PbDinEHR: A Novel Privacy by Design Developed Framework Using Distributed Data Storage and Sharing for Secure and Scalable Electronic Health Records Management

Privacy in Electronic Health Records (EHR) has become a significant concern in today's rapidly changing world, particularly for personal and sensitive user data. The sheer volume and sensitive nature of patient records require healthcare providers to exercise an intense quantity of caution during EHR implementation. In recent years, various healthcare providers have been hit by ransomware and distributed denial of service attacks, halting many emergency services during COVID-19. Personal data breaches are becoming more common day by day, and privacy concerns are often raised when sharing data across a network, mainly due to transparency and security issues. To tackle this problem, various researchers have proposed privacy-preserving solutions for EHR. However, most solutions do not extensively use Privacy by Design (PbD) mechanisms, distributed data storage and sharing when designing their frameworks, which is the emphasis of this study. To design a framework for Privacy by Design in Electronic Health Records (PbDinEHR) that can preserve the privacy of patients during data collection, storage, access and sharing, we have analysed the fundamental principles of privacy by design and privacy design strategies, and the compatibility of our proposed healthcare principles with Privacy Impact Assessment (PIA), Australian Privacy Principles (APPs) and General Data Protection Regulation (GDPR). To demonstrate the proposed framework, ‘PbDinEHR', we have implemented a Patient Record Management System (PRMS) to create interfaces for patients and healthcare providers. In addition, to provide transparency and security for sharing patients' medical files with various healthcare providers, we have implemented a distributed file system and two permission blockchain networks using the InterPlanetary File System (IPFS) and Ethereum blockchain. This allows us to expand the proposed privacy by design mechanisms in the future to enable healthcare providers, patients, imaging labs and others to share patient-centric data in a transparent manner. The developed framework has been tested and evaluated to ensure user performance, effectiveness, and security. The complete solution is expected to provide progressive resistance in the face of continuous data breaches in the patient information domain.

FACVO-DNFN: Deep learning-based feature fusion and Distributed Denial of Service attack detection in cloud computing

Cloud computing offers a broad range of resource pools for conserving a huge quantity of information. Due to the intrusion of attackers, the information that exists in the cloud is threatened. Distributed Denial of Service (DDoS) attack is the main reason for attacks in the cloud. In this study, a Fractional Anti Corona Virus Optimization-based Deep Neuro-Fuzzy Network (FACVO-based DNFN) is devised for detecting DDoS in the cloud. The production of log files, feature fusion, data augmentation, and DDoS attack detection is the processing stages involved in this phase of the DDoS attack detection process. The feature fusion is carried out by RV coefficient and Deep Quantum Neural Network (Deep QNN), and the data augmentation is performed. Then, the Anti Corona Virus Optimization (ACVO) method and Fractional Calculus (FC) are both incorporated to create the FACVO algorithm. The DNFN is trained by the created FACVO algorithm, which identifies the DDoS attack. The proposed approach achieved testing accuracy, TPR, TNR, and precision values of 0.9304, 0.9088, 0.9293, and 0.8745 for using the NSL-KDD dataset without attack, and 0.9200, 0.8991, 0.9015, and 0.8648 for using the BoT-IoT dataset without attack. [ FROM AUTHOR]

BASIC CYBER DEFENCE EDUCATION FOR EVERYONE

Basic cyber defence has become a mandatory requirement nowadays, for almost everyone. Gone are the days when only computer science nerds and IT specialists where required to know and take care of the security aspects of electronic devices. Especially after the savage war unleashed by the Russian aggressors onto Ukraine, the whole world realized how important is the safety of our online environment. Only after both the Kremlin led aggressors and the civilized world attacked and counter-attacked within the online battlefield, especially and mostly based on different DDoS attack flavors, most people understood how important cyber defence is today. In an era where virtually everything is run/managed/supervised or at least aided by computers linked to the mother-network, the Internet, we rely on the technology being present, available, and working in all aspects of our lives.

Identifying Key Relationships between Nation-State Cyberattacks and Geopolitical and Economic Factors: A Model

Nation-state cyberattacks, and particularly Advanced Persistent Threats (APTs), have rocketed in the last years. Their use may be aligned with nation-state geopolitical and economic (GPE) interests, which are key for the underlying international relations (IRs). However, the interdependency between APTs and GPE (and thus IRs) has not been characterized yet and it could be a steppingstone for an enhanced cyberthreat intelligence (CTI). To address this limitation, a set of analytic models are proposed in this work. They are built considering 234M geopolitical events and 306 malicious software tools linked to 13 groups of 7 countries between 2000 and 2019. Models show a substantial support for launched and received cyberattacks considering GPE factors in most countries. Moreover, strategic issues are the key motivator when launching APTs. Therefore, from the CTI perspective, our results show that there is a likely cause-effect relationship between IRs (particularly GPE relevant indicators) and APTs.

Hybrid Cryptographic Scheme for Secure Communication in Mobile Ad Hoc Network-Based E-Healthcare System

The COVID-19 pandemic has affected people’s lives in all aspects. This pandemic has raised the usage of ubiquitous networks such as mobile ad hoc networks (MANETs) for information exchange in various domains. MANET is a group of versatile nodes that communicates with each other without relying on a fixed physical framework. One of the prominent features of MANET is its versatile topology. Because of this striking feature, MANETs are employed in various domains like defense and combat operations, disaster management, healthcare, and environmental monitoring. In this paper, we enlighten the significance of MANET in the smart healthcare system. The COVID-19 pandemic outbreak demanded the reshaping of the healthcare systems to combat the pandemic and similar cataclysms. Existing healthcare systems are proved inefficient in dealing with pandemic situations, because they are not fully automated and also vulnerable to various security attacks. Therefore, it is vital to empower the healthcare sectors by integrating ubiquitous networks and other emerging technologies. In this paper, we proposed a MANET-based secure healthcare system to exchange medical data among portable nodes. Ensuring secure communication in the MANET-based healthcare system is one of the challenging issues. Healthcare system demands the sharing of confidential medical data among mobile nodes. So it is essential to provide secure information exchange in healthcare system by using strong cryptographic schemes. In this paper, we proposed a hybrid cryptographic algorithm for secure medical information exchange among mobile healthcare nodes. The proposed cryptographic scheme uses logistic map for key generation. Logistic map exhibits high security with less computational power. Our simulation results show that the proposed hybrid cryptographic scheme exhibits better security against various attacks in MANET-based healthcare systems.

Detect Denial of Service Attack (DoS) in MANETs Partition Scenario Using Puzzle Map Method

The usage of the Internet increases significantly especially regards COVID-19. The lifestyle relies of the networks and the Internet to do all tasks such as job, meetings, and education. The mobile ad hoc network (MANET) is a type of wireless network that nodes can communicate without the occurrence of an administration point. Nodes in MANET can join and leave the network frequently, so the scenario of networks merging and partitioning can occur. In this study, the detection of Denial of Service (DoS) attack which degrades the performance of the network will be illustrated in the situation of network partitioning. The detection will be based on MrDR method and puzzle map method, the puzzle concept will help to complete the network partitioning and avoid the DoS attack. The proposed method is simulated using NS2 and the results shows the effectiveness of the puzzle map method to increase the performance of network during the partitioning scenario.

Research on NTP Nonlinear Reflection Attack Identification Based on AHP Multidimensional Matrix in Global COVID-19 Environment

Under the influence of the global epidemic, various businesses have moved online one after another. With the rise of emerging industries such as online medical treatment, online education, and online conference, the proportion of attacks in the network service industry has increased year by year. UDP-FLOOD is still the primary scenario of DDoS attacks. Among them, with a large number of attack resources and most of them are high configuration servers, NTP (Network Time Protocol) reflection has become the most common UDP reflection attack method, accounting for 59% of the overall distribution. Therefore, establishing an efficient NTP attack detection system is a very important content to prevent network malicious attacks. At present, NTP-attacking based defensed methods mainly include IP filtering, hop mapping, and response packet detection, but they all have obvious weaknesses. Among them, the IP detection scheme can only detect historical attack IP, the implementation of hop mapping scheme is complex, and the resource overhead of response packet detection scheme is too large. Therefore, this paper proposes a nonlinear detection algorithm based on AHP multidimensional matrix quad information entropy. Through simulation experiments, the change of quad information entropy of attack intensity from 10% to 100% is counted. The detection rate based on the traditional target IP and target port algorithm is only 50% and 60%, which is significantly lower than this algorithm. Experiments show that the detection rate of this algorithm is higher.