SD
S.J. Duveen
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Cybercrime is becoming increasingly sophisticated, creating major challenges for cybersecurity and, in particular, network forensics. Network forensics analysts play an essential role in detecting intrusions, investigating incidents, and preventing attacks through the analysis of network traffic. Yet the volume and complexity of network data, combined with the limits of current tools, make this work time-consuming and error-prone. This study addresses these challenges by designing and developing a prototype visualisation tool tailored to the needs of network forensics analysts. The research follows a structured four-phase methodology. First, interviews with professional analysts identify key tasks and challenges, forming the basis for the tool’s requirements. Second, the What-Why-How framework maps these tasks to appropriate visualisation techniques. Third, iterative prototyping translates these insights into an interactive prototype tool, refined through user feedback. Finally, an impact analysis assesses effectiveness, intuitiveness, and correctness by evaluating tasks performed by both experienced and inexperienced participants. The evaluation shows that the prototype significantly improves analysis workflows, lowering cognitive load, increasing efficiency, and reducing errors. Both experienced and inexperienced participants performed tasks faster using the prototype, demonstrating both accessibility and a reduced learning curve. The findings also highlight the value of a user-centred, task-oriented approach to visualisation design. This research contributes to network forensics by presenting a structured approach to developing domain-specific visualisation tools. The results highlight their potential to enhance the speed, accuracy, and reliability of analysis, strengthening cybersecurity as a whole.
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Cybercrime is becoming increasingly sophisticated, creating major challenges for cybersecurity and, in particular, network forensics. Network forensics analysts play an essential role in detecting intrusions, investigating incidents, and preventing attacks through the analysis of network traffic. Yet the volume and complexity of network data, combined with the limits of current tools, make this work time-consuming and error-prone. This study addresses these challenges by designing and developing a prototype visualisation tool tailored to the needs of network forensics analysts. The research follows a structured four-phase methodology. First, interviews with professional analysts identify key tasks and challenges, forming the basis for the tool’s requirements. Second, the What-Why-How framework maps these tasks to appropriate visualisation techniques. Third, iterative prototyping translates these insights into an interactive prototype tool, refined through user feedback. Finally, an impact analysis assesses effectiveness, intuitiveness, and correctness by evaluating tasks performed by both experienced and inexperienced participants. The evaluation shows that the prototype significantly improves analysis workflows, lowering cognitive load, increasing efficiency, and reducing errors. Both experienced and inexperienced participants performed tasks faster using the prototype, demonstrating both accessibility and a reduced learning curve. The findings also highlight the value of a user-centred, task-oriented approach to visualisation design. This research contributes to network forensics by presenting a structured approach to developing domain-specific visualisation tools. The results highlight their potential to enhance the speed, accuracy, and reliability of analysis, strengthening cybersecurity as a whole.
Large volumes of medical data (MD) are continuously generated by the healthcare domain. When sharing these data, issues arise regarding privacy and security. To solve these issues, a permissioned blockchain (BC) can be used, but since blockchains do not have access control (AC) as a default feature, the integration of an access control system (ACS) is necessary to ensure the confidentiality of the medical data. The main question that we aim to answer is: How can access control techniques (ACT) be incorporated into a BC-based medical data sharing system (MDSS)? To answer this question, we created an access control system (ACS), based on HyperLedger Fabric, after evaluating existing techniques, with the use of a set of questions, that were chosen specifically for this purpose. Our ACS uses a smart contract, called the Access Contract to restrict access, based on access levels and permission queries, which are stored in the state ledgers of HyperLedger Fabric's world state. The Access Contract defines the necessary transactions for an ACS, in which these variables are used. Our ACS satisfies more metric questions than the related works's average and is thus optimal. We found that AC can be incorporated into a BC-based MDSS, by utilizing smart contracts to define the needed transactions that use access levels and permission queries to restrict the access of users.
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Large volumes of medical data (MD) are continuously generated by the healthcare domain. When sharing these data, issues arise regarding privacy and security. To solve these issues, a permissioned blockchain (BC) can be used, but since blockchains do not have access control (AC) as a default feature, the integration of an access control system (ACS) is necessary to ensure the confidentiality of the medical data. The main question that we aim to answer is: How can access control techniques (ACT) be incorporated into a BC-based medical data sharing system (MDSS)? To answer this question, we created an access control system (ACS), based on HyperLedger Fabric, after evaluating existing techniques, with the use of a set of questions, that were chosen specifically for this purpose. Our ACS uses a smart contract, called the Access Contract to restrict access, based on access levels and permission queries, which are stored in the state ledgers of HyperLedger Fabric's world state. The Access Contract defines the necessary transactions for an ACS, in which these variables are used. Our ACS satisfies more metric questions than the related works's average and is thus optimal. We found that AC can be incorporated into a BC-based MDSS, by utilizing smart contracts to define the needed transactions that use access levels and permission queries to restrict the access of users.