A lightweight privacy preserving authenticated key agreement protocol for SIP-based VoIP

Session Initiation Protocol (SIP) is an essential part of most Voice over Internet Protocol (VoIP) architecture. Although SIP provides attractive features, it is exposed to various security threats, and so an efficient and secure authentication scheme is sought to enhance the security of SIP. Several attempts have been made to address the tradeoff problem between security and efficiency, but designing a successful authenticated key agreement protocol for SIP is still a challenging task from the viewpoint of both performance and security, because performance and security as two critical factors affecting SIP applications always seem contradictory. In this study, we employ biometrics to design a lightweight privacy preserving authentication protocol for SIP based on symmetric encryption, achieving a delicate balance between performance and security. In addition, the proposed authentication protocol can fully protect the privacy of biometric characteristics and data identity, which has not been considered in previous work. The completeness of the proposed protocol is demonstrated by Gong, Needham, and Yahalom (GNY) logic. Performance analysis shows that our proposed protocol increases efficiency significantly in comparison with other related protocols

An Efficient Lightweight Provably Secure Authentication Protocol for Patient Monitoring Using Wireless Medical Sensor Networks

The refurbishing of conventional medical network with the wireless medical sensor network has not only amplified the efficiency of the network but concurrently posed different security threats. Previously, Servati and Safkhani had suggested an Internet of Things (IoT) based authentication scheme for the healthcare environment promulgating a secure protocol in resistance to several attacks. However, the analysis demonstrates that the protocol could not withstand user, server, and gateway node impersonation attacks. Further, the protocol fails to resist offline password guessing, ephemeral secret leakage, and gateway-by-passing attacks. To address the security weaknesses, we furnish a lightweight three-factor authentication framework employing the fuzzy extractor technique to safeguard the user’s biometric information. The Burrows-Abadi-Needham (BAN) logic, Real-or-Random (ROR) model, and Scyther simulation tool have been imposed as formal approaches for establishing the validity of the proposed work. The heuristic analysis stipulates that the proposed work is impenetrable to possible threats and offers several security peculiarities like forward secrecy and three-factor security. A thorough analysis of the preexisting works with the proposed ones corroborates the intensified security and efficiency with the reduced computational, communication, and security overheads

Authentication schemes for Smart Mobile Devices: Threat Models, Countermeasures, and Open Research Issues

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.This paper presents a comprehensive investigation of authentication schemes for smart mobile devices. We start by providing an overview of existing survey articles published in the recent years that deal with security for mobile devices. Then, we give a classification of threat models in smart mobile devices in five categories, including, identity-based attacks, eavesdropping-based attacks, combined eavesdropping and identity-based attacks, manipulation-based attacks, and service-based attacks. This is followed by a description of multiple existing threat models. We also provide a classification of countermeasures into four types of categories, including, cryptographic functions, personal identification, classification algorithms, and channel characteristics. According to the characteristics of the countermeasure along with the authentication model iteself, we categorize the authentication schemes for smart mobile devices in four categories, namely, 1) biometric-based authentication schemes, 2) channel-based authentication schemes, 3) factors-based authentication schemes, and 4) ID-based authentication schemes. In addition, we provide a taxonomy and comparison of authentication schemes for smart mobile devices in form of tables. Finally, we identify open challenges and future research directions

ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things

The Internet of Medical Things (IoMT) is a unification of smart healthcare devices, tools, and software, which connect various patients and other users to the healthcare information system through the networking technology. It further reduces unnecessary hospital visits and the burden on healthcare systems by connecting the patients to their healthcare experts (i.e., doctors) and allows secure transmission of healthcare data over an insecure channel (e.g., the Internet). Since Artificial Intelligence (AI) has a great impact on the performance and usability of an information system, it is important to include its modules in a healthcare information system, which will be very helpful for the prediction of some phenomena, such as chances of getting a heart attack and possibility of a tumor, from the collected and analysed healthcare data. To mitigate these issues, in this paper, a new AI-enabled lightweight, secure communication scheme for an IoMT environment has been designed and named as ASCP-IoMT, in short. The security analysis of ASCP-IoMT is performed in different ways, such as an informal way and a formal way (through the random oracle model). ASCP-IoMT performs better than other similar schemes and provides superior security with extra functionality features as compared those for the existing state of art solutions. A practical implementation of ASCP-IoMT is also performed in order to measure its impact on various network performance parameters. The end to end delay values of ASCP-IoMT are 0.01587, 0.07440 and 0.17097 seconds and the throughput values of ASCP-IoMT are 5.05, 10.88 and 16.41 bits per second (bps) under the different considered cases, respectively. For AI-based Big data analytics phase, the values of computation time (seconds) for decision tree, support vector machine (SVM), and logistic regression are measured as 0.19, 0.23, and 0.27, respectively. Moreover, the different values of accuracy for decision tree, SVM and logistic regression are 84.24%, 87.57%, and 85.20%, respectively. From these values, it is clear that decision tree method requires less time than the other considered techniques, whereas accuracy is high in case of SVM

A Study to Understand and Compare Evidence Based Practice Among Health Professionals Involved in Pain Management

Pain management is a common concern of multiple health professionals. Evidence-based practice (EBP) in pain management is a recognized approach used to improve health outcomes. EBP tools can facilitate its implementation. PAIN+ is a tool that provides access to pre-appraised current best research evidence on pain to support clinical decisions. It is important to understand the knowledge, attitudes and behavior of professionals towards EBP and more specifically how they access research about pain management. The overarching purpose of this thesis is to better understand how clinicians from different professions involved in pain management view EBP and implement specific strategies to find pain related research evidence. We conducted a series of studies incorporating various methods to address these questions. Data was collected supplementary to a large randomized control trial to compare “Push” vs. “Pull” strategies for uptake of pain research. In the first study, we compared the knowledge, attitudes, outcomes expectations and behaviors of physicians, nurses, physiotherapists, occupational therapists and psychologists towards EBP in pain management using a validated knowledge attitude and behavior (KABQ) questionnaire. In the second study, we used a mixed methods approach to understand the competencies of clinicians accessing electronic databases to search for evidence on pain management. In the third study, we performed a structured classification of the abstracts that were viewed by clinicians to understand their access behaviors. In the last part of the thesis, we compared the usefulness of PAIN+ with PubMed using a randomized crossover trial approach. The results of this thesis indicate that the professionals involved in pain management have good knowledge of and attitudes towards EBP, but behavior i.e. implementation of EBP in practice and perception of outcomes of implementing EBP were low. In the second study, we found that professionals had acceptable levels of basic literature searching skills but had low levels of use of more advanced skills, and were not aware of using clinical queries in their search. In the third study, we found that all professionals accessed research evidence when provided alerts about pain research and some variations in the types of studies accessed were observed. Differences in access behaviors might reflect differences in professional approach to pain management. In our fourth study the crossover randomized controlled trial; we found PAIN+ and PubMed were both rated useful in retrieving pain evidence for clinicians. Professionals showed an interest in evidence-based pain management, but their skills for finding evidence were limited, they appeared to need training in locating and appraising pain related research evidence, and may benefit from tools that reduce this burden