SBODS: A Secure Sharded Blockchain-based Online Diagnostic System During Pandemics

This chapter focuses on the very serious problem of denial of service (DoS) attacks against computer systems and networks. DoS attacks are attacks designed to disrupt, overwhelm, and/or damage computing resources and data. These attacks are aimed not against confidentiality of data or integrity of systems and data, but rather against availability of systems and data. Attackers launch DoS attacks for a wide variety of reasons. The probability of success is high, due in large part to the fact that the Internet itself has not been designed for security. The cost of DoS attacks is often high—several well‐known attacks have reportedly cost organizations billions of dollars. Many types of DoS attacks have surfaced so far, including resource starvation attacks, buffer overflow attacks, and packet fragmentation attacks. Distributed denial of service (DDoS) attacks, attacks in which programs planted in multiple systems cooperate, represent an even higher level of threat. Preventing DoS attacks is difficult in the first place due to a plethora of reasons; limiting the potential for damage and disruption is often the most cost‐effective strategy. Hot sites, cold sites, use of uninterruptible power supplies (UPSs) and Redundant Array of Independent Drives (RAID) solutions, and other measures can limit the damage from DoS attacks. Using properly configured and maintained firewalls is one of the best single prevention measures. Additionally, using packet filters on systems, keeping up with patches, limiting services that run on these systems, and using other measures go a long way in helping prevent susceptibility to DoS attacks.

Strategies to minimize preventable morbidity and mortality resulting from pandemics like COVID-19.

Intrusion Detection Systems (IDS) play a vital role in monitoring and preventing unauthorized access within critical network infrastructures. However, current IDS approaches often suffer from limitations in detection accuracy, scalability, and adaptability to evolving threats. To address these challenges, this study proposes an optimized Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model for network-based intrusion detection. The model is specifically designed to capture temporal dependencies in sequential network traffic data, enabling accurate classification of benign and malicious activity while maintaining computational efficiency. The proposed model underwent rigorous validation to assess both its detection accuracy and generalizability. This was achieved by utilizing two distinct datasets that encompass realistic network traffic and a wide array of attack scenarios, including Denial of Service (DoS), Probe, User-to-Root (U2R), and Remote-to-Local (R2L) intrusions. The performance of the model is compared with several other recent models. The proposed model achieved high classification results among all the compared models, with an accuracy of 99.94%, precision of 99.98%, recall of 99.93%, and an F1-score of 99.95%. These outstanding results underscore the model’s robust capability to accurately identify and flag malicious activities within complex network environments. The model’s generalization power was similarly affirmed on a second independent dataset. The model maintained strong performance with an accuracy of 99.42%, precision of 99.20%, recall of 99.69%, and an F1-score of 99.45%. The successful demonstration of both high detection accuracy and strong generalization capability highlights the practical utility and inherent reliability of our proposed model for real-world threat detection.

Client-server-based healthcare systems are unable to manipulate a high data volume, prone to a single failure point, limited scalability, and data integrity. Particularly, several measures introduced to help curb the spread of Covid-19 were not effective and patient records were not adequately managed and maintained. Most vaccination-proof certificates were forged by unauthorized parties and no standard verification medium exists. Therefore, this paper proposes a blockchain-enabled vaccination management system (VMS). VMS utilizes smart contracts to store encrypted patients record, generate vaccination certificates, and verify the legitimacy of the certificate using a QR code. VMS prototype is implemented using Ethereum, a public blockchain and simulations performed based on Apache JMeter and Hyperledger Caliper to assess its performance in terms of throughput, latency and response time, and the average time per transaction. Results show VMS achieved an average: response time of 132.24 ms, the throughput of 379.89 tps, latency of 204.60 ms, and time of transactions is 10s-12s for 1000 transactions. Also, its comparison with the centralized database shows the traditional database’s effectiveness in transaction processing but lacks data privacy and security strengths. We, therefore, recommend the use of blockchain in the healthcare system and other related sectors such as elections, and student records management to ensure data privacy and security and rid the system of a single point of failure.

Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

Recent trends have led hardware manufacturers to place multiple processing cores on a single chip, making parallel programming the intended way of taking advantage of the increased processing power. However, bringing concurrency to average programmers is considered to be one of the major challenges in computer science today. The difficulty lies not only in writing correct parallel programs, but also in achieving the required efficiency and performance. For parallel programs, performance is not only about obtaining low execution times on a fixed number of cores, but also about maintaining efficiency as the number of available cores is increased. Ideally, programmers should have in their toolkit techniques that can be used when designing parallel programs, before any code is available for testing on production hardware, and which are then able to predict scalability once the program is implemented. Existing methods are either unreliable at predicting scalability, such as the case of disjoint-access parallelism, or do not apply to lock-based programs, which currently make up a large part of existing concurrent programs. Furthermore, using some of these techniques is so complicated that it outweighs the time required to implement, debug and test the program on real hardware. In this thesis we study the problem of predicting the scalability of concurrent programs without implementing them. This allows programmers in the design phase of a concurrent algorithm to choose only one or a few promising solutions that will be implemented, debugged and tested on production hardware. We first consider disjoint-access parallelism, an existing property that applies only to a very restricted class of programs. After an extensive practical evaluation spanning across a variety of scenarios, we find it to be ineffective at predicting scalability. For predicting the scalability of more general concurrent algorithms, we propose the obstruction degree, a new scalability metric based on the consistency requirements of algorithms. It applies to programs using locks, invalidation primitives and transactional memory. Our metric allows programmers to compare two given algorithms as well as predict their scalability limit, the maximum number of processors to which they can scale, thus allowing programmers to choose the appropriate size hardware for running their programs. We also examine the composition of relaxed memory transactions in order to combine the ease of programming offered by transactional memory with the increased scalability of transactions that circumvent the traditional transactional model. We present outheritance, a property we show to be both necessary and sufficient for ensuring the correct composition of relaxed transactions, and we show how to calculate the obstruction degree of compositions that use this new property. We use outheritance to build OE-STM, a new software transactional memory algorithm having elastic transactions that correctly compose.

The miniaturization of sensors, the increasingly low cost, the wide range of types of sensors available as well as the wireless communication medium used, allow sensor networks to develop in several fields of application. Sensor networks can be very useful in many applications when it comes to collecting and processing information from the environment. In addition, today these micro devices are among the main components of the Internet of Things (IoT) paradigm. Several constraints block the proper functioning of the wireless sensor network. These limitations motivate a large part of research problems in the field of wireless sensor networks (WSNs), in particular the energy and security constraints that are a fundamental problem. In sensor networks, it is essential that each sensor node and base station can verify that the data received was sent by a trusted sender and not by an adversary who tricked legitimate nodes into accepting fake data. False data can change the way a network might be predicted. Data integrity should be maintained. The data must not change and the precise data must reach the end of the user. In this paper, we tackle security problems in WSN. And we focus on protection against so-called “denial of service” attacks. So the aim of the work presented is therefore to propose a set of effective detection and reaction methods to denial of service attacks, while saving or distributing the energy consumption of the sensors as well as possible to extend the network's operating time as much as possible.

The social Internet of Medical Things (S-IoMT) highly demands dependable and non-invasive device identification and authentication and makes data services more prevalent in a reliable learning system. In real time, healthcare systems consistently acquire, analyze, and transform a few operational intelligence into actionable forms through digitization to capture the sensitive information of the patient. Since the S-IoMT tries to distribute health-related services using IoT devices and wireless technologies, protecting the privacy of data and security of the device is so crucial in any eHealth system. To fulfill the design objectives of eHealth, smart sensing technologies use built-in features of social networking services. Despite being more convenient in its potential use, a significant concern is a security preventing potential threats and infringement. Thus, this paper presents a lightweight two-factor authentication framework (L2FAK) with privacy-preserving functionality, which uses a mobile sink for smart eHealth. Formal and informal analyses prove that the proposed L2FAK can resist cyberattacks such as session stealing, message modification, and denial of service, guaranteeing device protection and data integrity. The learning analysis verifies the features of the physical layer using federated learning layered authentication (FLLA) to learn the data characteristics by exploring the learning framework of neural networks. In the evaluation, the core scenario is implemented on the TensorFlow Federated framework to examine FLLA and other relevant mechanisms on two correlated datasets, namely, MNIST and FashionMNIST. The analytical results show that the proposed FLLA can analyze the protection of privacy features effectively in order to guarantee an accuracy ≈89.83% to 93.41% better than other mechanisms. Lastly, a real-time testbed demonstrates the significance of the proposed L2FAK in achieving better quality metrics, such as transmission efficiency and overhead ratio than other state-of-the-art approaches.

A Mobile agents are combination of software programs which works automatically in homogeneous and non-homogeneous environment from one host to another for sharing information among users. Mobile agents migrate in unsecure network, so mobile agent’s security is a major concern during the communication and sharing of data & information. Mobile agent’s migration has major security issues i.e. data integrity, data confidentiality & authentication, on-repudiation, denial of service and access control. In this paper neural network based synchronization key exchange is proposed for Encryption and Decryption.

BackgroundEarly diagnosis, as well as prompt and effective treatment of uncomplicated malaria, are essential components of the anti-malaria strategy in Madagascar to prevent severe malaria, reduce mortality and limit malaria transmission. The purpose of this study was to assess the performance of the malaria rapid diagnostic tests (RDTs) used by community health workers (CHWs) by comparing RDT results with two reference methods (microscopy and Polymerase Chain Reaction, PCR).MethodsEight CHWs in two districts, each with a different level of endemic malaria transmission, were trained to use RDTs in the management of febrile children under five years of age. RDTs were performed by CHWs in all febrile children who consulted for fever. In parallel, retrospective parasitological diagnoses were made by microscopy and PCR. The results of these different diagnostic methods were analysed to evaluate the diagnostic performance of the RDTs administered by the CHWs. The stability of the RDTs stored by CHWs was also evaluated.ResultsAmong 190 febrile children with suspected malaria who visited CHWs between February 2009 and February 2010, 89.5% were found to be positive for malaria parasites by PCR, 51.6% were positive by microscopy and 55.8% were positive by RDT. The performance accuracy of the RDTs used by CHWs in terms of sensitivity, specificity, positive and negative predictive values was greater than 85%. Concordance between microscopy and RDT, estimated by the Kappa value was 0.83 (95% CI: 0.75-0.91). RDTs stored by CHWs for 24 months were capable of detecting Plasmodium falciparum in blood at a level of 200 parasites/μl.ConclusionIntroduction of easy-to-use diagnostic tools, such as RDTs, at the community level appears to be an effective strategy for improving febrile patient management and for reducing excessive use of anti-malarial drugs.

Maritime Ad-Hoc Networks (MANETs) have surpassed key milestones in real-time navigation data exchange during modern maritime operations, including ship-to-ship communication, autonomous navigation and smart port integration. These applications are, however, posed with a variety of security risks due to the network's centralized and dynamic attributes, which may compromise data confidentiality, integrity, accessibility, and navigational safety. This paper studies the comprehensive literature on security weaknesses and gaps in maritime MANETs, analyzing the unique security threats imposed by the maritime environment such as disconnection, lack of infrastructure, and large coverage areas. Extreme gaps in security protocols crafted by existing literature was uncovered as a result of scarce void patching on established security measures. Spoofing, denial of service, and man-in-the-middle exploits proved most prevalent using case study analysis, risk assessment techniques, and simulation-based evaluation. Key mitigative recommendations which have been proofed under maritime contexts are emergency, authentication, and access control focused tailored encryption. There is therefore an urgent need to secure maritime systems with real time communication and navigation features alongside tested blueprints which will lead the charge in developing robust architectures for maritime networks.

Denial of service (DoS) attacks present one of the most significant threats to assurance of dependable and secure information systems. Rapid development of new and increasingly sophisticated attacks requires resourcefulness in designing and implementing reliable defences. This paper presents an overview of current DoS attack and defence concepts, from a theoretical and practical point of view. Considering the elaborated DoS mechanisms, main directions are proposed for future research required in defending against the evolving threat..

The paper aims to provide a summary with a reference to the Internet of Things (IoT) of the distributed denial of service (DDoS). The network research community has known about the denial of service (DoS), which is intended to stop legitimate users from accessing a particular network tool since the early 1980s. The aim of the paper is to define DDoS scope, classifications and opportunities for assailants who use IoT to conduct such operations and to use IoT in healthcare. The research approach is to analyze DDoS, IoT and the use of IoT literature in the health sector. The study found that DDOS attacks on IoT-specific devices have become a very easy phenomenon because network attacks are much simpler due to limited security protocols. IOT distribution companies have made limited efforts to enable security of these devices and protecting the data, thus making them susceptible to vulnerabilities. Due to weak security layers, the confidentiality, authenticity and integrity of private data collected by IoT devices are threatened by attackers. The current research identified the gap and importance for further research to provide a mechanism for detection and prevention of DDOS attack in the application layer of IoT healthcare devices in India. Considering the major challenges in IOTs in defending against external attacks and hacking, it is important that they have inherent frameworks warding off such attacks.

As we increasingly rely on information infrastructures to support critical operations in different areas, data integrity in overlay networks has significant importance with potentially rigorous consequences. Different attacks on data integrity aims to degrade availability of the secure and unmodified data to the network system. Gossip, or epidemic, protocols have emerged significantly to implement highly scalable and resilient reliable network system. Data integrity techniques in gossip protocol can have a significant role in order to secure data from an adversary. This paper describes an approach to represent and detect threats regarding data integrity in gossip based overlay networks. Establishing secure links between pairs of directly connected gossip nodes is an important issue for building overlay networks. This paper systematically identifies important security requirements of symmetric key setup. A preconfigured master key scheme is proposed that can satisfy the data integrity requirements if the master key has not been compromised. In this approach preconfigured master key is used in order to generate symmetric keys which are used to incorporate integrity checks.

Detection of histidine-rich protein 2 (HRP2) from the malaria parasite Plasmodium falciparum provides evidence for active or recent infection, and is utilized for both diagnostic and surveillance purposes, but current laboratory immunoassays for HRP2 are hindered by low sensitivities and high costs. Here we present a new HRP2 immunoassay based on antigen capture through a bead-based system capable of detecting HRP2 at sub-picogram levels. The assay is highly specific and cost-effective, allowing fast processing and screening of large numbers of samples. We utilized the assay to assess results of HRP2-based rapid diagnostic tests (RDTs) in different P. falciparum transmission settings, generating estimates for true performance in the field. Through this method of external validation, HRP2 RDTs were found to perform well in the high-endemic areas of Mozambique and Angola with 86.4% and 73.9% of persons with HRP2 in their blood testing positive by RDTs, respectively, and false-positive rates of 4.3% and 0.5%. However, in the low-endemic setting of Haiti, only 14.5% of persons found to be HRP2 positive by the bead assay were RDT positive. Additionally, 62.5% of Haitians showing a positive RDT test had no detectable HRP2 by the bead assay, likely indicating that these were false positive tests. In addition to RDT validation, HRP2 biomass was assessed for the populations in these different settings, and may provide an additional metric by which to estimate P. falciparum transmission intensity and measure the impact of interventions.