Search

The correctness and the true validated data in Human Resource Management (HRM) are important for organizations as the data plays an impactful role in recruiting, developing, and retaining a skilled workforce. On one hand, the validated data in an organization helps in recruiting legitimate skillful employees; on the other hand, keeping the employee's data safe and maintaining privacy laws such as compliance with the General Data Protection Regulation (GDPR) is also an organization's responsibility. Besides, transparency in human resource management operations is crucial because it promotes trust and fairness within an organization. The present HRM systems are centralized in nature and their verifiable credential system is ineffective; this leads to the intentions of internal data sabotage or internal threats. Besides, the organizations' biases also become more prominent.In this paper, we address the above-mentioned problems with a blockchain framework for HRM to utilize the privacy of data access through a Privacy Information Retrieval (PIR) process. To be specific, our proposed framework called Blockchained piR of resOurces as humaN (BRON), is the first blockchain framework to show an effective mechanism to access data from organizations globally without hampering privacy. BRON uses a generalized user registration process to use the services of data access and in the background, it uses Zero-Knowledge Proofs (ZKPs) for global verification and PIR for privacy-based data retrieval. More specifically, credential verification and ZKP-based PIR are the highlights of our proposed BRON. Another interesting aspect of BRON is the use of Proof-of-Authority (PoA) to validate the anonymity and unlinkability of any HR operation. Finally, BRON has also contributed with a smart contract to incentivize the employees. BRON is very generic and easily be customizable as per the HR requirements. We run a set of experiments on BRON and observe that it is successful in providing privacy-assured data access and decentralized human resource data management. Overall, BRON provides 30% reduced latency and 35% better throughput as compared to the existing blockchain solutions in the direction of HRM.

Vehicular networks are expanding their applications for future sustainability. The reported increasing rate of data breaches through vehicular networks by Distributed Denial of Service (DDoS) type of intrusion creates concern for such networks. Existing security solutions focus only on intrusion detection. However, prevention solutions are more proactive and provide security by probabilistic analysis. Existing prevention models for vehicular networks have low accuracy and are unable to handle zero-day attacks and advanced persistent threats. In this paper, we solve the problems mentioned above and introduce Predictive Risk Evaluation for Vehicular Infrastructure Resilience (PREVIR), the first amalgamated model of logit method (statistical analysis) and LogitBoost method (machine learning) to prevent DDoS attacks in vehicular networks. In PREVIR, the logit model predicts the packet probabilities for identifying maliciousness. The machine learning method improves PREVIR’s performance through iterative refinement of the model’s periodic updates based on new traffic parameters. We run a set of experiments on PREVIR. We use our NS3-generated dataset, NSL-KDD public dataset, and CIC-DDoS public dataset. PREVIR analyses multiple attack types, including UDP flood, TCP flood, mixed flooding, U2R, Probe, and R2L attacks. The results show that PREVIR classifies packets with accuracy up to 99.99%. Our proposed PREVIR model achieves a True Positive Ratio (TPR) up to 100% and an average False Positive Ratio (FPR) of 35%. The comparative analysis shows that PREVIR’s efficiency is 20% better on average in the prevention of malicious packets as compared to the state-of-the-art models.

Blockchain networks provide a reliable and secure mode of communication due to their decentralized and distributed nature. The emergence of amalgamated blockchain-based internet-of-things (IoT) systems has generated a huge amount of data to be online. Though blockchains show potential for ensuring transparency, traceability, and immutable records, the privacy of online data in blockchains becomes a question. The privacy of blockchain transactions is at stake as various privacy breaching methods are used by attackers such as linking the transactions, deanonymization, etc. However, the benefits of blockchain make the technology a dominator in the present and future technological paradigms. The other side of the coin deals with privacy information retrieval (PIR), which is necessary to retrieve private information from servers without much revealing. However, the conjunction of blockchain privacy and PIR is very critical and an important aspect of blockchain solutions. In this present survey, we pioneer in analyzing the privacy factors of existing blockchain solutions. We discuss the privacy parameters and important privacy enhancement techniques for blockchains comprehensively. We show the applicability of privacy in various domains including e-commerce, supply chain, healthcare, and IoT. We also discuss PIR-related issues and solutions in the existing literature. We highlight open research problems and discuss the benefits of collaborating with PIR and blockchain systems to improve privacy in blockchains. Our survey is beneficial for academia and industries to be aware of the present status of privacy solutions in blockchains and to address the identified loopholes to make the systems better.

Introduction: Self-medication is an important health issue. The rising tendency of self-medication has been a cause for concern. Rheumatic patients, being vulnerable to various symptoms like pain and fever, may resort to self-medication. Although several studies have been conducted among different populations, there is a lack of literature focusing on rheumatic disease patients in India. Aim: To investigate the knowledge, attitude, and practice of self-medication among patients attending the Rheumatology Outpatient clinic at a tertiary care centre in eastern India. Materials and Methods: This descriptive cross-sectional study was conducted at the Rheumatology Outpatient Clinic in Medical College Kolkata, a tertiary hospital in West Bengal, India, from July 21, 2021, to September 20, 2021. A total of 105 patients attending the rheumatology outpatient clinic were included in the study. Data were collected by conducting interviews with the patients using prestructured and prevalidated questionnaire after obtaining informed consent. The Knowledge, Attitude, and Practice (KAP) of self-medication were assessed, and the data were statistically analysed using Chi-square tests. Results: The mean age of the study population was 38.77 years±12.56 years. Among the 105 subjects, 61 (58.1%) were suffering from arthritis, 26 (24.8%) from Systemic Lupus Erythematosus (SLE), and 6 (5.7%) from kidney disease. A total of 75 (71.4%) study subjects reported that they knew what self-medication was. The majority of the patients (86.7%) were unaware of the common adverse effects. However, 90 (85.7%) stated that self-medication was generally not beneficial, whereas 9 (8.6%) held the opposite view. A total of 64 (61%) reported having practiced self-medication in the past year. Conclusion: The present study reported a self-medication rate of 61%. Self-medication was found to be more frequent among the younger age group, females, and individuals with higher educational qualifications.

Introduction: Some specific personality factors in patients of bipolar disorder are potential risk factors for developing comorbid substance use disorder. Comorbid substance use disorder is one of the most common factors associated with non-adherence to treatment in bipolar disorder patients leading to poor quality of life. We compared all three variables in bipolar disorder-1patients with or without substance use disorder. Methods: It is a cross-sectional study done on 90 patients of Bipolar disorder-1 diagnosed as per DSM-5, 45 without and 45 with comorbid substance use disorder. Personality factors, non-adherence to medication and quality of life were assessed and compared between both the groups using standardized tools and appropriate statistical analysis methods. Results: There was a significant difference between the groups in terms of distribution of socio-economic status (Bias Corrected Cramer'sV = 0.31 moderate association), gender (Bias Corrected Cramer'sV = 0.37 moderate association), distribution of 16 personality factor B (Cramer's V = 0.28) and factor G (χ2 = 6.222, p = 0.045) (Cramer's V = 0.26). Limitations: Small sample size and a cross-sectional study design. Conclusion: SUD and NSUD group differed on 16 PF factor B and G which are factors for reasoning and Rule-Consciousness respectively although on regression analysis this difference was not significant for factor G. In binary logistic regression multivariable model, an average score (as compared to low score) of personality factor B was found to significantly increase the ODDS of substance use. SUD group had more severity and greater number of episodes, had lower level of education, belonged to low socio-economic status.

Purpose – This research aims to determine to what degree registered and nonregistered Rohingyas differ in their difficulties and coping strategies. Design/methodology/approach – Kutupalong registered and one nonregistered camp (Camp 2E) were selected as the study area, and a mixed-methods approach was followed to collect the data. Six in-depth interviews and two focus group discussions (FGDs) were conducted first, and then the questionnaire survey was conducted on 315 Rohingyas, comprising 116 registered and 199 non-registered refugees. Findings – The results indicate a substantial difference in the difficulties and coping techniques of registered and nonregistered refugees in food, residence, health and security. Except for the health and security issue, the registered Rohingyas (RRs) have a relatively better life than the nonregistered Rohingyas (NRRs). The main problem registered refugees undergo is economic, followed by health service, food, residence, social and security issue. For nonregistered refugees, economic and social issues receive maximum attention, while security is their last concern. The coping strategies show that all strategies against difficulties significantly differ between registered and nonregistered Rohingyas. Practical implications – Based on their registration status, this research may assist humanitarian workers and policymakers in better understanding of Rohingya refugees' livelihood strategies and challenges in Bangladesh. The findings may also help practitioners and policymakers build new programs and services to assist complex and difficult refugee groups in improving their livelihoods and access to essential amenities. Originality/value – Previous research shows little attention to the variations between registered and unregistered refugees. However, almost no studies have compared the challenges and coping methods of registered and unregistered Rohingya refugees in Bangladesh and other regions. This research was meant to define and offer an in-depth analysis of the Rohingya refugees' livelihood strategies in the Kutupalong registered and nonregistered camp in Bangladesh to fill the knowledge gap.

Despite improvements in the detection and treatment of severe mental disorders, suicide remains a significant public health concern. Suicide prevention and control initiatives can benefit greatly from a thorough comprehension and foreseeability of suicide patterns. Understanding suicide patterns, especially through social media data analysis, can help in suicide prevention and control efforts. The objective of this study is to evaluate predictors of suicidal behavior in humans using machine learning. It is crucial to create a machine learning model for detection of suicide thoughts by monitoring a user's social media posts to identify warning signs of mental health issues. Through the analysis of social media posts, our research intends to develop a machine learning model for identifying suicide ideation and probable mental health problems. This study will help immensely to comprehend the environmental risk factors that influence suicidal thoughts and conduct across time. In this research the use of machine learning on social media data is an exciting new direction for understanding the environmental risk factors that impact an individual's susceptibility to suicide ideation and conduct over time. The machine learning algorithms showed high accuracy, precision, recall, and F1-score in detecting suicide patterns on social media data whereas SVM has the highest performance with an accuracy of 0.886.

The increasing number of connected devices in the era of Internet of Thing (IoT) has also increased the number intrusions. Intrusion Detection System (IDS) is a secondary intelligent system to monitor, detect, and alert about malicious activities; an Intrusion Prevention System (IPS) is an extension of a detection system that triggers relevant action when an attack is suspected in a futuristic aspect. Both IDS and IPS systems are significant and useful for developing a security model. Several studies exist to review the detection and prevention models; however, the coherence in the opportunistic or advancements in the models is missing. Besides, the existing models also have some limitations, which need to be surveyed to develop new security models. Our survey is the first one to present a study of risk factor analysis using mapping technique, and provide a proposal for hybrid framework for an efficient security model for intrusion detection and/or prevention. We explore the importance of various Artificial Intelligence (AI)-based techniques, tools, and methods used for the detection and/or prevention systems in IoTs. More specifically, we emphasize on Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection-prevention systems and provide a comparative analysis focusing on the feasibility, compatibility, challenges, and real-time issues. This present survey is beneficial for industry and academia to categorize the challenges and issues in the current security models and generate the new dimensions of developments of security frameworks with efficient ML or DL methods.

Industrial Internet of Things (IIoTs) are the extensions of the Internet of Things (IoTs) and have paved the way towards the industry revolution 4.0. IIoT accelerates the industry automation of internal and external working process including transport, manufacturing, and marketing units with a number of connected devices. Being the extension of IoT, it inherits the insecurities of the technology; however, this sensor-configured infrastructure of IIoT needs some extra effort to customize the existing security solutions. In spite of the reconstructions of security models, the scope of improved developments is open to detect unknown attacks. The present study helps the researchers to understand the cause for intrusion by classifying and comparing various attacks of each IIoT layers. The main focus of this survey is to analyze various security issues faced by IIoT and provide a comparative analysis on the available solutions to enhance the industrial IoTs' protection systems. This study also notifies some open research problems for academia, technologists, and researchers to flourish the IIoT domain and its security aspects.

There is an increasing drive to provide improved levels of trust within an Internet-of-Things (IoTs) environments, but the devices and sensors used tend to be limited in their capabilities for dealing with traditional cryptography methods. Resource constraints and security are often the two major concerns of IIoT (Industrial IoT applications and big data generation at the present time. The strict security measures are often not significantly resource-managed and therefore, negotiation normally takes place between these. Following this, various light-weight versions of generic security primitives have been developed for IIoT and other resource-constrained sustainability. In this paper, we address the authentication concerns for resource-constrained environments by designing an efficient authentication protocol. Our authentication scheme is based on LiSP (light-weight Signcryption Protocol); however, some further customization has been performed on it to make it more suitable for IIoT-like resource-constrained environments. We use Keccack as the hash function in the process and Elli for light-weight public-key cryptography. We name our authentication scheme: Extended light-weight Signcryption Protocol with Keccack (LiSP-XK). The paper outlines a comparative analysis on our new design of authentication against a range of state-of-the-art schemes. We find the suitability of LiSP-XK for IIoT like environments due to its lesser complexity and less energy consumption. Moreover, the signcryption process is also beneficial in enhancing security. Overall the paper shows that LiSP-XK is overall 35% better in efficiency as compared to the other signcryption approaches.

Blockchain is a technology used with the series of users in peer-to-peer transactions to utilize the usability properties of the immutable data records. The distributed nature of this technology has given the wide acceptance to its range of applications in various sectors. Seeing the prospect of this new technology, we have chosen the field of human resource management as these data needs to be privacy preserving and confidential along with significant research value. Distributed ledger approach is a novel idea in this field of work specifically for the application of human resource records management. We have used a privacy preserving framework that provides a transparent system for human resource record management. Wallets are generated with organization id and outputting with public-private key pair along with privacy parameter mapping with hash. Keys are used to provide confidentiality, integrity and authentication. Smart contract uses distributed but converged decision with privacy level classification. Performance of the proposed work has been measured based on time, memory consumptions, failure point identification and read-write latencies. The analysis of the results confirms the efficiency of the system.

The increasing number of devices in the age of Internet-of-Thing (IoT) has arisen a number of problems related to security. Cryptographic processes, more precisely the signatures and the keys, increase and generate an overhead on the network resources with these huge connections. Therefore, in this paper we present a signcryption framework to address the above problems. The solution highlights the use of aggregate signcryption and certificaless approach based on bilinear pairings. The use of signcryption with aggregation and certificateless authentication reduces the time consumption, overhead and complexity. The solution is also able to solve the key staling problems. Experimental results and comparative analysis based on key parameters, memory utilization and bandwidth utilization have been measured. It confirms that the presented work is efficient for IoT infrastructure.

The progress in network technology and hardware in conjunction with the Internet-of-Things (IoTs) has provided the comfortability for an easier human life at present. Apart from the so-called “smart” environments, including smart homes, smart city, smart agriculture, IoTs have been included recently in e-healthcare systems as well for real-time diagnosis and medical consultancy. To enhance the capabilities of IoT-based healthcare systems, fog layers have been employed and that have shown its worth by providing fast response time and low latency. However, such development is posing a severe challenge in preserving the privacy of the users which further addresses the security/privacy issues to some extent. Being in an infant stage, such technology has invariably run into fewer privacy controls. Therefore, our present work is about an e-healthcare framework dealing with electronic medical records (EMRs) which preserves the privacy issues. Moreover, we have experimented the proposed work with respect to response time and delay and have compared with recent works. The results show that the proposed work is efficient in providing privacy along with standard network parameters.

In this research paper, blockchain-based trust management model is proposed to enhance trust relationship among beacon nodes and to eradicate malicious nodes in Wireless Sensor Networks (WSNs). This composite trust evaluation involves behavioral-based trust as well as data-based trust. Various metrics such as closeness, honesty, intimacy and frequency of interaction are taken into account to compute behavioral-based trust of beacon nodes. Further, the composite (behavior and data) trust value of each beacon nodes is broadcast to Base Stations (BS) to generate a blockchain of trust values. Subsequently, the management model discards the beacon node with least trust value and that ensures reliability and consistency of localization in WSNs. The simulated results of the proposed algorithm are compared with the existing ones in terms of detection accuracy, False Positive Rate (FPR) and False Negative Rate (FNR) and Average Energy Consumption (AEC).

The Rivest Cipher 4 (RC4) has been one of the most popular stream ciphers for providing symmetric key encryption, and is now proposed as an efficient cipher within light-weight cryptography. As an algorithm it has been considered to be one of the fastest stream ciphers and one of the easiest to implement. Unfortunately, despite its simplicity of usage, a number of attacks on it have been found. Therefore, various improvements of this algorithm exist in cryptography, but none of them use proper randomness. This paper outlines modified version of RC4 and which has the desirable features of an efficient stream cipher algorithm, and which integrates the Symmetric Random Function Generator (SRFG) method. Though RC4 uses pseudorandom features with an initialisation vector and a seed value, the use of true randomness in RC4 is novel in this domain. Therefore, this paper proposes a modified RC4 as MRC4, and which then evaluates the statistical features of MRC4 based upon parameters such as non-linearity, resiliency, balancedness, propagation and immunity. Further, we have compared the security features and confusion-diffusion attributes with some recent variants of RC4 and have found that MRC4 is efficient in withstanding against attacks. The experimental results show that MRC4 supports a 60% better confusion property and 50% better diffusion as compared to the original RC4 method.

Vehicular ad hoc networks (VANETs) have been considered as one of the most influential applications of mobile ad hoc networks. The VANETs have also become an important part of Internet of Things. Along with the advantages, such networks are facing a number of security issues. A number of research have been done so far to identify the solution of the security problems in the VANETs. But the existing solutions do not confirm the confidentiality-integrity-availability (CIA) triad services simultaneously. Therefore, in this paper, we have provided a solution for the VANETs security using end-to-end authentication to avoid intrusion in the VANETs. After verifying the communicating entities, the data are transmitted in encrypted form thus the proposed approach provides CIA security services to VANETs communication. Along with this, we have used a sandboxing method for in-vehicle communication for prevention of intrusion in form of downloaded information. Moreover, in this paper, the VANETs are considered as a hierarchical model to concentrate on a less number of message exchanges. Furthermore, the simulation results show the efficiency of our proposed solution.

Cryptographic algorithms and functions should possess some of the important functional requirements such as: non-linearity, resiliency, propagation and immunity. Several previous studies were executed to analyze these characteristics of the cryptographic functions specifically for Boolean and symmetric functions. Randomness is a requirement in present cryptographic algorithms and therefore, Symmetric Random Function Generator (SRFG) has been developed. In this paper, we have analysed SRFG based on propagation feature and immunity. Moreover, NIST recommended statistical suite has been tested on SRFG outputs. The test values show that SRFG possess some of the useful randomness properties for cryptographic applications such as individual frequency in a sequence and block-based frequency, long run of sequences, oscillations from 0 to 1 or vice-versa, patterns of bits, gap bits between two patterns, and overlapping block bits. We also analyze the comparison of SRFG and some existing random number generators. We observe that SRFG is efficient for cryptographic operations in terms of propagation and immunity features.

Vehicular networks are the combination of transport systems and the internet systems formed with the main motive to increase the safety of passengers, although non-safety applications are also provided by vehicular networks. Internet of Things (IoT) has a subsection called Mobile Ad hoc Network (MANET)m which in turn has a subsection called Vehicular Ad hoc Network (VANET). Internet of Energy (IoE) is a new domain that is formed using electric vehicles connected with VANETs. As a large number of transport systems are coming into operation and various pervasive applications are designed to handle such networks, the increasing number of attacks in this domain is also creating threats. As IoE is connected to VANETs extension with electric cars, the future of VANETs can be a question if security measures are not significant. The present survey is an attempt to cover various attack types on vehicular networks with existing security solutions available to handle these attacks. This study will help researchers in getting in-depth information about the taxonomy of vehicular network security issues which can be explored further to design innovative solutions. This knowledge will also be helpful for new research directions, which in turn will help in the formulation of new strategies to handle attacks in a much better way.

Localization is one of the key concepts in wireless sensor networks. Different techniques and measures to calculate the location of unknown nodes were introduced in recent past. But the issue of nodes’ mobility requires more attention. The algorithms introduced earlier to support mobility lack the utilization of the anchor nodes’ privileges. Therefore, in this article, an improved DV-Hop localization algorithm is introduced that supports the mobility of anchor nodes as well as unknown nodes. Coordination of anchor nodes creates a minimum connected dominating set that works as a backbone in the proposed algorithm. The focus of the research paper is to locate unknown nodes with the help of anchor nodes by utilizing the network resources efficiently. The simulated results in network simulator-2 and the statistical analysis of the data provide a clear impression that our novel algorithm improves the error rate and the time consumption.

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms.

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations.

Burn injuries and their subsequent treatment cause one of the most excruciating forms of pain imaginable. The psychological aspects of burn injury have been researched in different parts of the world, producing different outcomes. Studies have shown that greater levels of acute pain are associated with negative long-term psychological effects such as acute stress disorder, depression, suicidal ideation, and post-traumatic stress disorder for as long as 2 years after the initial burn injury. The concept of allostatic load is presented as a potential explanation for the relationship between acute pain and subsequent psychological outcomes. A biopsychosocial model is also presented as a means of obtaining better inpatient pain management and helping to mediate this relationship.