Cellular Automata based Cryptography Model for Reliable Encryption Using State Transition in Wireless Network Optimizing Data Security

2021 ◽  
Author(s):  
Ayan Banerjee ◽  
Anirban Kundu
Keyword(s):  
Cellular Automata ◽  
Wireless Network ◽  
Data Security ◽  
State Transition

Phase Transition in Elementary Cellular Automata with Memory

2014 ◽  
Vol 24 (09) ◽  
pp. 1450116 ◽  
Author(s):  
Shigeru Ninagawa ◽  
Andrew Adamatzky ◽  
Ramón Alonso-Sanz
Keyword(s):  
Cellular Automata ◽  
State Transition ◽  
Time Interval ◽  
Complex Behavior ◽  
Weighted Function ◽  
Transition Functions ◽  
Elementary Cellular Automata ◽  
Computational Universality ◽  
With Memory ◽  
Time Automaton

We study elementary cellular automata with memory. The memory is a weighted function averaged over cell states in a time interval, with a varying factor which determines how strongly a cell's previous states contribute to the cell's present state. We classify selected cell-state transition functions based on Lempel–Ziv compressibility of space-time automaton configurations generated by these functions and the spectral analysis of their transitory behavior. We focus on rules 18, 22, and 54 because they exhibit the most intriguing behavior, including computational universality. We show that a complex behavior is observed near the nonmonotonous transition to null behavior (rules 18 and 54) or during the monotonic transition from chaotic to periodic behavior (rule 22).

Data Security in Wired and Wireless Systems

2019 ◽  
pp. 1213-1240
Author(s):  
Abhinav Prakash ◽  
Dharma Prakash Agarwal
Keyword(s):  
Wireless Network ◽  
Data Security ◽  
Security Protocols ◽  
Wireless Systems ◽  
Main Tool ◽  
Security Protocol ◽  
Network Data ◽  
Wireless Technologies ◽  
Secured Communication ◽  
Different Parts

The issues related to network data security were identified shortly after the inception of the first wired network. Initial protocols relied heavily on obscurity as the main tool for security provisions. Hacking into a wired network requires physically tapping into the wire link on which the data is being transferred. Both these factors seemed to work hand in hand and made secured communication somewhat possible using simple protocols. Then came the wireless network which radically changed the field and associated environment. How do you secure something that freely travels through the air as a medium? Furthermore, wireless technology empowered devices to be mobile, making it harder for security protocols to identify and locate a malicious device in the network while making it easier for hackers to access different parts of the network while moving around. Quite often, the discussion centered on the question: Is it even possible to provide complete security in a wireless network? It can be debated that wireless networks and perfect data security are mutually exclusive. Availability of latest wideband wireless technologies have diminished predominantly large gap between the network capacities of a wireless network versus a wired one. Regardless, the physical medium limitation still exists for a wired network. Hence, security is a way more complicated and harder goal to achieve for a wireless network (Imai, Rahman, & Kobara, 2006). So, it can be safely assumed that a security protocol that is robust for a wireless network will provide at least equal if not better level of security in a similar wired network. Henceforth, we will talk about security essentially in a wireless network and readers should assume it to be equally applicable to a wired network.

scholarly journals Characterization of the Evolution of Nonlinear Uniform Cellular Automata in the Light of Deviant States

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Pabitra Pal Choudhury ◽  
Sudhakar Sahoo ◽  
Mithun Chakraborty
Keyword(s):  
Cellular Automata ◽  
Cellular Automaton ◽  
Boolean Functions ◽  
State Transition ◽  
The State ◽  
Transition Diagram ◽  
One Dimensional ◽  
State Transition Diagram ◽  
Linear Rule

Dynamics of a nonlinear cellular automaton (CA) is, in general asymmetric, irregular, and unpredictable as opposed to that of a linear CA, which is highly systematic and tractable, primarily due to the presence of a matrix handle. In this paper, we present a novel technique of studying the properties of the State Transition Diagram of a nonlinear uniform one-dimensional cellular automaton in terms of its deviation from a suggested linear model. We have considered mainly elementary cellular automata with neighborhood of size three, and, in order to facilitate our analysis, we have classified the Boolean functions of three variables on the basis of number and position(s) of bit mismatch with linear rules. The concept of deviant and nondeviant states is introduced, and hence an algorithm is proposed for deducing the State Transition Diagram of a nonlinear CA rule from that of its nearest linear rule. A parameter called the proportion of deviant states is introduced, and its dependence on the length of the CA is studied for a particular class of nonlinear rules.

Self-Description for Construction and Computation on Graph-Rewriting Automata

2007 ◽  
Vol 13 (4) ◽  
pp. 383-396 ◽  
Author(s):  
Kohji Tomita ◽  
Satoshi Murata ◽  
Haruhisa Kurokawa
Keyword(s):  
Structural Change ◽  
Cellular Automata ◽  
State Transition ◽  
Graph Structure ◽  
Graph Rewriting ◽  
Rewriting Systems ◽  
Lattice Space ◽  
Rule Sets ◽  
Graph Rewriting Systems ◽  
Self Replication

This article shows how self-description can be realized for construction and computation in a single framework of a variant of graph-rewriting systems called graph-rewriting automata. Graph-rewriting automata define symbol dynamics on graphs, in contrast to cellular automata on lattice space. Structural change is possible along with state transition. Self-replication based on a self-description is shown as an example of self-description for construction. This process is performed using a construction arm, which is realized as a subgraph, that executes a program described in the graph structure. In addition, a metanode structure is introduced to embed rule sets in the graph structure as self-description for computation. These are regarded as universal graph-rewriting automata that can serve as a model of systems that maintain themselves through replication and modification.

scholarly journals DESIGNING COMPLEX DYNAMICS IN CELLULAR AUTOMATA WITH MEMORY

2013 ◽  
Vol 23 (10) ◽  
pp. 1330035 ◽  
Author(s):  
GENARO J. MARTÍNEZ ◽  
ANDREW ADAMATZKY ◽  
RAMON ALONSO-SANZ
Keyword(s):  
Cellular Automata ◽  
State Transition ◽  
Complex Dynamics ◽  
Transition Function ◽  
Systematic Analysis ◽  
Time Dynamics ◽  
Controversial Topic ◽  
State Transition Function ◽  
And Behavior ◽  
With Memory

Since their inception at Macy conferences in later 1940s, complex systems have remained the most controversial topic of interdisciplinary sciences. The term "complex system" is the most vague and liberally used scientific term. Using elementary cellular automata (ECA), and exploiting the CA classification, we demonstrate elusiveness of "complexity" by shifting space-time dynamics of the automata from simple to complex by enriching cells with memory. This way, we can transform any ECA class to another ECA class — without changing skeleton of cell-state transition function — and vice versa by just selecting a right kind of memory. A systematic analysis displays that memory helps "discover" hidden information and behavior on trivial — uniform, periodic, and nontrivial — chaotic, complex — dynamical systems.

A MATHEMATICAL FRAMEWORK FOR CELLULAR LEARNING AUTOMATA

2004 ◽  
Vol 07 (03n04) ◽  
pp. 295-319 ◽  
Author(s):  
HAMID BEIGY ◽  
M. R. MEYBODI
Keyword(s):  
Cellular Automata ◽  
State Transition ◽  
Transition Probability ◽  
Learning Automata ◽  
Mathematical Framework ◽  
Stochastic Cellular Automata ◽  
Learning Automaton ◽  
State Transition Probability ◽  
Theoretical Investigations ◽  
Cellular Learning Automata

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations.

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