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The COVID-19 global pandemic created an optimal environment for counterfeiters to exploit vulnerabilities in the manufacturing industry. The decentralized and global nature of additive manufacturing (AM) systems created new attack vectors for counterfeiting due to ease of compromise of product and process information. To solve this challenge, innovative technologies and scientifically reliable methods for predicting authenticity are in great demand. In this work, a framework for differentiating between authentic and counterfeit AM automotive and aerospace components is proposed. Extant literature was reviewed and current anti-counterfeit technologies analyzed, informing the basis of the framework. The process was validated with a castle nut printed via selective laser-melting of a stainless steel M18 castle nut slightly modified with a Cantor dust fractal. The castle nut was then inspected with 225 and 450 kV X-ray tomography to assess the fidelity of fractal structure. A model trust anchor image sequence was developed and analyzed with a perceptual hash, Hamming distance computations, distribution functions, and null hypothesis for proof of authenticity. For authentic parts, a blockchain was updated with provenance. Future work will explore wider implementation with the goal of reducing prevalence of counterfeit parts in AM manufacturing systems.

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.

Malicious insider attacks affect all sectors, economies, and organizations of the world, with insider cyber-sabotage causing significant damage. Key findings from previous research on malicious insider threats underscore a need for prevention and detection programs that are broadly based. Current risk management administrative and technical mitigation techniques often include indicators of potential malicious insiders in the form of personal factors and observable behaviors. However, these need empirical validation in the context of cyber-sabotage. This article builds on previous research on insider threats and investigates the relationship between motives, behavioral indicators, and insider cyber-sabotage. We examine 74 actual cases of convicted insider threat attacks using a logistic regression model. The results confirm hypotheses that revenge (one of the motives), addiction (one of the observable behavioral indicators), and poor work performance increase the likelihood of insider sabotage. The findings provide empirical validation of cyber-sabotage specific indicators for insider threat programs. Implications and future research are discussed.

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.

“Insiders” remain a significant threat to organizations—evidenced by recent cases involving Robert Hansen, Bradley Manning, and Edward Snowden—even in light of significant movement toward neutralizing the threat through detection and prevention. Insiders pose detection challenges for security professionals because they often have legitimate access and intimate organizational knowledge. Nonetheless, past insider threat detection research has predominantly focused on signature-based detection of digital indicators of insider activity and behavioral profiling. This article develops a novel relationship between addiction theory and the insider threat from an information systems perspective. This discussion introduces seven propositions concerning this relationship, addiction antecedents, and the factors moderating the relationship between addiction and the insider threat. This model has significant implications for the insider threat detection challenge, as it provides new signals that may be useful for detec...

Security incidents occur at an alarming rate with malicious software (malware) involved in a large number of these incidents. Current malware evaluation and handling practices in organizations are unstandardized and intelligence regarding threat levels often comes from vendors and peers, who lack a unified threat metric system. This paper explores a method to quantify malware threats systematically and proposes a quantitative malware threat metric system. The proposed method makes communicating the level of threat more effective and efficient, particularly for information sharing organizations.

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.