Abstract

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.

Abstract

Fog computing-based Internet of Things (IoT) architecture is useful for various types of delay efficient net- work communications and services, like digital healthcare. However, there are privacy and security issues with the fog computing-based healthcare systems, which can further increase the risk of leakage of sensitive healthcare data. Therefore, a secu- rity mechanism, such as access control for fog computing-based healthcare systems, is needed to protect its data against various potential attacks. Moreover, the blockchain technology can be used to solve the digital healthcare’s data integrity related problems. The use of Artificial Intelligence (AI) further makes the system more effective in case of prediction of health related diseases. In this paper, an AI-enabled secure communication mechanism in fog computing-based healthcare system (in short, AISCM-FH) has been proposed. The security analysis of the proposed AISCM-FH is provided using the standard random oracle model and also with the heuristic (non-mathematical) security analysis. A pragmatic study determines the impact of the proposed AISCM-FH on key performance indicators. Moreover, we include a detailed performance comparison of AISCM-FH with other relevant existing schemes to show that it has low communication and computation costs, and provides superior security and extra functionality attributes as compared to those for other competing existing approaches.

Abstract

Sustainable smart healthcare applications are those in which health services can be provided to remotely located patients through the Internet without placing extra burden on environmental resources. They should be operated with minimum power consumption using biodegradable, recyclable, and environmentally friendly healthcare equipment and products. In an Internet of Medical Things (IoMT)-enabled sustainable smart healthcare environment, all the health services are capable of producing informative data whenever some raw information is provided as the input or are capable of performing work on their own with less intervention from humans. As a result, they provide great advantages over the traditional healthcare system. As sustainable smart healthcare devices are operated through the Internet, it is possible that they could be attacked by various hackers. To mitigate these issues, in this paper, we propose a new access control along with a key-establishment mechanism for a sustainable smart healthcare system. The results of the security analysis showed that the proposed scheme was highly robust against a variety of passive and active attacks. In comparison to existing competing schemes, the proposed scheme is lightweight, as well as delivers high security and additional functionality. Finally, a practical demonstration of the proposed scheme is provided to show its impact on the key network performance parameters. Keywords: sustainability; smart healthcare; Internet of Medical Things (IoMT); authentication; security

Abstract

The emergence of contemporary technologies like cloud computing and the Internet of Things (IoT) has revolutionized the trends in the cyber world to serve humanity. There are plenty of applications in which they are being used, especially in smart cities and their constituents, Maritime Transportation System (MTS) is one of them. The IoT-enabled MTS has the potential to entertain the growing challenges of modern-day ship transportation. Secure real-time data access from numerous smart IoT devices is the most critical and crucial exercise for Big Data acquisition in IoT-enabled MTS. Therefore, we have developed a Physically Unclonable Function (PUF) based authenticated key agreement solution to deal with this challenge. This solution enables the mobile user and IoT node to mutually authenticate each other via Cloud-Gateway before real-time data exchange and transmission in IoT-enabled MTS. The use of PUF in our solution brings invincibility against physical security threats. An inclusive security analysis under the assumption of the specified threat model is carried out to substantiate the security resilience of our solution. The conduct of our solution is realized through security features, communication, and computation cost and It has been observed that our solution achieves efficiency of 37.3% and 9.7% in communication and computation overhead, respectively. Moreover, the network performance effectiveness of our solution is demonstrated

Abstract

Medical image security includes copyright protection, authentication, data integrity and confidentiality simultaneously. In this article, a new robust and fragile medical image security scheme has been introduced. The Beddington, Free and Lawton (BFL) map and the Hénon map based image encryption scheme is brought out for confidentiality. Further, a Discrete Wavelet Transform (DWT)-based image watermarking scheme is developed for copyright protection, authentication and data integrity. The proposed scheme is essentially robust and retrievable in terms of the watermark; however, it is highly sensitive and irretrievable with reference to the host image. The scheme separates the host image in Region of Interest (ROI) and Region of Non-Interest (RONI) parts, and it embeds the encoded message inside the RONI part. In addituon, the proposed scheme does not need any side information for message

Abstract

Over the past few years, online social networks (OSNs) have become an inseparable part of people's daily lives. Instead of being passive readers, people are now enjoying their role as content contributors. OSN has permitted its users to share their information including the multimedia content. OSN users can express themselves in virtual communities by providing their opinions and interacting with others. As a consequence, the privacy and security threats in OSNs have emerged as a major concern to the research and business world. In the recent past, a number of survey works have been conducted to discuss different security and privacy threats in OSNs. However, till date, no survey work has been conducted that aims to classify and analyze various machine learning (ML)-based solutions adapted for the security defense of OSNs. In this survey article, we present a detailed taxonomy with a classification of various works done on various security attacks in OSNs. We then review and summarize the existing state of art survey works on OSN security, and indicate the merits and limitations of these survey works. Next, we review all recent works that aim to provide ML-based solutions toward defense of security attacks on OSNs. Finally, we discuss the future road-map on OSN security and provide a comprehensive analysis on the open research issues with feasible measurements and possible solutions.

Abstract

We consider a setup where Alice is transmitting channel coded messages to Bob and intruder Willie is snooping over this communication. Willie does not know the channel code that Alice is using and wishes to identify it in order to decode the intercepted messages. This problem is termed as blind reconstruction of channel codes and has possible applications in cognitive radios and military. In this work, we consider the situation when Willie knows that Alice's channel code belongs to the family of binary cyclic codes and the underlying communication channel is the binary erasure channel. We present an algorithm to identify the parameters of the unknown channel code corresponding to the given data. The key step in our algorithm consists of distinguishing between the two situations when all of the assumed parameters are correct and when either of the parameter is incorrect. As part of analysis, we present a lower bound on probability of correctly distinguishing between these two situations.

Abstract

Maintaining the river water quality has become more prominent these days due to excess pollutants from various sources like industries and domestic sewage discharge. The assessment of river water quality is a significant task for the management and sustainability of the riverine health system for future generations. This study evaluates the water quality of the Bhadra river stretch in India using the water quality index and qual2k model. The water quality index (WQI) is estimated using Weighted Average Water Quality Index (WAWQI) method and water quality indicators like Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), Electrical Conductivity (Ec), Nitrate (NO3), pH, and Temperature are simulated using Qual2k model. The Qual2k model was calibrated and validated for a period of 91-months (April 2006 to October 2013). The estimated WQI values range from 92.35 to 112, and the quality classification ranges from very poor to unfit for consumption. It was observed that the quality status of the river water was very poor in upstream and downstream segments, while unfit for consumption in the middle segment indicating access to industrial and anthropogenic activities. To conserve river water from pollution, strict laws and regulations must be implemented to improve health and preserve water resources for future generations.

Abstract

After one learns to program in miniKanren, it is natural to want to understand how it is implemented. However, there is no resource to learn how to implement miniKanren with constraints such as =/=, symbolo, numbero, and absento. Furthermore, these are used to implement a wide class of interesting programs such as relational interpreters and relational type inferencers, making the need for such a resource all the more felt. This paper aims to be that resource. Additional Key Words and Phrases: miniKanren implementation, Racket, relational programming

Abstract

Investigation of microstates extracted from EEG signals of tip-pinch and wrist flexion/extension movement