Industrial cloud computing and Internet of Things have transformed the healthcare industry with the rapid growth of distributed healthcare data. Security and privacy of healthcare data are crucial challenges in the healthcare industry. This article proposes a novel technique using deep learning and blockchain techniques for electronic health record privacy-preservation. The processed dataset classified normal and abnormal users using the convolutional neural network approach. Then, by using blockchain integrated with a cryptography-based federated learning module, the abnormal users have been processed and removed from the database along with the accessibility for the health records. The simulation has been done in the Python tool and experimental results show that the model’s classification results and performance are better than other existing techniques.

The demand for medical sensors in the Smart Healthcare System (SHS) creates an intelligent Internet of Medical Things (IoMT) system. This system plays an important role in detecting the vital parameters of the human body. However, security and privacy issues in terms of network vulnerability have arisen due to the transmission of data and lack of control over the data. The Intrusion Detection System (IDS) is one of the security solutions to identify various threats and vulnerabilities in the consumable edge-centric IoMT industry. Several IDS techniques have been developed in previous years. However, a real-time and highly accurate attack detection system in the edge-centric IoMT industry is needed. This paper proposes a blended deep learning framework that leverages the strengths and capabilities of different deep learning architectures. The proposed model combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to recognize the latest intruders accurately and defend the healthcare data. The major outcome of the proposed framework is to detect different attacks during data transmission at the edge of the network with high accuracy and efficiency. The proposed model was analyzed on the CSE-CIC-IDS 2018 systematic dataset containing two distinct classes of profiles. The experimental results demonstrate that the proposed framework’s accuracy is higher than the existing approach.

The images generated by smart cameras and sensors in an Industrial Internet of Things (IIoT) ecosystem are at significant risk when transmitted over a public network due to the dynamicity and open nature of the IIoT environment. Encryption is a viable method for safeguarding IIoT digital images. This article addresses an image security framework using chaotic maps and DNA cryptography. The proposed algorithm uses a tent, circle, Chebyshev, and 3-D logistic map in a multilevel fashion to generate three keys. These keys are used for row-column rotation of subblocks, determine the rule to perform DNA encoding–decoding on the subblocks, and generate a key image on which DNA XOR operation is performed to get the encrypted image. The proposed scheme’s result analysis illustrates that the average NPCR (99.6566%), UACI (33.4588%), Entropy (7.9971), and larger key-space of $ 10^{195}$ are better than the existing schemes and are resistant to different attacks.

The Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.