The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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Een grote groep Nederlanders wordt jaarlijks slachtoffer van phishing. Burgers en bedrijven nemen echter in te beperkte mate zelfbeschermende maatregelen. In dit onderzoek wordt in kaart gebracht welke factoren bijdragen aan de intentie om zelfbeschermende maatregelen te nemen tegen phishing door drie risicogroepen, namelijk jongeren, ouderen en mkb’ers. We passen de Protection Motivation Theory toe, en onderbouwen een uitbreiding van dit model met twee factoren: affectieve respons en subjectieve normen. Data is verzameld middels vragenlijstonderzoek bij een panelbureau onder jongeren (N=1179), ouderen (N=1191) en mkb’ers (N=1020). De sterkste voorspeller voor de intentie tot het nemen van zelfbeschermende maatregelen tegen phishing bleek de affectieve respons (zorgen maken om phishing), gevolgd door een negatief effect van zelfeffectiviteit en positieve effecten van waargenomen ernst (jongeren en mkb’ers) en subjectieve norm (mkb’ers). Implicaties van de bevindingen voor handhavers en interventies worden besproken.
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Entrepreneurs are likely to be victims of ransomware. Previous studies have found that entrepreneurs tend to adopt few preventive measures, thereby increasing their chances of victimization. Due to a lack of research, however, not much is known about why entrepreneurs lack self-protective behaviors and how they can be encouraged to change said behaviors. Therefore, the purpose of this study is to explain, by means of an extended model of the Protection Motivation Theory (PMT), the motivation for entrepreneurs using protective measures against ransomware in the future. The data for our study were collected thanks to a questionnaire that was answered by 1,020 Dutch entrepreneurs with up to 250 employees. Our Structural Equation Modelling (SEM) analysis revealed that entrepreneurs are more likely to take preventive measures against ransomware if they perceive the risk of ransomware as severe (perceived severity), if they perceive their company as being vulnerable (perceived vulnerability), if they are concerned about the risks (affective response), and if they think that the people and companies around them expect them to apply preventive measures (subjective norms). However, if entrepreneurs think that they are capable of handling the risk (self-efficacy) and are convinced that their adopted preventive measures are effective (response efficacy), they are less likely to take preventive measures. Furthermore, for entrepreneurs that outsource IT security, the significant effect of perceived vulnerability and subjective norms disappears. The likelihood of entrepreneurs protecting their business against ransomware is thus influenced by a complex interplay of various motivational factors and is partly dependent on the business’ characteristics. Based on these findings, we will discuss security professionals’ prospects for increasing the cyber resilience of entrepreneurs, thus preventing cybercrime victimization.
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This article examines the network structure, criminal cooperation, and external interactions of cybercriminal networks. Its contribution is empirical and inductive. The core of this study involved carrying out 10 case analyses on closed cybercrime investigations – all with financial motivations on the part of the offenders - in the UK and beyond. Each analysis involved investigator interview and access to unpublished law enforcement files. The comparison of these cases resulted in a wide range of findings on these cybercriminal networks, including: a common division between the scam/attack components and the money components; the presence of offline/local elements; a broad, and sometimes blurred, spectrum of cybercriminal behaviour and organisation. An overarching theme across the cases that we observe is that cybercriminal business models are relatively stable.
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Purpose: The aims of this study were to investigate how a variety of research methods is commonly employed to study technology and practitioner cognition. User-interface issues with infusion pumps were selected as a case because of its relevance to patient safety. Methods: Starting from a Cognitive Systems Engineering perspective, we developed an Impact Flow Diagram showing the relationship of computer technology, cognition, practitioner behavior, and system failure in the area of medical infusion devices. We subsequently conducted a systematic literature review on user-interface issues with infusion pumps, categorized the studies in terms of methods employed, and noted the usability problems found with particular methods. Next, we assigned usability problems and related methods to the levels in the Impact Flow Diagram. Results: Most study methods used to find user interface issues with infusion pumps focused on observable behavior rather than on how artifacts shape cognition and collaboration. A concerted and theorydriven application of these methods when testing infusion pumps is lacking in the literature. Detailed analysis of one case study provided an illustration of how to apply the Impact Flow Diagram, as well as how the scope of analysis may be broadened to include organizational and regulatory factors. Conclusion: Research methods to uncover use problems with technology may be used in many ways, with many different foci. We advocate the adoption of an Impact Flow Diagram perspective rather than merely focusing on usability issues in isolation. Truly advancing patient safety requires the systematic adoption of a systems perspective viewing people and technology as an ensemble, also in the design of medical device technology.
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