Current Security Techniques Research Articles

COVID-19: Secure Healthcare Internet of Things Networks, Current Trends and Challenges with Future Research Directions

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes.

Deep Learning-based Continuous Authentication for an IoT-enabled healthcare service

The Internet of Things (IoT) has introduced a new dimension to the Internet in the last decade; nonetheless, security, particularly attacks on authentication, continue to be a significant concern in IoT. The majority of research endeavours consider external attacks that originate from outside of an IoT network. Their authentication mechanisms authenticate users at the outset of a session. However, a device or user within the network may be a more significant threat than the external attacker due to their accessibility. An intruder during the session can physically grasp any IoT device and impersonate it. Therefore, the suggested security system continuously authenticates legitimate users inside a session. The system takes data from users and authenticates them using a Deep Learning-based Long Short-Term Memory classification algorithm. There are 3.5 percent false acceptances and 2.4% false rejections for the security system. The research also compared the suggested approach to other current security techniques.

Big Data Analytics and Artificial intelligence in business and Marketing: Cloud security and encryption influencing business

Security is one of the most important aspects of any business. However, it is often the most neglected aspect as most businesses focus their resources on development and marketing. Security is the paramount concern of the user in the domain of cloud computing. Cloud based services offer a certain level of security from various attacks but a lot of the responsibility lies in the hands of the user. Furthermore, the current protocols and technologies employed in cloud services have a lot of potential for exploits that are ignored due to lower priority. In this paper the aspects of cloud security that are most important from a business owners’ standpoint are explored. Current security techniques have been discussed and state of art optimized framework has been proposed along with discussion about the complexity and usability of said framework.

A group-key-based sensitive attribute protection in cloud storage using modified random Fibonacci cryptography

Cloud computing is an eminent technology for providing a data storage facility with efficient storage, maintenance, management and remote backups. Hence, user data are shifted from customary storage to cloud storage. In this transfer, the sensitive attributes are also shifted to cloud storage with high-end security. Current security techniques are processed with high encryption time and provide identical security of entire data with single key dependent. These processes are taking high computational time and leaks entire information if the key is hacked. The proposed Group Key Based Attribute Encryption using Modified Random Fibonacci Cryptographic (MRFC) technique rectifies these issues. Instead of machine learning technique, data owner preference-based attributes segregation is used to divide an input dataset into sensitive and non-sensitive attribute groups. Based on inter-organization usage and data owner’s willingness, sensitive attribute is divided into ‘n + 1′ subgroups and each subgroup is encrypted by ‘n + 1’ group keys. The encrypted sensitive subgroups are merged with non-sensitive attributes and uploaded into a private cloud. The novelties of this paper are, (1) data owner preferred sensitive attribute classification instead of machine learning algorithms, (2) sensitive attribute encryption instead of entire attributes, (3) To reduce encryption time without compromising data owner privacy, (4) To decrypt and access the required subgroup instead of the entire attribute. Our experimental results show that, the proposed method takes minimal processing time, better classification accuracy and minimal memory space with high security to selected attributes as compared to existing classification and security techniques. Hence, sensitive data security and privacy is achieved with minimal processing cost.

A Review of Artificial Immune System Based Security Frameworks for MANET

Mobile ad hoc networks (MANETs) are collections of wireless mobile devices that form a communication network with restricted broadcast range, limited resources and without fixed infrastructure. Routing is a critical function in multi-hop MANETs. At the same time, security in MANETs—especially routing security—presents a number of new and interesting challenges. Communication is achieved by relaying data along routes that are dynamically discovered and maintained through collaboration between the nodes. Advances in the field of artificial immune systems provide an opportunity to improve MANET security and performance. Artificial immune systems mimic the functionality of the human immune system wherein there is clear distinction between self and non self and this delineation is important in a MANET where there is no centralized management. The high level of protection provided to the human body by an evolved immune system can be applied as a security feature in MANET. The current security techniques proposed for MANET have varying degrees of success due to the dynamic nature of a MANET. This paper will review different strategies for the application of artificial immune systems to MANETs.

Unshared Secret Key Cryptography

Current security techniques can be implemented with either secret key exchange or physical-layer wiretap codes. In this paper, we investigate an alternative solution for MIMO wiretap channels. Inspired by the artificial noise (AN) technique, we propose the unshared secret key (USK) cryptosystem, where the AN is redesigned as a one-time pad secret key aligned within the null space between a transmitter and a legitimate receiver. The proposed USK cryptosystem is a new physical-layer cryptographic scheme, which was obtained by combining traditional network-layer cryptography and physical-layer security. Unlike previously studied AN techniques, rather than ensuring nonzero secrecy capacity, the USK is valid for an infinite lattice input alphabet and guarantees Shannon's ideal secrecy and perfect secrecy without the need for secret key exchange. We then show how ideal secrecy can be obtained for finite lattice constellations with an arbitrarily small outage.