Abstract
Technological advancement in the recent decades enhanced the calibre of human life. Contemporary research in machine learning (ML) exhibits a mock-up to make decisions on its own and is applied in various fields including medical diagnosis, email filtering, banking, computer vision, financial marketing, image processing, cyber security. The systems inter-connected across the world via internet are attacked by hackers, and it is prevented by cyber security. The optimum solution for cyber-attacks is attained by collaborating ML techniques with cyber security and envisioned issues are designed by cyber machine learning models. In this chapter, an algorithm is proposed to defend data by encoding the text to an unintelligent text and decoding it to original text by applying graph labelling in cryptography. Symmetric key is designed based on the edge label of an odd-even congruence graph to achieve secured communication in CPS. In addition, a program is suggested using Python programming to attain cipher text and its converse.
TopIntroduction
This chapter aims to design a protected structure for Cyber Physical System(CPS). CPS is necessary for the succeeding generations smart systems, constructed as a network of relation between substantial and virtual model. Its functioning hangs on the concrete process along with feedback loops. Revelation of new concepts revamped the Information Technology every day and one such mandatory system is CPS, introduced in 2006. In late 2006 US National Foundation recognized CPS as a cue field for research and funded for numerous workshop related to CPS. Initially CPS is used in the infrastructure of power grids, home automation system and later in almost all fields. Security challenges of CPS and its security control systems were analysed during 2006 to 2009 (Jairo Giraldo, Esha Sarkar, Alvaro A. Cardenas Michali Maniatakos and Murat Kantarcioglu, 2017). In the succeeding years, CPS transmogrified as a sophisticated domain with countless attributes such as complex systems (Vegh and Miclea, 2014) and acquire data from sensor device machine to carry over in network with or without machine and human interaction (Bhabad and Scholar, 2015). Several security challenges arise, including securing protocols and initialization of certainty among CPS subsystems (Lu et al., 2013). Subsequently safety liability of CPS was investigated and its safety measures were initiated for confidentiality (Jacub Wrm, Yier Jin, Yang Lice and Shiyan Hu, 2016).
Cryptography and Steganagraphy is merged to arrive a new solution for data security, moreover hierarchical approach is employed to the information for universal security (L. Vegh and L. Miclea, 2014). Cryptography is applied to frame secured physical unclonable function using Fuzzy extractors to overwhelm security obstacles, but still it retains the challenges in feedback protocols (Jin C, Herder L, Nguyen P.H, Fuller B, Devadas and Van Dijk M, 2017). To protect the information in sensor communication channel, an event-based cryptographic encryption function is established using non deterministic automation (Publio M. Lima, Lilian K. Carvalho, Marcos V. Moreira, 2020). To reduce the encryption time, a new encryption algorithm is proposed, to separate the sensitive information to be secured in CPS and encrypt it instead of encrypting all communications (Xiaogang Zhu, Gautam Srivastara and Reza M. Parizi, 2019). An advanced cryptographic method is employed to perform the operations directly on encrypted values without decrypting the text, but it occupies more memory space (Junsoo Kim, Chanhwa Lee, Hyungbo Shim, Jung Hee Cheon, Andrey Kim, Miran Kim and Yongsoo Song, 2016). In addition, to enhance security various graph labelling technique such as product mod labelling (Deepa B, Maheswari and Balaji V, 2019), Vertex magic total labelling (Rahul Chawla, Sagar Deshpande, Manas M. N, Saahil Chhabria and Krishnappa H. K, 2019), Inner magic and inner antimagic labelling (Auparajita Krishnaa, 2019), Super magic labelling (Giridaran M, 2020), Antimagic labelling (Dharmendra Kumar Gurjar, Auparajita Krishnaa, 2021) are utilized to propose the encryption algorithm.
In this chapter, the authors proposed new algorithm to encrypt and decrypt the messages by employing odd-even congruence graph labelling technique in cryptography to ensure confidentiality and security for data in the CPS. It is more secure to manipulate graphs in cryptography, since it is difficult to identify the graph and its labelling technique which is utilized for encryption. Furthermore a Python coding is developed to generate the cipher text in earlier.
Key Terms in this Chapter
Cipher Text: It is an unreadable output of an encoding algorithm.
Encoding: The process of converting data from one form to another form is referred as encoding.
Congruence Labelling: Suppose G = (V,E) be a graph with |V| = p and |E| = q is referred as a congruence graph, if there exist a vertex labelling function f: V ?{1,2,....k} for every v i in V and induces edge labelling g: E?{1,2,3,....k} such that f(v i ) = f(v j ) mod g(e i ) for every e i = v i v j , where k = min{2|V|,2|E|}.
Graph Labelling: Graph labelling is the assignment of integers to vertices and edges.
Graph: The graph comprises of vertices and edges to represent the mathematical structures to model pairwise relations between objects.
Cryptography: Cryptography is a method of protecting information and communications in computer systems through the use of codes.