An enhanced encryption-based security framework in the CPS Cloud

Abstract

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

Developing a Security Enhancement for Healthcare Applications Using Blockchain-Based Firefly-Optimized Elliptic Curve Digital Signature Algorithm

“…We made use of the percentage to assess the suggested model's efficacy. In the provided hyper-parameter setup, the model offers accuracy and precision.Cross Breed Blowfish and MD5 (CBM-MD5) (18), Fully Homomorphic encryption algorithm (FHEA) (19), and Improved Chacha20 Algorithm (ICC) (20), can achieve ideal performance.…”

“…The CBM-MD5 maintaining a customized hybrid cryptography system is harder when flaws are found and cryptographic standards change. It could be necessary to make regular upgrades and changes to handle new threats and vulnerabilities [18]. The possibility offering a single symbol error could alter an entire block makes ICC likewise prone to errors [19].…”

Exploring Parallels Between Islamic Theology and Technological Metaphors

“…It transforms information into a secure code and guards against unauthorized access. This technique is fundamental to digital security and ensures that the private data remains confidential and accessible only through a specific key 50,51 . Parallel to this, various cultures and traditions have their own methods of protection.…”

Lightweight similarity checking for English literatures in mobile edge computing

“…Therefore, in future study, we will further consider the privacy disclosure issue in our proposed algorithm by introducing more effective privacy protection techniques including blockchain, federated learning, DP, encryption, etc. [ 50 – 52 ]. In addition, energy saving and cost optimization are also key challenges in typical big data applications [ 53 , 54 ].…”