Providing security to the healthcare data stored in an IoT-cloud environment is one of the most challenging and demanding tasks in recent days. Because the IoT-cloud framework is constructed with an enormous number of sensors that are used to generate a massive amount of data, however, it is more susceptible to vulnerabilities and attacks, which degrades the security level of the network by performing malicious activities. Hence, Artificial Intelligence (AI) technology is the most suitable option for healthcare applications because it provides the best solution for improving the security and reliability of data. Due to this fact, various AI-based security mechanisms are implemented in the conventional works for the IoT-cloud framework. However, it faces significant problems of increased complexity in algorithm design, inefficient data handling, not being suitable for processing the unstructured data, increased cost of IoT sensors, and more time consumption. Therefore, this paper proposed an AI-based intelligent feature learning mechanism named Probabilistic Super Learning- (PSL-) Random Hashing (RH) for improving the security of healthcare data stored in IoT-cloud. Also, this paper is aimed at reducing the cost of IoT sensors by implementing the proposed learning model. Here, the training model has been maintained for detecting the attacks at the initial stage, where the properties of the reported attack are updated for learning the characteristics of attacks. In addition to that, the random key is generated based on the hash value of the data matrix, which is incorporated with the standard Elliptic Curve Cryptography (ECC) technique for data security. Then, the enhanced ECC-RH mechanism performs the data encryption and decryption processes with the generated random hash key. During performance evaluation, the results of both existing and proposed techniques are validated and compared using different performance indicators.

/pdf/healthcare-data-security-using-iot-sensors-based-on-random-1zvgzzak.pdf

Healthcare Data Security Using IoT Sensors Based on Random Hashing Mechanism

Abstract Numerous users are experiencing unsafe communications due to the growth of big network mediums, where no node communication is detected in emergency scenarios. Many people find it difficult to communicate in emergency situations as a result of such communications. In this paper, a mobile cloud computing procedure is implemented in the suggested technique in order to prevent such circumstances, and to make the data transmission process more effective. An analytical framework that addresses five significant minimization and maximization objective functions is used to develop the projected model. Additionally, all mobile cloud computing nodes are designed with strong security, ensuring that all the resources are allocated appropriately. In order to isolate all the active functions, the analytical framework is coupled with a machine learning method known as Decision Tree. The suggested approach benefits society because all cloud nodes can extend their assistance in times of need at an affordable operating and maintenance cost. The efficacy of the proposed approach is tested in five scenarios, and the results of each scenario show that it is significantly more effective than current case studies on an average of 86%. 

/pdf/an-archetypal-determination-of-mobile-cloud-computing-for-2ro9dlis.pdf

An archetypal determination of mobile cloud computing for emergency applications using decision tree algorithm

Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use. 

Hunger games search optimization with deep learning model for sustainable supply chain management

Abstract The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. 

A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

Consensus-based mutual authentication scheme for Industrial IoT

The cloud computing standards are gaining an increased research interest due to various benefits they offer. Though there are so many influences with cloud computing, security and privacy problems are various issues handling with the extensive adaption by the model. Malicious problem of service provider is one more issue which cannot be traceable by data proprietors. Hence, finding the appropriate solutions to these security issues at both administrator level and customer level is very attractive in various directions. Cryptographically enforced access control for securing electronic pathological records (CEASE) is formulated by extending the proposed ciphertext-based attribute-based encryption (CP-ABE) with advanced encryption standard (AES) through limited-shuffle techniques. The main objective of CEASE is to provide data confidentiality, and access control limited-shuffle protects the data from inference attacks and protects the data confidentiality for hot data. In the next step, this research works design a multistage encrypt-or model by differentiating the users as public and personal. Two separate algorithms such as Vigenere encryption algorithm and two-fish encryption are applied in personal and public domain, respectively. Further, where, hierarchical agglomerative clustering (HAC) algorithm is also processed for clustering of users in the public domain by which the overhead decreases effectively. As a final system, this work develops an integrated framework by combining the CP-ABE with AES, multistage encryptor and limited-shuffle. As it is combined with individual methods, this method achieves an efficient performance in the provision of security and data confidentiality.

An Authentication Model with High Security for Cloud Database

Business organizations and individual users are using cloud storage for storing their data and files. Cloud storage is managed by cloud service provider (CSP) being third party person to the data owners. Cloud storage consists of user's confidential data. After storing data in cloud, the owner of data cannot have control over data, where owner cannot trust the CSP because possibility of a malicious administrator. Based on this, different schemes are proposed. Security is a major concern for cloud stored data, and CSP has to provide trust to the data owner on security of the cloud stored data. In general, security to data and applications is provided through authentication and authorization. Security through authentication is provided by distributing user name and password to data users. However, the organizational user is not allowed to access all the organizational data. Authorization for accessing the data is provided by using access control models. Regular models are not enough to use the CSP based on the models uses dynamic method and proposed different models using attribute-based encryption (ABE). Earlier access control models cannot be used because of multiple disadvantages. This chapter will discuss dynamic access control model named as RA-HASBE. This model is proved to be scalable and flexible, due to sub-domain hierarchy. It is also proved to be dynamic by permitting user to access the data by risk evaluation using risk engine.

Data Security in Cloud Computing Using Abe-Based Access Control

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An Authentication Model with High Security for Cloud Database

Data Security in Cloud Computing Using Abe-Based Access Control