Brian Weber

Luke Koch, Sean Oesch, Mary Adkisson et al.

Standardized file formats play a key role in the development and use of computer software. However, it is possible to abuse standardized file formats by creating a file that is valid in multiple file formats. The resulting polyglot (many languages) file can confound file format identification, allowing elements of the file to evade analysis.This is especially problematic for malware detection systems that rely on file format identification for feature extraction. File format identification processes that depend on file signatures can be easily evaded thanks to flexibility in the format specifications of certain file formats. Although work has been done to identify file formats using more comprehensive methods than file signatures, accurate identification of polyglot files remains an open problem. Since malware detection systems routinely perform file format-specific feature extraction, polyglot files need to be filtered out prior to ingestion by these systems. Otherwise, malicious content could pass through undetected. To address the problem of polyglot detection we assembled a data set using the mitra tool. We then evaluated the performance of the most commonly used file identification tool, file. Finally, we demonstrated the accuracy, precision, recall and F1 score of a range of machine and deep learning models. Malconv2 and Catboost demonstrated the highest recall on our data set with 95.16% and 95.45%, respectively. These models can be incorporated into a malware detector's file processing pipeline to filter out potentially malicious polyglots before file format-dependent feature extraction takes place.

Justyna P. Zwolak, Jacob M. Taylor, Reed W. Andrews et al.

Gate-defined quantum dots are a promising candidate system for realizing scalable, coupled qubit systems and serving as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. This meeting report outlines current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present insights and ideas put forward by the quantum dot community on how to overcome them. We aim to provide guidance and inspiration to researchers invested in automation efforts.

Brian Weber, Justyna P. Zwolak

Gate-defined semiconductor quantum dot (QD) arrays are a promising platform for quantum computing. However, presently, the large configuration spaces and inherent noise make tuning of QD devices a nontrivial task and with the increasing number of QD qubits, the human-driven experimental control becomes unfeasible. Recently, researchers working with QD systems have begun putting considerable effort into automating device control, with a particular focus on machine-learning-driven methods. Yet, the reported performance statistics vary substantially in both the meaning and the type of devices used for testing. While systematic benchmarking of the proposed tuning methods is necessary for developing reliable and scalable tuning approaches, the lack of openly available standardized datasets of experimental data makes such testing impossible. The QD auto-annotator -- a classical algorithm for automatic interpretation and labeling of experimentally acquired data -- is a critical step toward rectifying this. QD auto-annotator leverages the principles of geometry to produce state labels for experimental double-QD charge stability diagrams and is a first step towards building a large public repository of labeled QD data.

Savannah Norem, Ashley E Rice, Samantha Erwin et al.

Security operation centers (SOCs) all over the world are tasked with reacting to cybersecurity alerts ranging in severity. Security Orchestration, Automation, and Response (SOAR) tools streamline cybersecurity alert responses by SOC operators. SOAR tool adoption is expensive both in effort and finances. Hence, it is crucial to limit adoption to those most worthwhile; yet no research evaluating or comparing SOAR tools exists. The goal of this work is to evaluate several SOAR tools using specific criteria pertaining to their usability. SOC operators were asked to first complete a survey about what SOAR tool aspects are most important. Operators were then assigned a set of SOAR tools for which they viewed demonstration and overview videos, and then operators completed a second survey wherein they were tasked with evaluating each of the tools on the aspects from the first survey. In addition, operators provided an overall rating to each of their assigned tools, and provided a ranking of their tools in order of preference. Due to time constraints on SOC operators for thorough testing, we provide a systematic method of downselecting a large pool of SOAR tools to a select few that merit next-step hands-on evaluation by SOC operators. Furthermore, the analyses conducted in this survey help to inform future development of SOAR tools to ensure that the appropriate functions are available for use in a SOC.

Brian J. Weber, Sandesh S. Kalantre, Thomas McJunkin et al.

The problem of classifying high-dimensional shapes in real-world data grows in complexity as the dimension of the space increases. For the case of identifying convex shapes of different geometries, a new classification framework has recently been proposed in which the intersections of a set of one-dimensional representations, called rays, with the boundaries of the shape are used to identify the specific geometry. This ray-based classification (RBC) has been empirically verified using a synthetic dataset of two- and three-dimensional shapes (Zwolak et al. in Proceedings of Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada [December 11, 2020], arXiv:2010.00500, 2020) and, more recently, has also been validated experimentally (Zwolak et al., PRX Quantum 2:020335, 2021). Here, we establish a bound on the number of rays necessary for shape classification, defined by key angular metrics, for arbitrary convex shapes. For two dimensions, we derive a lower bound on the number of rays in terms of the shape's length, diameter, and exterior angles. For convex polytopes in $\mathbb{R}^N$, we generalize this result to a similar bound given as a function of the dihedral angle and the geometrical parameters of polygonal faces. This result enables a different approach for estimating high-dimensional shapes using substantially fewer data elements than volumetric or surface-based approaches.

Justyna P. Zwolak, Sandesh S. Kalantre, Thomas McJunkin et al.

While classification of arbitrary structures in high dimensions may require complete quantitative information, for simple geometrical structures, low-dimensional qualitative information about the boundaries defining the structures can suffice. Rather than using dense, multi-dimensional data, we propose a deep neural network (DNN) classification framework that utilizes a minimal collection of one-dimensional representations, called \emph{rays}, to construct the "fingerprint" of the structure(s) based on substantially reduced information. We empirically study this framework using a synthetic dataset of double and triple quantum dot devices and apply it to the classification problem of identifying the device state. We show that the performance of the ray-based classifier is already on par with traditional 2D images for low dimensional systems, while significantly cutting down the data acquisition cost.

Alan Sherman, Enis Golaszewski, Edward LaFemina et al.

May 30-June 2, 2017, Scholarship for Service (SFS) scholars at the University of Maryland, Baltimore County (UMBC) analyzed the security of a targeted aspect of the UMBC computer systems. During this hands-on study, with complete access to source code, students identified vulnerabilities, devised and implemented exploits, and suggested mitigations. As part of a pioneering program at UMBC to extend SFS scholarships to community colleges, the study helped initiate six students from two nearby community colleges, who transferred to UMBC in fall 2017 to complete their four-year degrees in computer science and information systems. The study examined the security of a set of "NetAdmin" custom scripts that enable UMBC faculty and staff to open the UMBC firewall to allow external access to machines they control for research purposes. Students discovered vulnerabilities stemming from weak architectural design, record overflow, and failure to sanitize inputs properly. For example, they implemented a record-overflow and code-injection exploit that exfiltrated the vital API key of the UMBC firewall. This report summarizes student activities and findings, and reflects on lessons learned for students, educators, and system administrators. Our students found the collaborative experience inspirational, students and educators appreciated the authentic case study, and IT administrators gained access to future employees and received free recommendations for improving the security of their systems. We hope that other universities can benefit from our motivational and educational strategy of teaming educators and system administrators to engage students in active project-based learning centering on focused questions about their university computer systems.