Introduction to Deception, Digital Forensics, and Malware Minitrack

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Reducing Complex Visualizations for Analysis

Data visualization provides a means to present known information in a format that is easily consumable and does not generally require specialized training. It is also well-suited to aid an analyst in discovering previously unknown information [1]. This is possible because visualization techniques can be used to highlight internal relationships and structures within the data, and present them in a graphical manner. Using visualization during the preliminary analysis phase can provide a pathway to enable an analyst to discover patterns or anomalies within the data that might otherwise go undiscovered as humans have an innate ability to visually identify patterns and anomalies. \ \ Even when an analyst has identified a pattern or anomaly within the data, creating an algorithm that allows for automated detection of other occurrences of the same, or similar, patterns is a non-trivial task. While humans are innately skilled at pattern recognition, computers are not, and patterns that might be obvious for a human to identify might be difficult for a computer to detect even when assisted by a skilled analyst [2]. This paper describes a method of taking a complex visualization, and reducing it into several smaller components in order to facilitate computer analysis of the analyst-identified patterns or anomalies in the data. From there, a detection scheme can be generated through an analyst-supervised data analysis process in order to find more occurrences in a larger dataset

Use My Digital Forensics Tool...It’s Shiny!

The tendency to use technologies without fully understanding the potential ramifications extends to all reaches of our lives. Digital forensics is not immune from this phenomenon. This paper discusses some past scenarios in which conclusions were drawn before all of the testing was complete. Digital forensics tools are then discussed including tool capabilities, tool analysis, and associated challenges. It identifies some potential issues and ramifications that may not be given appropriate consideration by digital forensic examiners or those who rely on these tools when weighing evidence. It concludes with some suggestions for future research directions that could answer some important questions about using digital forensics tools effectively

Introduction to Inside the Insider Threat Minitrack

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Investigating the Implications of Virtual Machine Introspection for Digital Forensics

Researchers and practitioners in computer forensics currently must base their analysis on information that is either incomplete or produced by tools that may themselves be compromised as a result of the intrusion. Complicating these issues are the techniques employed by the investigators themselves. If the system is quiescent when examined, most of the information in memory has been lost. If the system is active, the kernel and programs used by the forensic investigators are likely to influence the results and as such are themselves suspect. Using virtual machines and a technique called virtual machine introspection can help overcome these limits, but it introduces its own research challenges. Recent developments in virtual machine introspection have led to the identification of four initial priority research areas in virtual machine introspection including virtual machine introspection tool development, applications of virtual machine introspection to non-quiescent virtual machines, virtual machine introspection covert operations, and virtual machine introspection detection

A simple energy usage toolkit from manufacturing simulation data

A fundamental problem in energy management is the inability to clearly predict any possible energy saving opportunities. The cost of both under or overestimating potential returns on investment can be prohibitive to a decision maker. In recent years the simulation of energy usage using existing manufacturing simulation tools has increased in popularity among researchers, but it is energy managers who need to see the benefits of this discipline. This paper proposes an interactive manufacturing energy management tool which makes use of existing productivity simulation models for the prediction of energy usage. An interactive Microsoft® Excel® based tool is developed to control Lanner’s WITNESS® discrete-event simulation software using Microsoft® Visual Basic® for Applications. The tool has the ability to predict potential areas where energy saving opportunities can be made within a complex manufacturing line, and is accessible from management presentations and proposals. The interactivity of the tool provides an environment which facilitates efficient hypothesis testing. The paper includes an industrial case study where the approach was used to quantify theoretical savings from certain energy usage reduction scenarios within a complex automotive engine manufacturing line

Post-intervention Status in Patients With Refractory Myasthenia Gravis Treated With Eculizumab During REGAIN and Its Open-Label Extension

OBJECTIVE: To evaluate whether eculizumab helps patients with anti-acetylcholine receptor-positive (AChR+) refractory generalized myasthenia gravis (gMG) achieve the Myasthenia Gravis Foundation of America (MGFA) post-intervention status of minimal manifestations (MM), we assessed patients' status throughout REGAIN (Safety and Efficacy of Eculizumab in AChR+ Refractory Generalized Myasthenia Gravis) and its open-label extension. METHODS: Patients who completed the REGAIN randomized controlled trial and continued into the open-label extension were included in this tertiary endpoint analysis. Patients were assessed for the MGFA post-intervention status of improved, unchanged, worse, MM, and pharmacologic remission at defined time points during REGAIN and through week 130 of the open-label study. RESULTS: A total of 117 patients completed REGAIN and continued into the open-label study (eculizumab/eculizumab: 56; placebo/eculizumab: 61). At week 26 of REGAIN, more eculizumab-treated patients than placebo-treated patients achieved a status of improved (60.7% vs 41.7%) or MM (25.0% vs 13.3%; common OR: 2.3; 95% CI: 1.1-4.5). After 130 weeks of eculizumab treatment, 88.0% of patients achieved improved status and 57.3% of patients achieved MM status. The safety profile of eculizumab was consistent with its known profile and no new safety signals were detected. CONCLUSION: Eculizumab led to rapid and sustained achievement of MM in patients with AChR+ refractory gMG. These findings support the use of eculizumab in this previously difficult-to-treat patient population. CLINICALTRIALSGOV IDENTIFIER: REGAIN, NCT01997229; REGAIN open-label extension, NCT02301624. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that, after 26 weeks of eculizumab treatment, 25.0% of adults with AChR+ refractory gMG achieved MM, compared with 13.3% who received placebo

Minimal Symptom Expression' in Patients With Acetylcholine Receptor Antibody-Positive Refractory Generalized Myasthenia Gravis Treated With Eculizumab

The efficacy and tolerability of eculizumab were assessed in REGAIN, a 26-week, phase 3, randomized, double-blind, placebo-controlled study in anti-acetylcholine receptor antibody-positive (AChR+) refractory generalized myasthenia gravis (gMG), and its open-label extension

Models of classroom assessment for course-based research experiences

Course-based research pedagogy involves positioning students as contributors to authentic research projects as part of an engaging educational experience that promotes their learning and persistence in science. To develop a model for assessing and grading students engaged in this type of learning experience, the assessment aims and practices of a community of experienced course-based research instructors were collected and analyzed. This approach defines four aims of course-based research assessment—(1) Assessing Laboratory Work and Scientific Thinking; (2) Evaluating Mastery of Concepts, Quantitative Thinking and Skills; (3) Appraising Forms of Scientific Communication; and (4) Metacognition of Learning—along with a set of practices for each aim. These aims and practices of assessment were then integrated with previously developed models of course-based research instruction to reveal an assessment program in which instructors provide extensive feedback to support productive student engagement in research while grading those aspects of research that are necessary for the student to succeed. Assessment conducted in this way delicately balances the need to facilitate students’ ongoing research with the requirement of a final grade without undercutting the important aims of a CRE education