Analysis on Cryptographic Design Techniques of Stream Ciphers and Attacks

Introduction

Advanced technologies such as blockchain based Internet of Things (IoT), cloud computing, interconnected smart cities play a significant role in managing efficient urban development with interoperability. Blockchain play a major role in enabling participants to securely communicate with reliability and transparency. When exchange of data is huge in volume, there is a need of lightweight efficient cryptographic techniques. The stream cipher is a subset of symmetric cryptosystems that benefits from the speed and scale of hardware design. It has always been a popular and essential research area in cryptography, and it is appropriate for use in circumstances of huge data transmission or resource limits. Cipher algorithms are becoming increasingly important in the security of information with the rapid advancement of network and communication technology. Instantaneously, the application background for cipher algorithms is becoming increasingly complicated, posing problems to existing cipher algorithms and necessitating the development of new appropriate designs shown by (Dey & Sarkar, 2021). Cloud computing is becoming more popular for everyday storage and computing requirements. Its applications range from keeping confidential files to preserving government statistics, enterprise business data, information from Internet of things, remote sensing data, and relevant information from cloud-based social media apps, among others.

Cloud users, on the other hand, become sufferers of the security breaches exposed by cloud storage due to a lack of understanding and sufficient security measures. To prevent security risks, both the Cloud Storage service Provider (CSP) and the users should utilize suitable approaches and maintain a high standard. Multiple users transfer their information to store it to the cloud, which is spread across a number of devices. Cloud storage companies' insiders have insight to the information's contents. Users of cloud data have little direct control over their data once it has been uploaded, therefore maintaining confidentiality, privacy, and integrity are the most difficult challenges. Cryptography techniques can help Cloud Storage accomplish its data secrecy, data integrity, and data and privacy protection goals. To do this, user data is encrypted with a key before being outsourced, allowing for regulated, allowed access. Unauthorized users cannot access the data by employing powerful encryption procedures like stream ciphering and block ciphering. The security issues in cloud storage using stream ciphering algorithms are examined in this work in order to improve its security. The security measures created by various organizations are discussed further. This survey allows researchers to get a larger overview of online storage security vulnerabilities and identify outstanding issues that may be investigated further. Due to a flaw in the encryption process, an attacker might brute force his way into the network and launch a meet-in-the-middle (MITM) assault. Spoofing on anything is commonly involved in MITM attacks. Authentication mechanisms based on public-private key pairs, such as RSA and ECDH, can be employed at various stages of the stack to assist assure authorized communication.

This chapter provides a comprehensive review on stream ciphers, categorizes and encapsulates the design guidelines of classic stream ciphers in clusters, and provides a brief the strengths and drawbacks of diverse stream ciphers in relation to security and incorporation to facilitate stringent requirements and also provide a valid research framework for successful designs. Furthermore, it attempts to predict how stream ciphers will be designed in the future. Multimedia data including health information is frequently huge and expensive. Conventional block encryption methods including such Advanced Encryption Standard (AES), Data Encryption Standard (DES), RSA, BLOWFISH, and IDEA have been proven to be ineffective for multimedia applications in real-time applications due to their slowness in handling large amounts of data.