Abstract

Secure access of the real-time data from the IoT smart devices (e.g., vehicles) by a legitimate external party (user) is an important security service for Big Data collection in Internet of Things (IoT)-based Intelligent Transportation System (ITS). To deal with this important issue, we design a new three-factor user authentication scheme, called UAP-BCIoT, which relies on Elliptic Curve Cryptography (ECC). The mutual authentication between the user and an IoT device happens via the semi-trusted Cloud-Gateway (CG) node in UAP-BCIoT. UAP-BCIoT supports several functionality features needed for IoT-based ITS environment including IoT smart device credential validation and Big Data analytics. A detailed security analysis is conducted based on the defined threat model to show that UAP-BCIoT is resilient against many known attacks. A thorough comparative study reveals that UAP-BCIoT supports better security, offers various functionality attributes, and also provides similar costs in communication as well computation as compared to other relevant schemes Finally, the practical demonstration of the proposed UAP-BCIoT is also provided to measure its impact on the network performance parameters.

Abstract

Drones, which are also known as Unmanned Aerial Vehicles (UAVs), are very useful in delivering the packages, and real-time object detection and tracking with minimal human interference. However, there may be several security threats in such an environment, for instance, a malicious user can spy unauthorized drones, transfer malicious packages, or even damage the network reliability that can have direct impact on drones control. This may lead to a potential threat for people, governments, and business sectors. To deal with these issues, in this paper, we propose a novel access control scheme for unauthorized UAV detection and mitigation in an Internet of Drones (IoD) environment, called ACSUD-IoD. With the help of the blockchain-based solution incorporated in ACSUD-IoD, the transactional data having both the normal secure data from a drone (UAV) to the Ground Station Server and the abnormal (suspected) data for detection of unauthorized UAVs by the are stored in private blockchain, that are authentic and genuine. As a result, the Big data analytics can be performed on the authenticated transactional data stored in the blockchain. Through the detailed security analysis including formal security under the broadly-accepted Real-Or-Random (ROR) model, formal security verification using the widely-applied Automated Validation of Internet Security Protocols and Applications (AVISPA) tool and non-mathematical security analysis show the robustness of the proposed scheme against a number of potential attacks needed in an IoD environment. The testbed experiments for various cryptographic primitives using the broadly-accepted Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) have been performed under both server and Raspberry PI 3 configurations. Furthermore, a detailed comparative analysis among the proposed scheme and other existing competing schemes shows the efficacy and more robustness as compared to the existing schemes. Finally, the blockchain-based practical demonstration shows the effectiveness of the proposed scheme.

Abstract

5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, replay, impersonation, password reckoning, physical device stealing, session key computation, privileged-insider, malware, man-in-the-middle, malicious routing, and so on). It is then very crucial to protect the infrastructure of 5G-enabled IoT communication environment against these attacks. This necessitates the researchers working in this domain to propose various types of security protocols under different types of categories, like key management, user authentication/device authentication, access control/user access control and intrusion detection. In this survey paper, the details of various system models (i.e., network model and threat model) required for 5G-enabled IoT communication environment are provided. The details of security requirements and attacks possible in this communication environment are further added. The different types of security protocols are also provided. The analysis and comparison of the existing security protocols in 5G-enabled IoT communication environment are conducted. Some of the future research challenges and directions in the security of 5G-enabled IoT environment are displayed. The motivation of this work is to bring the details of different types of security protocols in 5G-enabled IoT under one roof so that the future researchers will be benefited with the conducted work.

Abstract

Access control is one of the important security services that is essential for an Internet of Things (IoT)-enabled authorized user using his/her smart mobile device to authenticate with the trusted Hospital Authority (HA) in a hospital. After mutual authentication, a secret key is established among the user and HA for secure data transmission. The secure data (transactions) gathered by the HA from the users in the hospital is encrypted using a shared key among various trusted hospital authorities involved in the private blockchain network of hospitals. The HA of each hospital is responsible for constructing the blocks in the blockchain using the encrypted transactions because the data in healthcare application is treated as confidential and private. To deal with this important problem, we design a novel access control scheme using private blockchain technology. The proposed scheme is shown to be secure against various well-known attacks. Moreover, the proposed scheme provides better security and functionality features, and also requires low communication and computational costs as compared to relevant approaches.

Abstract

6G-enabled network in a box (NIB) is a multi- generational, rapidly deployable hardware and software technology for the communication. 6G-enabled NIB provides high level of flexibility which makes it capable to provide connectivity services for different types of applications as it is effective for the communications of after disaster scenario battlefields scenario and industrial scenario. In 6G-enabled NIB deployed industrial applications, various passive and active attacks are possible because the involved entities communicate over insecure channel. In this paper, a new remote user authentication and key management scheme is proposed for securing 6G-enabled NIB deployed for industrial applications, which we call in short as UAKMS-NIB. The security analysis shows the resilience of the UAKMS-NIB against various types of possible attacks. The practical demonstration of UAKMS-NIB is also provided to measure its impact on the network performance parameters.

Abstract

With the emergence of the concept of smart city and the increasing demands for a range of vehicles, Internet of Vehicles (IoV) has achieved a lot of attention by providing multiple benefits, including vehicle emergence, accidents, levels of pollution, and traffic congestion. Moreover, IoV provides various services by combining vehicular ad-hoc networks (VANET) with the Internet of Things (IoT) in smart cities. However, the communication among vehicles is susceptible to various security threats because the sensitive message is transmitted via a insecure channel in the IoV-based smart city environment. Thus, a secure message authentication protocol is indispensable to ensure various services for IoV in a smart city environment. In 2020, a secure message authentication protocol for IoV communication in smart cities has been proposed. However, we discover that the analyzed scheme suffers from various potential attacks such as impersonation, secret key disclosure, and off-line guessing attacks, and also does not ensure authentication. To solve the security threats of the analyzed scheme, we design a secure and efficient message authentication protocol for IoV in a smart city environment, called IoV-SMAP. The proposed IoV-SMAP can resist security drawbacks and provide user anonymity, and mutual authentication. We demonstrate the security of IoV-SMAP by performing informal and formal analyses such as the Real-or-Random (ROR) model, and Automated Validation of Internet Security Protocols and Application (AVISPA) simulations. In addition, we compare the performance of IoV-SMAP with related existing competing authentication schemes. We demonstrate that IoV-SMAP provides better security along with efficiency than related competing schemes and is suitable for the IoV-based smart city environment.

Abstract

The real-world data is often susceptible to label noise, which might constrict the effectiveness of the existing state of the art algorithms for ordinal regression. Existing works on ordinal regression do not take label noise into account. We propose a theoretically grounded approach for class conditional label noise in ordinal regression problems. We present a deep learning implementation of two commonly used loss functions for ordinal regression that is both-1) robust to label noise, and 2) rank consistent for a good ranking rule. We verify these properties of the algorithm empirically and show robustness to label noise on real data and rank consistency. To the best of our knowledge, this is the first approach for robust ordinal regression models.

Abstract

Spoken dialogue systems typically use one or several (top-N) ASR sequence(s) for inferring the semantic meaning and tracking the state of the dialogue. However, ASR graphs, such as confusion networks (confnets), provide a compact representation of a richer hypothesis space than a top-N ASR list. In this paper, we study the benefits of using confusion networks with a neural dialogue state tracker (DST). We encode the 2-dimensional confnet into a 1-dimensional sequence of embed-dings using a confusion network encoder which can be used with any DST system. Our confnet encoder is plugged into the 'Global-locally Self-Attentive Dialogue State Tacker' (GLAD) model for DST and obtains significant improvements in both accuracy and inference time compared to using top-N ASR hypotheses .

Abstract

Dysarthria is a motor speech disorder leading to imprecise articulation of speech. Acoustic analysis capable of detecting and assessing articulation errors is useful in dysarthria diagnosis and therapy. Since speakers with dysarthria experience difficulty in producing rhotics due to complex articulatory gestures of these sounds, the hypothesis of the present study is that duration of the rhotic approximant /ɹ/ distinguishes dysarthric speech of different severity levels. Duration measurements were conducted using the third formant (F3) trajectories estimated from quasi-closed-phase (QCP) spectrograms. Results indicate that the severity level of spastic dysarthria has a significant effect on duration of /ɹ/. In addition, the phonetic context has a significant effect on duration of /ɹ/, the ɪ-r-ɛ context showing the largest difference in /ɹ/ duration between dysarthric speech of the highest severity levels and healthy speech. The results of this preliminary study can be used in the future to develop signal processing and machine learning methods to automatically predict the severity level of spastic dysarthria from speech signals.

Abstract

We consider the task of Medical Concept Normalization (MCN) which aims to map informal medical phrases such as “loosing weight” to formal medical concepts, such as “Weight loss”. Deep learning models have shown high performance across various MCN datasets containing small number of target concepts along with adequate number of training examples per concept. However, scaling these models to millions of medical concepts entails the creation of much larger datasets which is cost and effort intensive. Recent works have shown that training MCN models using automatically labeled examples extracted from medical knowledge bases partially alleviates this problem. We extend this idea by computationally creating a distant dataset from patient discussion forums. We extract informal medical phrases and medical concepts from these forums using a synthetically trained classifier and an off-the-shelf medical entity linker respectively. We use pretrained sentence encoding models to find the k-nearest phrases corresponding to each medical concept. These mappings are used in combination with the examples obtained from medical knowledge bases to train an MCN model. Our approach outperforms the previous state-of-the-art by 15.9% and 17.1% classification accuracy across two datasets while avoiding manual labeling.