A SYSTEMATIC LITERATURE SURVEY: INTERNET OF THINGS

IoT has given a novel form of communication called Machine-to-Machine communication which has quite promising future in India. According to WATconsult report, IoT will impact all the businesses in India by 2020. The Growth of IoT is due to the adoption of the internet, smart phones and social networks by humans. Agriculture and Healthcare are the major sectors where IoT can play a vital role for change and better quality of service. IoT suffers from many challenges in India like lack of consumer awareness, poor high speed data and high manufacturing cost. Internet of things has the ability to transform real world objects into smart objects. The main aim of this systematic study is to provide an overview of Internet of Things, its applications, security, architecture, and vital technologies for future research in the area of Internet of Thing

Towards Authentication of IoMT Devices via RF Signal Classification

The increasing reliance on the Internet of Medical Things (IoMT) raises great concern in terms of cybersecurity, either at the device’s physical level or at the communication and transmission level. This is particularly important as these systems process very sensitive and private data, including personal health data from multiple patients such as real-time body measurements. Due to these concerns, cybersecurity mechanisms and strategies must be in place to protect these medical systems, defending them from compromising cyberattacks. Authentication is an essential cybersecurity technique for trustworthy IoMT communications. However, current authentication methods rely on upper-layer identity verification or key-based cryptography which can be inadequate to the heterogeneous Internet of Things (IoT) environments. This thesis proposes the development of a Machine Learning (ML) method that serves as a foundation for Radio Frequency Fingerprinting (RFF) in the authentication of IoMT devices in medical applications to improve the flexibility of such mechanisms. This technique allows the authentication of medical devices by their physical layer characteristics, i.e. of their emitted signal. The development of ML models serves as the foundation for RFF, allowing it to evaluate and categorise the released signal and enable RFF authentication. Multiple feature take part of the proposed decision making process of classifying the device, which then is implemented in a medical gateway, resulting in a novel IoMT technology.A confiança crescente na IoMT suscita grande preocupação em termos de cibersegurança, quer ao nível físico do dispositivo quer ao nível da comunicação e ao nível de transmissão. Isto é particularmente importante, uma vez que estes sistemas processam dados muito sensíveis e dados, incluindo dados pessoais de saúde de diversos pacientes, tais como dados em tempo real de medidas do corpo. Devido a estas preocupações, os mecanismos e estratégias de ciber-segurança devem estar em vigor para proteger estes sistemas médicos, defendendo-os de ciberataques comprometedores. A autenticação é uma técnica essencial de ciber-segurança para garantir as comunicações em sistemas IoMT de confiança. No entanto, os métodos de autenticação atuais focam-se na verificação de identidade na camada superior ou criptografia baseada em chaves que podem ser inadequadas para a ambientes IoMT heterogéneos. Esta tese propõe o desenvolvimento de um método de ML que serve como base para o RFF na autenticação de dispositivos IoMT para melhorar a flexibilidade de tais mecanismos. Isto permite a autenticação dos dispositivos médicos pelas suas características de camada física, ou seja, a partir do seu sinal emitido. O desenvolvimento de modelos de ML serve de base para o RFF, permitindo-lhe avaliar e categorizar o sinal libertado e permitir a autenticação do RFF. Múltiplas features fazem parte do processo de tomada de decisão proposto para classificar o dispositivo, que é implementada num gateway médico, resultando numa nova tecnologia IoMT

Biometrics for internet‐of‐things security: A review

The large number of Internet‐of‐Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric‐based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric‐cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state‐of‐the‐art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward‐looking issues and future research directions

Efficient Gaussian Process Classification-based Physical-Layer Authentication with Configurable Fingerprints for 6G-Enabled IoT

Physical-Layer Authentication (PLA) has been recently believed as an endogenous-secure and energy-efficient technique to recognize IoT terminals. However, the major challenge of applying the state-of-the-art PLA schemes directly to 6G-enabled IoT is the inaccurate channel fingerprint estimation in low Signal-Noise Ratio (SNR) environments, which will greatly influence the reliability and robustness of PLA. To tackle this issue, we propose a configurable-fingerprint-based PLA architecture through Intelligent Reflecting Surface (IRS) that helps create an alternative wireless transmission path to provide more accurate fingerprints. According to Baye's theorem, we propose a Gaussian Process Classification (GPC)-based PLA scheme, which utilizes the Expectation Propagation (EP) method to obtain the identities of unknown fingerprints. Considering that obtaining sufficient labeled fingerprint samples to train the GPC-based authentication model is challenging for future 6G systems, we further extend the GPC-based PLA to the Efficient-GPC (EGPC)-based PLA through active learning, which requires fewer labeled fingerprints and is more feasible. We also propose three fingerprint selecting algorithms to choose fingerprints, whose identities are queried to the upper-layers authentication mechanisms. For this reason, the proposed EGPC-based scheme is also a lightweight cross-layer authentication method to offer a superior security level. The simulations conducted on synthetic datasets demonstrate that the IRS-assisted scheme reduces the authentication error rate by 98.69% compared to the non-IRS-based scheme. Additionally, the proposed fingerprint selection algorithms reduce the authentication error rate by 65.96% to 86.93% and 45.45% to 70.00% under perfect and imperfect channel estimation conditions, respectively, when compared with baseline algorithms.Comment: 12 pages, 9 figure

The Internet of Things Security and Privacy: Current Schemes, Challenges and Future Prospects

The Internet of Things devices and users exchange massive amount of data. Some of these exchanged messages are highly sensitive as they involve organizational, military or patient personally identifiable information. Therefore, many schemes and protocols have been put forward to protect the transmitted messages. The techniques deployed in these schemes may include blockchain, public key infrastructure, elliptic curve cryptography, physically unclonable function and radio frequency identification. In this paper, a review is provided of these schemes including their strengths and weaknesses. Based on the obtained results, it is clear that majority of these protocols have numerous security, performance and privacy issues

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A Survey Study of the Current Challenges and Opportunities of Deploying the ECG Biometric Authentication Method in IoT and 5G Environments

The environment prototype of the Internet of Things (IoT) has opened the horizon for researchers to utilize such environments in deploying useful new techniques and methods in different fields and areas. The deployment process takes place when numerous IoT devices are utilized in the implementation phase for new techniques and methods. With the wide use of IoT devices in our daily lives in many fields, personal identification is becoming increasingly important for our society. This survey aims to demonstrate various aspects related to the implementation of biometric authentication in healthcare monitoring systems based on acquiring vital ECG signals via designated wearable devices that are compatible with 5G technology. The nature of ECG signals and current ongoing research related to ECG authentication are investigated in this survey along with the factors that may affect the signal acquisition process. In addition, the survey addresses the psycho-physiological factors that pose a challenge to the usage of ECG signals as a biometric trait in biometric authentication systems along with other challenges that must be addressed and resolved in any future related research.

Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting

Radio frequency fingerprinting (RFF) is a promising device authentication technique for securing the Internet of things. It exploits the intrinsic and unique hardware impairments of the transmitters for RF device identification. In real-world communication systems, hardware impairments across transmitters are subtle, which are difficult to model explicitly. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RFF. Most existing DL-based RFF models use a single representation of radio signals as the input. Multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of the RF fingerprint. In this work, we propose a novel multi-channel attentive feature fusion (McAFF) method for RFF. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including IQ samples, carrier frequency offset, fast Fourier transform coefficients and short-time Fourier transform coefficients, for better RF fingerprint identification. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features from multiple channels are learned during training the McAFF model. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a WiFi dataset, named WFDI, using commercial WiFi end-devices as the transmitters and a Universal Software Radio Peripheral (USRP) as the receiver. ..