BlockU: extended usage control in and for Blockchain

An electronic business transaction among untrusted bodies without consulting a mutually trusted party has remained widely accepted problem. Blockchain resolves this problem by introducing peer-to-peer network with a consensus algorithm and trusted ledger. Blockchain originally introduced for cryptocurrency that came with proof-of-work consensus algorithm. Due to some performance issues, scientists brought concept of permissioned Blockchain. Hyperledger Fabric is a permissioned Blockchain targeting business-oriented problems for industry. It is designed for efficient transaction execution over Blockchain with pluggable consensus model; however, there is limitation of rapid application development. Hyperledger introduced a new layer called Hyperledger Composer on top of the Fabric layer, which provides an abstract layer to model the business application readily and quickly. Composer provides a smart contract to extend the functionality and flexibility of Fabric layer and provides a way of communication with other systems to meet business requirements. Hyperledger Composer uses role-based access control (RBAC) model to secure access to its valuable assets. However, RBAC is not enough because many business deals require continuous assets monitoring. Our proposed model, BlockU, covers all possible access control models required by a business. BlockU can monitor assets continuously during transactions and updates attributes accordingly. Moreover, we incorporate hooks in Hyperledger Composer to implement extended permission model that provides extensive permission management capability on an asset. Subsequently, our proposed enhanced access control model is implemented with a minimal change to existing Composer code base and is backward compatible with the current security mechanism.info:eu-repo/semantics/publishedVersio

Application of model based post-stack inversion in the characterization of reservoir sands containing porous, tight and mixed facies: a case study from the Central Indus Basin, Pakistan

Porosity is a key parameter for reservoir evaluation. Inferring the porosity from seismic data is often challenging and prone to uncertainties due to number of factors. The main aim of this paper is to show the applicability of seismic inversion on old vintage seismic data to map spatial porosity at reservoir level. 3D-seismic and wireline log data are used to map the reservoir properties of the Lower Goru productive sands in the Gambat Latif block, Central Indus Basin, Pakistan. The Lower Goru formation was interpreted with the help of seismic and well data. Interpreted horizons are thus further used in model-based seismic inversion techniques to map the spatial distribution of porosity. Well-log data are used in the construction of low acoustic impedance models. Calibration of reservoir porosity with inverted acoustic impedance is achieved through well-log data. The results from model-based inversion reasonably estimate the porosity distribution within the C-sand interval of the Lower Goru Member. After post-stack inversion, the porosity values at wells Tajjal-01, Tajjal-02 and Tajjal-03 are 10%, 8% and 12%, respectively. Porosity values calculated from post-stack inversion at the corresponding well locations are in good agreement with the borehole-derived porosity

Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques

Seismic post-stack inversion facilitates the interpretation, mapping and quantification of hydrocarbon-bearing zones. This study estimates reservoir properties (i.e. acoustic impedance and porosity) by applying post-stack seismic inversion techniques to a gas prone reservoir in the Sawan area, Southern Indus Basin, Pakistan. In this particular study, model-based and sparse-spike inversion algorithms are successfully applied on 3D seismic and wireline log data to predict reservoir character in the Lower Goru Formation (C-sand interval). Our results suggest that model-based post-stack seismic inversion provides more reasonable estimates (i.e. returning detailed spatial variations) for acoustic impedance and porosity when compared to sparse-spike inversion algorithms. The calibration of these estimates with petrophysical data from wireline log data indicates an appropriate agreement amongst them. Importantly, the results obtained in our case study can be applied to similar basins in Asia with 'tight' oil and 'tight' gas filling sand-shale intercalations with different thickness and areal distributions

Resource potential of gas reservoirs in South Pakistan and adjacent Indian subcontinent revealed by post-stack inversion techniques

Seismic post-stack inversion facilitates the interpretation, mapping and quantification of hydrocarbon-bearing zones. This study estimates reservoir properties (i.e. acoustic impedance and porosity) by applying post-stack seismic inversion techniques to a gas prone reservoir in the Sawan area, Southern Indus Basin, Pakistan. In this particular study, model-based and sparse-spike inversion algorithms are successfully applied on 3D seismic and wireline log data to predict reservoir character in the Lower Goru Formation (C-sand interval). Our results suggest that model-based post-stack seismic inversion provides more reasonable estimates (i.e. returning detailed spatial variations) for acoustic impedance and porosity when compared to sparse-spike inversion algorithms. The calibration of these estimates with petrophysical data from wireline log data indicates an appropriate agreement amongst them. Importantly, the results obtained in our case study can be applied to similar basins in Asia with 'tight' oil and 'tight' gas filling sand-shale intercalations with different thickness and areal distributions

ForensicTransMonitor: A Comprehensive Blockchain Approach to Reinvent Digital Forensics and Evidence Management

In the domain of computer forensics, ensuring the integrity of operations like preservation, acquisition, analysis, and documentation is critical. Discrepancies in these processes can compromise evidence and lead to potential miscarriages of justice. To address this, we developed a generic methodology integrating each forensic transaction into an immutable blockchain entry, establishing transparency and authenticity from data preservation to final reporting. Our framework was designed to manage a wide range of forensic applications across different domains, including technology-focused areas such as the Internet of Things (IoT) and cloud computing, as well as sector-specific fields like healthcare. Centralizing our approach are smart contracts that seamlessly connect forensic applications to the blockchain via specialized APIs. Every action within the forensic process triggers a verifiable transaction on the blockchain, enabling a comprehensive and tamper-proof case presentation in court. Performance evaluations confirmed that our system operates with minimal overhead, ensuring that the integration bolsters the judicial process without hindering forensic investigations

Integrating Blockchain and Deep Learning for Enhanced Mobile VPN Forensics: A Comprehensive Framework

In an era marked by technological advancement, the rising reliance on Virtual Private Networks (VPNs) necessitates sophisticated forensic analysis techniques to investigate VPN traffic, especially in mobile environments. This research introduces an innovative approach utilizing Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) for classifying VPN traffic, aiding forensic investigators in precisely identifying applications or websites accessed via VPN connections. By leveraging the combined strengths of CNNs and GNNs, our method provides an effective solution for discerning user activities during VPN sessions. Further extending this framework, we incorporate blockchain technology to meticulously record all mobile VPN transactions, ensuring a tamper-proof and transparent ledger that significantly bolsters the integrity and admissibility of forensic evidence in legal scenarios. A specific use-case demonstrates this methodology in mobile forensics, where our integrated approach not only accurately classifies data traffic but also securely logs transactional details on the blockchain, offering an unprecedented level of detail and reliability in forensic investigations. Extensive real-world VPN dataset experiments validate our approach, highlighting its potential to achieve high accuracy and offering invaluable insights for both technological and legal domains in the context of mobile VPN usage

ICT-Based Solutions for Alzheimer’s Disease Care: A Systematic Review

In recent years, there has been a growing recognition of the significant challenges posed by Alzheimer’s Disease (AD) and the need for innovative solutions to improve the quality of life for affected individuals. As AD prevalence continues to rise, technological advancements offer promising opportunities to address the multifaceted needs of patients and caregivers. This survey paper thoroughly investigates technological innovations in AD care, offering valuable insights into cutting-edge approaches that have the potential to positively impact the lives of affected individuals. By providing a holistic view of available assistive solutions, we review 2459 papers and selected 46 relevant studies published between 2015 and 2023, specifically focusing on healthcare technologies and solutions, utilizing sensing methods. The former will include Telemedicine, E-health, Smart Environment, Internet of Things (IoT), Ambient Assisted Living (AAL), Internet of Medical Things (IoMT), and Personalized Assistive Solutions (PAS), while the latter encompasses Wearable/Environmental, Radio/Audio, Video/Image, and Digital Platforms. Our comparative assessment of recent survey papers reveals the unique contribution of this study, as it comprehensively examines the intersection of multiple parameters. By summarizing insights from these studies, we identify gaps and recommend future directions for advancements in AD care

Integrity and Privacy-Aware, Patient-Centric Health Record Access Control Framework Using a Blockchain

Blockchains are gaining traction as secure and reliable platforms for data sharing in fields such as banking, supply chain management, food production, energy, the Internet, and medical services. Furthermore, when decentralized, a blockchain can be regarded as an immutable ledger storing data entries. Moreover, this modern technology was designed to disrupt various data-driven industries, including the healthcare industry. While electronic healthcare services have enabled more straightforward and accessible treatment, patient privacy has become vulnerable to external and internal attacks by healthcare personnel. Therefore, we aimed to design a framework to control patient health records that ensures the patient can provide the necessary permissions to those who access his/her health records. This framework will record all activities via blockchain and usage control. Through this framework, we aim to create a user-centric and privacy-aware experience. A literature review and experiments have been performed to select an optimized and placable blockchain operating system. In addition, performance analysis showed that the OS and smart contracts work at an acceptable speed