The collection and analysis of volatile memory is a vibrant area of research in the cybersecurity community. The ever-evolving and growing threat landscape is trending towards fileless malware, which avoids traditional detection but can be found by examining a system’s random access memory (RAM). Additionally, volatile memory analysis offers great insight into other malicious vectors. It contains fragments of encrypted files’ contents, as well as lists of running processes, imported modules, and network connections, all of which are difficult or impossible to extract from the file system. For these compelling reasons, recent research efforts have focused on the collection of memory snapshots and methods to analyze them for the presence of malware. However, to the best of our knowledge, no current reviews or surveys exist that systematize the research on both memory acquisition and analysis. We fill that gap with this novel survey by exploring the state-of-the-art tools and techniques for volatile memory acquisition and analysis for malware identification. For memory acquisition methods, we explore the trade-offs many techniques make between snapshot quality, performance overhead, and security. For memory analysis, we examined the traditional forensic methods used, including signature-based methods, dynamic methods performed in a sandbox environment, as well as machine learning-based approaches. We summarize the currently available tools, and suggest areas for more research.

Nyholm, Hannah↗
DOE OSTI2018

Endogenous amyloid-β mediates memory forgetting in the normal brain

Highlights: • Injection of anti-Aβ antibody into the ventricles prolonged object recognition memory. • It was also effective when injected after memory consolidation. • Genetic deletion of FcγRIIb, an Aβ receptor, prolonged object recognition memory. Amyloid beta (Aβ) is known to be one of the strong candidate molecules for initiating Alzheimer's disease and has been extensively studied in the light of disease pathophysiology. However, it is still elusive what roles Aβ play in the normal brain. In this study, we report that Aβ is required for memory forgetting in the normal brain. We monitored object recognition memory, and in order to quench soluble Aβ, we microinjected anti-Aβ antibody (4G8) into the ventricles after memory acquisition. Microinjection of anti-Aβ antibody prolonged the maintenance of object recognition memory. This effect appeared not to be due to modulation of memory consolidation since antibody injection after memory consolidation still had a similar effect on memory maintenance. Furthermore, the maintenance of object recognition memory was prolonged in Fcgr2b KO mice, which lacks IgG Fcγ receptor II-b (FcγRIIb), a receptor for soluble Aβ oligomers. Taken together, these findings suggest that endogenous Aβ is involved in memory forgetting in the normal brain.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Sparse Data Acquisition on Emerging Memory Architectures

Emerging memory devices, such as resistive crossbars, have the capacity to store large amounts of data in a single array. Acquiring the data stored in large-capacity crossbars in a sequential fashion can become a bottleneck. We present practical methods, based on sparse sampling, to quickly acquire sparse data stored on emerging memory devices that support the basic summation kernel, reducing the acquisition time from linear to sub-linear. Experimental results show that at least an order of magnitude improvement in acquisition time can be achieved when the data is sparse.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

MemAxes Visualization Software

Hardware advancements such as Intel's PEBS and AMD's IBS, as well as software developments such as the perf_event API in Linux have made available the acquisition of memory access samples with performance information. MemAxes is a visualization and analysis tool for memory access sample data. By mapping the samples to their associated code, variables, node topology, and application dataset, MemAxes provides intuitive views of the data.

Source record↗
DOE OSTI2014

MemAxes Visualization Software

Hardware advancements such as Intel's PEBS and AMD's IBS, as well as software developments such as the perf_event API in Linux have made available the acquisition of memory access samples with performance information. MemAxes is a visualization and analysis tool for memory access sample data. By mapping the samples to their associated code, variables, node topology, and application dataset, MemAxes provides intuitive views of the data.

Source record↗
DOE OSTI2015

FPGA-based prototype of portable environmental radiation monitor

This new portable radiological environmental monitor consists of 2 main components, Gamma ionization chamber and a FPGA-based electronic enclosure linked to convivial software for treatment and analyzing. The HPIC ion chamber is the heart of this radiation measurement system and is running in range from 0 to 100 mR/h, so that the sensitivity at the output is 20 mV/μR/h, with a nearly flat energy response from 0,07 to 10 MEV. This paper presents a contribution for developing a new nuclear measurement data acquisition system based on Cyclone III FPGA Starter Kit ALTERA, and a user-friendly software to run real-time control and data processing. It was developed to substitute the older radiation monitor RSS-112 PIC installed in CNESTEN's Laboratory in order to improve some of its functionalities related to acquisition time and data memory capacity. As for the associated acquisition software, it was conceived under the virtual LabView platform from National Instrument, and offers a variety of system setup for radiation environmental monitoring. It gives choice to display both the statistical data and the dose rate. Statistical data shows a summary of current data, current time/date and dose integrator values, and the dose rate displays the current dose rate in large numbers for viewing from a distance as well as the date and time. The prototype version of this new instrument and its data processing software has been successfully tested and validated for viewing and monitoring the environmental radiation of Moroccan nuclear center. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Content-aware foveated camera for multi-target tracking

Modern image sensors deliver substantial space-time bandwidth, yet indiscriminate acquisition often overwhelms memory, computation, and downstream perception. We present a content-aware, multi-foveated camera that dynamically reallocates sensing and magnification to multiple regions of interest (ROIs). A phase-only spatial light modulator (SLM) serves as a solid-state, inertia-free beam-steering and lens element, enabling per-frame field-of-view (FOV) reconfiguration and content-aware target tracking. By interleaving frames across foveae, our system preserves a wide-FOV situational context while refreshing each ROI at high rates, thereby reducing data volume without degrading task performance. We constructed a prototype employing a single-SLM, single-sensor architecture and demonstrated its application in real-time multi-object tracking with dynamic ROI maintenance across multiple viewpoints. The approach offers a general pathway to integrate detection, tracking, and segmentation algorithms in the acquisition loop, shifting workload from post hoc processing to intelligent capture.

Zang, Zihan [Univ. of California, Los Angeles, CA ↗
DOE OSTI2020

Temporal Gating of Synaptic Competition in the Amygdala by Cannabinoid Receptor Activation

Abstract The acquisition of fear memories involves plasticity of the thalamic and cortical pathways to the lateral amygdala (LA). In turn, the maintenance of synaptic plasticity requires the interplay between input-specific synaptic tags and the allocation of plasticity-related proteins. Based on this interplay, weakly activated synapses can express long-lasting forms of synaptic plasticity by cooperating with strongly activated synapses. Increasing the number of activated synapses can shift cooperation to competition. Synaptic cooperation and competition can determine whether two events, separated in time, are associated or whether a particular event is selected for storage. The rules that determine whether synapses cooperate or compete are unknown. We found that synaptic cooperation and competition, in the LA, are determined by the temporal sequence of cortical and thalamic stimulation and that the strength of the synaptic tag is modulated by the endocannabinoid signaling. This modulation is particularly effective in thalamic synapses, supporting a critical role of endocannabinoids in restricting thalamic plasticity. Also, we found that the availability of synaptic proteins is activity-dependent, shifting competition to cooperation. Our data present the first evidence that presynaptic modulation of synaptic activation, by the cannabinoid signaling, functions as a temporal gating mechanism limiting synaptic cooperation and competition.

Madeira, Natália↗
DOE OSTI2018

Behavioural phenotypes in mice after prenatal and early postnatal exposure to intermediate frequency magnetic fields

Highlights: • Human exposure to intermediate frequency magnetic fields (MF) is increasing. • We exposed prenatal and early postnatal male mice to 7.5 kHz MFs at 12 and 120 μT. • A behavioural teratology study focused on the developing nervous system. • No consistent adverse effects were observed. Electromagnetic fields are ubiquitous in the environment. Human exposure to intermediate frequency (IF) fields is increasing due to applications like electronic article surveillance systems, wireless power transfer, and induction heating cooking hobs. However, there are limited data on possible health effects of exposure to IF magnetic fields (MF). In the present study, we set out to assess cognitive and behavioural effects of IF MF in mice exposed during prenatal and early postnatal periods. Pregnant female mice were exposed continuously to 7.5 kHz MFs at 12 and 120 μT, from mating until weaning of pups. Sham exposed pregnant mice were used as a control group. A behavioural teratology study was conducted on the male offspring at two months of age to detect possible effects on the developing nervous system. Body weight development did not differ between the exposure groups. The exposure did not alter spontaneous motor activity when exploring a novel cage or anxiety in novelty-suppressed feeding or marble burying tests. Improved performance in the Rotarod task was observed in the 12 µT group, while the 120 μT exposure group swam more slowly than the sham exposed group in the Morris swim navigation task. However, indices of learning and memory (path length and escape latency during task acquisition and search bias during the probe test) did not differ between the exposure groups. Furthermore, the passive avoidance task did not indicate any impairment of long-term memory over a 48 h interval in the exposed groups. In a post-mortem histopathological analysis, there was no evidence for an effect of IF MF exposure on astroglial reactivity or hippocampal neurogenesis. The results suggest that the IF MF used did not have detrimental effects on spatial learning and memory or histological markers of tissue reaction. The two statistically significant findings that were observed (improved performance in the Rotarod task in the 12 µT group and decreased swimming speed in the 120 µT group) are likely to be chance findings, as they do not form an internally consistent, dose-dependent pattern indicative of specific developmental effects.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Noise-Based Selection of Robust Inherited Model for Accurate Continual Learning

There is a growing demand for an intelligent system to continually learn knowledge from a data stream. Continual learning requires both the preservation of previous knowledge (i.e., avoiding catastrophic forgetting) and the acquisition of new knowledge. Different from previous works that focus only on model adaptation (e.g., regularization, network expansion, memory rehearsal, etc.), we propose a novel training scheme named acquisitive learning (AL), which emphasizes both the knowledge inheritance and knowledge acquisition. AL starts from an elaborately selected model with pre-trained knowledge (the inherited model) and then adapts it to new data using segmented training. The selection is achieved by injecting random noise to various inherited models for better model robustness, which promises higher accuracy in further knowledge acquisition. The approach is validated by the visualization of the loss landscape and quantitative roughness measurement. The combination of the selective inherited model and knowledge acquisition reduces catastrophic forgetting by 10X on the CIFAR-100 dataset.

Du, Xiaocong↗
DOE OSTI2012

The CMS High Level Trigger System: Experience and future development

The CMS experiment at the LHC features a two-level trigger system. Events accepted by the first level trigger, at a maximum rate of 100 kHz, are read out by the Data Acquisition system (DAQ), and subsequently assembled in memory in a farm of computers running a software high-level trigger (HLT), which selects interesting events for offline storage and analysis at a rate of order few hundred Hz. The HLT algorithms consist of sequences of offline-style reconstruction and filtering modules, executed on a farm of 0(10000) CPU cores built from commodity hardware. Experience from the operation of the HLT system in the collider run 2010/2011 is reported. The current architecture of the CMS HLT, its integration with the CMS reconstruction framework and the CMS DAQ, are discussed in the light of future development. The possible short- and medium-term evolution of the HLT software infrastructure to support extensions of the HLT computing power, and to address remaining performance and maintenance issues, are discussed.

Bauer, G.↗
DOE OSTI2014

A seamless acquisition digital storage oscilloscope with three-dimensional waveform display

In traditional digital storage oscilloscope (DSO), sampled data need to be processed after each acquisition. During data processing, the acquisition is stopped and oscilloscope is blind to the input signal. Thus, this duration is called dead time. With the rapid development of modern electronic systems, the effect of infrequent events becomes significant. To capture these occasional events in shorter time, dead time in traditional DSO that causes the loss of measured signal needs to be reduced or even eliminated. In this paper, a seamless acquisition oscilloscope without dead time is proposed. In this oscilloscope, three-dimensional waveform mapping (TWM) technique, which converts sampled data to displayed waveform, is proposed. With this technique, not only the process speed is improved, but also the probability information of waveform is displayed with different brightness. Thus, a three-dimensional waveform is shown to the user. To reduce processing time further, parallel TWM which processes several sampled points simultaneously, and dual-port random access memory based pipelining technique which can process one sampling point in one clock period are proposed. Furthermore, two DDR3 (Double-Data-Rate Three Synchronous Dynamic Random Access Memory) are used for storing sampled data alternately, thus the acquisition can continue during data processing. Therefore, the dead time of DSO is eliminated. In addition, a double-pulse test method is adopted to test the waveform capturing rate (WCR) of the oscilloscope and a combined pulse test method is employed to evaluate the oscilloscope's capture ability comprehensively. The experiment results show that the WCR of the designed oscilloscope is 6 250 000 wfms/s (waveforms per second), the highest value in all existing oscilloscopes. The testing results also prove that there is no dead time in our oscilloscope, thus realizing the seamless acquisition.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

PyZebrascope: An Open-Source Platform for Brain-Wide Neural Activity Imaging in Zebrafish

Understanding how neurons interact across the brain to control animal behaviors is one of the central goals in neuroscience. Recent developments in fluorescent microscopy and genetically-encoded calcium indicators led to the establishment of whole-brain imaging methods in zebrafish, which record neural activity across a brain-wide volume with single-cell resolution. Pioneering studies of whole-brain imaging used custom light-sheet microscopes, and their operation relied on commercially developed and maintained software not available globally. Hence it has been challenging to disseminate and develop the technology in the research community. Here, we present PyZebrascope, an open-source Python platform designed for neural activity imaging in zebrafish using light-sheet microscopy. PyZebrascope has intuitive user interfaces and supports essential features for whole-brain imaging, such as two orthogonal excitation beams and eye damage prevention. Its camera module can handle image data throughput of up to 800 MB/s from camera acquisition to file writing while maintaining stable CPU and memory usage. Its modular architecture allows the inclusion of advanced algorithms for microscope control and image processing. As a proof of concept, we implemented a novel automatic algorithm for maximizing the image resolution in the brain by precisely aligning the excitation beams to the image focal plane. PyZebrascope enables whole-brain neural activity imaging in fish behaving in a virtual reality environment. Thus, PyZebrascope will help disseminate and develop light-sheet microscopy techniques in the neuroscience community and advance our understanding of whole-brain neural dynamics during animal behaviors.

59 BASIC BIOLOGICAL SCIENCES↗