Blockchain-enabled EHR access auditing: Enhancing healthcare data security.

In the realm of modern healthcare, Electronic Health Records EHR serve as invaluable assets, yet they also pose significant security challenges. The absence of EHR access auditing mechanisms, which includes the EHR audit trails, results in accountability gaps and magnifies security vulnerabilities. This situation effectively paves the way for unauthorized data alterations to occur without detection or consequences. Inadequate EHR compliance auditing procedures, particularly in verifying and validating access control policies, expose healthcare organizations to risks such as data breaches, and unauthorized data usage. These vulnerabilities result from unchecked unauthorized access activities. Additionally, the absence of EHR audit logs complicates investigations, weakens proactive security measures, and raises concerns to put healthcare institutions at risk. This study addresses the pressing need for robust EHR auditing systems designed to scrutinize access to EHR data, encompassing who accesses it, when, and for what purpose. Our research delves into the complex field of EHR auditing, which includes establishing an immutable audit trail to enhance data security through blockchain technology. We also integrate Purpose-Based Access Control (PBAC) alongside smart contracts to strengthen compliance auditing by validating access legitimacy and reducing unauthorized entries. Our contributions encompass the creation of audit trail of EHR access, compliance auditing via PBAC policy verification, the generation of audit logs, and the derivation of data-driven insights, fortifying EHR access security.

Statement Recognition of Access Control Policies in IoT Networks.

Access Control Policies (ACPs) are essential for ensuring secure and authorized access to resources in IoT networks. Recognizing these policies involves identifying relevant statements within project documents expressed in natural language. While current research focuses on improving recognition accuracy through algorithm enhancements, the challenge of limited labeled data from individual clients is often overlooked, which impedes the training of highly accurate models. To address this issue and harness the potential of IoT networks, this paper presents FL-Bert-BiLSTM, a novel model that combines federated learning and pre-trained word embedding techniques for access control policy recognition. By leveraging the capabilities of IoT networks, the proposed model enables real-time and distributed training on IoT devices, effectively mitigating the scarcity of labeled data and enhancing accessibility for IoT applications. Additionally, the model incorporates pre-trained word embeddings to leverage the semantic information embedded in textual data, resulting in improved accuracy for access control policy recognition. Experimental results substantiate that the proposed model not only enhances accuracy and generalization capability but also preserves data privacy, making it well-suited for secure and efficient access control in IoT networks.

Cloud-Based Zero Trust Access Control Policy: An Approach to Support Work-From-Home Driven by COVID-19 Pandemic.

The ubiquitous cloud computing services provide a new paradigm to the work-from-home environment adopted by the enterprise in the unprecedented crisis of the COVID-19 outbreak. However, the change in work culture would also increase the chances of the cybersecurity attack, MAC spoofing attack, and DDoS/DoS attack due to the divergent incoming traffic from the untrusted network for accessing the enterprise's resources. Networks are usually unable to detect spoofing if the intruder already forges the host's MAC address. However, the techniques used in the existing researches mistakenly classify the malicious host as the legitimate one. This paper proposes a novel access control policy based on a zero-trust network by explicitly restricting the incoming network traffic to substantiate MAC spoofing attacks in the software-defined network (SDN) paradigm of cloud computing. The multiplicative increase and additive decrease algorithm helps to detect the advanced MAC spoofing attack before penetrating the SDN-based cloud resources. Based on the proposed approach, a dynamic threshold is assigned to the incoming port number. The self-learning feature of the threshold stamping helps to rectify a legitimate user's traffic before classifying it to the attacker. Finally, the mathematical and experimental results exhibit high accuracy and detection rate than the existing methodologies. The novelty of this approach strengthens the security of the SDN paradigm of cloud resources by redefining conventional access control policy.

Access Control for Emerging Distributed Systems.

Technologies such as BigData, Cloud, Grid, and IoT are reshaping current data systems and practices, and IT experts are just as keen on harnessing the power of distributed systems to boost security and prevent fraud. How can massive distributed system capabilities be used to improve processing, instead of inflating risk?