Biblio

Over the last few decades, and thanks to the advancement of embedded systems and wireless technologies, the wireless sensors network (WSN) are increasingly used in many fields. Many researches are being done on the use of WSN in Wireless body Area Network (WBAN) systems to facilitate and improve the quality of care and remote patient monitoring.The broadcast nature of wireless communications makes it difficult to hide transmitted signals from unauthorized users. To this end, Physical layer security is emerging as a promising paradigm to protect wireless communications against eavesdropping attacks. The primary contribution of this paper is achieving a minimum secrecy outage probability by using the jamming technique which can be used by the legitimate communication partner to increase the noise level of the eavesdropper and ensure higher secure communication rate. We also evaluate the effect of additional jammers on the security of the WBAN system.

This paper introduces a technique that enhances the capabilities of an intrusion detection system (IDS) in a wireless body area network (WBAN). This technique involves adopting two known machine-learning algorithms: artificial neural network (ANN) and the J48 form of decision trees. The enhanced technique reduces the security threats to a WBAN, such as denial-of-service (DoS) attacks. It is essential to manage noise, which might affect the data gathered by the sensors. In this paper, noise in data is measured because it can affect the accuracy of the machine learning algorithms and demonstrate the level of noise at which the machine-learning model can be trusted. The results show that J48 is the best model when there is no noise, with an accuracy reaching 99.66%, as compared to the ANN algorithm. However, with noisy datasets, ANN shows more tolerance to noise.

Due to the mobility and openness of wireless body area networks (WBANs), the security of WBAN has been questioned by people. The patient's physiological information in WBAN is sensitive and confidential, which requires full consideration of user anonymity, untraceability, and data privacy protection in key agreement. Aiming at the shortcomings of Li et al.'s protocol in terms of anonymity and session unlinkability, forward/backward confidentiality, etc., a new anonymous mutual authentication and key agreement protocol was proposed on the basis of the protocol. This scheme only uses XOR and the one-way hash operations, which not only reduces communication consumption but also ensures security, and realizes a truly lightweight anonymous mutual authentication and key agreement protocol.

With the help of technological advancements in the last decade, it has become much easier to extensively and remotely observe medical conditions of the patients through wearable biosensors that act as connected nodes on Body Area Networks (BANs). Sensitive nature of the critical data captured and communicated via wireless medium makes it extremely important to process it as securely as possible. In this regard, lightweight security mechanisms are needed to overcome the hardware resource restrictions of biosensors. Random and secure cryptographic key generation and agreement among the biosensors take place at the core of these security mechanisms. In this paper, we propose the SKA-PSAR (Augmented Randomness for Secure Key Agreement using Physiological Signals) system to produce highly random cryptographic keys for the biosensors to secure communication in BANs. Similar to its predecessor SKA-PS protocol by Karaoglan Altop et al., SKA-PSAR also employs physiological signals, such as heart rate and blood pressure, as inputs for the keys and utilizes the set reconciliation mechanism as basic building block. Novel quantization and binarization methods of the proposed SKA-PSAR system distinguish it from SKA-PS by increasing the randomness of the generated keys. Additionally, SKA-PSAR generated cryptographic keys have distinctive and time variant characteristics as well as long enough bit sizes that provides resistance against cryptographic attacks. Moreover, correct key generation rate is above 98% with respect to most of the system parameters, and false key generation rate of 0% have been obtained for all system parameters.

With the rapid development of micro-electronics and Information and Communication Technology (ICT), users can utilize various service such as Internet of Things(IoT), smart-healthcare and smart-home using wearable devices. However, the sensitive information of user are revealed by attackers because the medical services are provided through open channel. Therefore, secure mutual authentication and key establishment are essential to provide secure services for legitimate users in Wireless Body Area Networks(WBAN). In 2019, Gupta et al. proposed a lightweight anonymous user authentication and key establishment scheme for wearable devices. We demonstrate that their scheme cannot withstand user impersonation, session key disclosure and wearable device stolen attacks. We also propose a secure and lightweight mutual authentication and key establishment scheme using wearable devices to resolve the security shortcomings of Gupta et al.'s scheme. The proposed scheme can be suitable to resource-limited environments.

In this paper, we address the fundamental problem of securely bootstrapping a group of wireless devices to a hub, when none of the devices share prior associations (secrets) with the hub or between them. This scenario aligns with the secure deployment of body area networks, IoT, medical devices, industrial automation sensors, autonomous vehicles, and others. We develop VERSE, a physical-layer group message integrity verification primitive that effectively detects advanced wireless signal manipulations that can be used to launch man-in-the-middle (MitM) attacks over wireless. Without using shared secrets to establish authenticated channels, such attacks are notoriously difficult to thwart and can undermine the authentication and key establishment processes. VERSE exploits the existence of multiple devices to verify the integrity of the messages exchanged within the group. We then use VERSE to build a bootstrapping protocol, which securely introduces new devices to the network. Compared to the state-of-the-art, VERSE achieves in-band message integrity verification during secure pairing using only the RF modality without relying on out-of-band channels or extensive human involvement. It guarantees security even when the adversary is capable of fully controlling the wireless channel by annihilating and injecting wireless signals. We study the limits of such advanced wireless attacks and prove that the introduction of multiple legitimate devices can be leveraged to increase the security of the pairing process. We validate our claims via theoretical analysis and extensive experimentations on the USRP platform. We further discuss various implementation aspects such as the effect of time synchronization between devices and the effects of multipath and interference. Note that the elimination of shared secrets, default passwords, and public key infrastructures effectively addresses the related key management challenges when these are considered at scale.

With the pervasiveness of the Internet of Things (IoT) and the rapid progress of wireless communications, Wireless Body Area Networks (WBANs) have attracted significant interest from the research community in recent years. As a promising networking paradigm, it is adopted to improve the healthcare services and create a highly reliable ubiquitous healthcare system. However, the flourish of WBANs still faces many challenges related to security and privacy preserving. In such pervasive environment where the context conditions dynamically and frequently change, context-aware solutions are needed to satisfy the users' changing needs. Therefore, it is essential to design an adaptive access control scheme that can simultaneously authorize and authenticate users while considering the dynamic context changes. In this paper, we propose a context-aware access control and anonymous authentication approach based on a secure and efficient Hybrid Certificateless Signcryption (H-CLSC) scheme. The proposed scheme combines the merits of Ciphertext-Policy Attribute-Based Signcryption (CP-ABSC) and Identity-Based Broadcast Signcryption (IBBSC) in order to satisfy the security requirements and provide an adaptive contextual privacy. From a security perspective, it achieves confidentiality, integrity, anonymity, context-aware privacy, public verifiability, and ciphertext authenticity. Moreover, the key escrow and public key certificate problems are solved through this mechanism. Performance analysis demonstrates the efficiency and the effectiveness of the proposed scheme compared to benchmark schemes in terms of functional security, storage, communication and computational cost.

WBANs integrate wearable and implanted devices with wireless communication and information processing systems to monitor the well-being of an individual. Various MAC (Medium Access Control) protocols with different objectives have been proposed for WBANs. The fact that any flaw in these critical systems may lead to the loss of one's life implies that testing and verifying MAC's protocols for such systems are on the higher level of importance. In this paper, we firstly propose a high-level formal and scalable model with timing aspects for a MAC protocol particularly designed for WBANs, named S-TDMA (Statistical frame based TDMA protocol). The protocol uses TDMA (Time Division Multiple Access) bus arbitration, which requires temporal aspect modeling. Secondly, we propose a formal validation of several relevant properties such as deadlock freedom, fairness and mutual exclusion of this protocol at a high level of abstraction. The protocol was modeled using a composition of timed automata components, and verification was performed using a real-time model checker.

The widespread diffusion of the Internet of Things (IoT) is introducing a huge number of Internet-connected devices in our daily life. Mainly, wearable devices are going to have a large impact on our lifestyle, especially in a healthcare scenario. In this framework, it is fundamental to secure exchanged information between these devices. Among other factors, it is important to take into account the link between a wearable device and a smart unit (e.g., smartphone). This connection is generally obtained via specific wireless protocols such as Bluetooth Low Energy (BLE): the main topic of this work is to analyse the security of this communication link. In this paper we expose, via an experimental campaign, a methodology to perform a vulnerability assessment (VA) on wearable devices communicating with a smartphone. In this way, we identify several security issues in a set of commercial wearable devices.

Technological advances in wearable and implanted medical devices are enabling wireless body area networks to alter the current landscape of medical and healthcare applications. These systems have the potential to significantly improve real time patient monitoring, provide accurate diagnosis and deliver faster treatment. In spite of their growth, securing the sensitive medical and patient data relayed in these networks to protect patients' privacy and safety still remains an open challenge. The resource constraints of wireless medical sensors limit the adoption of traditional security measures in this domain. In this work, we propose a distributed mobile agent based intrusion detection system to secure these networks. Specifically, our autonomous mobile agents use machine learning algorithms to perform local and network level anomaly detection to detect various security attacks targeted on healthcare systems. Simulation results show that our system performs efficiently with high detection accuracy and low energy consumption.

Recent advances in Wireless Sensor Networks have given rise to many application areas in healthcare such as the new field of Wireless Body Area Networks. The health status of humans can be tracked and monitored using wearable and non-wearable sensor devices. Security in WBAN is very important to guarantee and protect the patient's personal sensitive data and establishing secure communications between BAN sensors and external users is key to addressing prevalent security and privacy concerns. In this paper, we propose secure and efficient key management scheme based on ECC algorithm to protect patient's medical information in healthcare system. Our scheme divided into three phases as setup, registration, verification and key exchange. And we use the identification code which is the SIM card number on a patient's smart phone with the private key generated by the legal use instead of the third party. Also to prevent the replay attack, we use counter number at every process of authenticated message exchange to resist.