Self-correcting, synchronizing ring counter using integrated circuit devices

Three nand gate circuits are used to add error detection and reset logic circuitry for initiating and retaining the correct binary state in the flip-flop circuits of a ring counter. As the input signals are counted, the position of the specified state moves in ordered sequence around circuit loop

Network Effects and Data Breaches: Investigating the Impact of Information Sharing and the Cyber Black Market

This paper was motivated by the growing data breach activities confronting organizations. Building on the literature on information sharing and network effects, we attempt to empirically examine how the number of security breaches may change as a result of two opposing network effects in the data breach battlefield, namely, the positive network effects driven by industry-wide information sharing efforts, and the negative network effects driven by the supply and demand changes in the underground cybercrime ecosystem, and whether a feedback loop can be formed so that the information sharing efforts can influence the costs and availability of malicious tools and suppress their demand. As one of the first studies to empirically examine the dynamics in the cybercrime economy, our research will provide important policy guidance to improve collaborative mechanisms to enhance industry wide information security, and illuminate a new way to monitor and curtail the flow of cyber-criminal activities

Investigation on Willingness of Employees to Share Information Security Advice

As modern organizations rely more on their information systems, mitigating information security risks becomes essential. Weaknesses in the information security management chain have continued to be challenged by employees. Therefore, enhancing employee security awareness becomes critical. Considering the effectiveness of informal methods, this research examines security advice sharing as one of the operative ways. Accordingly, in this paper, by adapting the theory of planned behavior as our theoretical lens, we propose a conceptual model of factors that are anticipated to impact the willingness of employees to share security advice. Finally, conclusion and avenues for future research are discussed

Understanding Crowdsourcing Contest Fitness Strategic Decision Factors and Performance: An Expectation-Confirmation Theory Perspective

Contest-based intermediary crowdsourcing represents a powerful new business model for generating ideas or solutions by engaging the crowd through an online competition. Prior research has examined motivating factors such as increased monetary reward or demotivating factors such as project requirement ambiguity. However, problematic issues related to crowd contest fitness have received little attention, particularly with regard to crowd strategic decision-making and contest outcomes that are critical for success of crowdsourcing platforms as well as implementation of crowdsourcing models in organizations. Using Expectation-Confirmation Theory (ECT), we take a different approach that focuses on contest level outcomes by developing a model to explain contest duration and performance. We postulate these contest outcomes are a function of managing crowdsourcing participant contest-fitness expectations and disconfirmation, particularly during the bidding process. Our empirical results show that contest fitness expectations and disconfirmation have an overall positive effect on contest performance. This study contributes to theory by demonstrating the adaptability of ECT literature to the online crowdsourcing domain at the level of the project contest. For practice, important insights regarding strategic decision making and understanding how crowd contest-fitness are observed for enhancing outcomes related to platform viability and successful organizational implementation

Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors

Organizations face both opportunities and risks with big data analytics vendors, and the risks are now profound, as data has been likened to the oil of the digital era. The growing body of research at the nexus of big data analytics and cloud computing is examined from the economic perspective, based on agency theory (AT). A conceptual framework is developed for analyzing these opportunities and challenges regarding the use of big data analytics and cloud computing in e-business environments. This framework allows organizations to engage in contracts that target competitive parity with their service-oriented decision support system (SODSS) to achieve a competitive advantage related to their core business model. A unique contribution of this paper is its perspective on how to engage a vendor contractually to achieve this competitive advantage. The framework provides insights for a manager in selecting a vendor for cloud-based big data services

Detecting threatening insiders with lightweight media forensics

This research uses machine learning and outlier analysis to detect potentially hostile insiders through the automated analysis of stored data on cell phones, laptops, and desktop computers belonging to members of an organization. Whereas other systems look for specific signatures associated with hostile insider activity, our system is based on the creation of a “storage profile” for each user and then an automated analysis of all the storage profiles in the organization, with the purpose of finding storage outliers. Our hypothesis is that malicious insiders will have specific data and concentrations of data that differ from their colleagues and coworkers. By exploiting these differences, we can identify potentially hostile insiders. Our system is based on a combination of existing open source computer forensic tools and datamining algorithms. We modify these tools to perform a “lightweight” analysis based on statistical sampling over time. In this, our approach is both efficient and privacy sensitive. As a result, we can detect not just individuals that differ from their co-workers, but also insiders that differ from their historic norms. Accordingly, we should be able to detect insiders that have been “turned” by events or outside organizations. We should also be able to detect insider accounts that have been taken over by outsiders. Our project, now in its first year, is a three-year project funded by the Department of Homeland Security, Science and Technology Directorate, Cyber Security Division. In this paper we describe the underlying approach and demonstrate how the storage profile is created and collected using specially modified open source tools. We also present the results of running these tools on a 500GB corpus of simulated insider threat data created by the Naval Postgraduate School in 2008 under grant from the National Science Foundation

Assessing Non-Invasive Liver Function in Patients With Intestinal Failure Receiving Total Parenteral Nutrition-Results From the Prospective PNLiver Trial

Liver abnormalities in intestinal failure (IF) patients receiving parenteral nutrition (PN) can progress undetected by standard laboratory tests to intestinal failure associated liver disease (IFALD). The aim of this longitudinal study is to evaluate the ability of non-invasive liver function tests to assess liver function following the initiation of PN. Twenty adult patients with IF were prospectively included at PN initiation and received scheduled follow-up assessments after 6, 12, and 24 months between 2014 and 2019. Each visit included liver assessment (LiMAx [Liver Maximum Capacity] test, ICG [indocyanine green] test, FibroScan), laboratory tests (standard laboratory test, NAFLD [non-alcoholic fatty liver disease] score, FIB-4 [fibrosis-4] score), nutritional status (bioelectrical impedance analysis, indirect calorimetry), and quality of life assessment. The patients were categorized post-hoc based on their continuous need for PN into a reduced parenteral nutrition (RPN) group and a stable parenteral nutrition (SPN) group. While the SPN group (n = 9) had significantly shorter small bowel length and poorer nutritional status at baseline compared to the RPN group (n = 11), no difference in liver function was observed between the distinct groups. Over time, liver function determined by LiMAx did continuously decrease from baseline to 24 months in the SPN group but remained stable in the RPN group. This decrease in liver function assessed with LiMAx in the SPN group preceded deterioration of all other investigated liver function tests during the study period. Our results suggest that the liver function over time is primarily determined by the degree of intestinal failure. Furthermore, the LiMAx test appeared more sensitive in detecting early changes in liver function in comparison to other liver function tests

Key parameters linking cyber-physical trust anchors with embedded internet of things systems

Integration of the Internet of Things (IoT) in the automotive industry has brought benefits as well as security challenges. Significant benefits include enhanced passenger safety and more comprehensive vehicle performance diagnostics. However, current onboard and remote vehicle diagnostics do not include the ability to detect counterfeit parts. A method is needed to verify authentic parts along the automotive supply chain from manufacture through installation and to coordinate part authentication with a secure database. In this study, we develop an architecture for anti-counterfeiting in automotive supply chains. The core of the architecture consists of a cyber-physical trust anchor and authentication mechanisms connected to blockchain-based tracking processes with cloud storage. The key parameters for linking a cyber-physical trust anchor in embedded IoT include identifiers (i.e., serial numbers, special features, hashes), authentication algorithms, blockchain, and sensors. A use case was provided by a two-year long implementation of simple trust anchors and tracking for a coffee supply chain which suggests a low-cost part authentication strategy could be successfully applied to vehicles. The challenge is authenticating parts not normally connected to main vehicle communication networks. Therefore, we advance the coffee bean model with an acoustical sensor to differentiate between authentic and counterfeit tires onboard the vehicle. The workload of secure supply chain development can be shared with the development of the connected autonomous vehicle networks, as the fleet performance is degraded by vehicles with questionable replacement parts of uncertain reliability