Intelligent Cipher Transfer Object for IoT Data Security

Data security is a critical element of every digital service and communication infrastructure in today’s interconnected world. With increasing popularity of Internet of Things (IoT), and an overall rise in the number of communication capable devices, it is necessary to explore alternative data security approaches and hence address issues/limitations in existing ones. This research aims to conduct a comprehensive performance and security analysis of the Intelligent Cipher Transfer Object (ICTO) technology, which secures data by embedding it with “self-governance” or “intelligent” modules enabling it to act on its own behalf. The study focuses on evaluating the system and network efficiency, as well as potential security vulnerabilities using conventional cryptanalysis and ML. The research methodology consists of three main components. Firstly, an extensive literature review is conducted to establish a theoretical foundation and identify issues and limitations associated with existing security frameworks. Secondly, performance evaluation is performed to assess the technology’s efficiency in terms of computational overhead, network overhead, and communication latency. Thirdly, a comprehensive security analysis is conducted to explore potential vulnerabilities. Cryptanalytic methods such as frequency analysis, index-of-coincidence/positional analysis, and probability analysis along with unsupervised ML algorithms including k-means and agglomerative clustering are utilized to discover potential weaknesses. Overall, this research contributes to assess the potential of the ICTO concept to be useful for securing IoT communications.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Engineering

Grantor

Texas State University

Year dc:date.issued

2023

Author dc:creator

• Sharma, Bishal

Advisor dc:contributor.advisor

• McClellan, Stan

Committee members dc:contributor.committeemember

• Aslan, Semih
• Valles, Damian
• Carvallo, Andres

• IoT
• ICTO
• Ingram School of Engineering
• data security

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10877/17103

OAI identifier oai:identifier

oai:digital.library.txst.edu:10877/17103