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Procedia Computer Science 78 (2016) 408 - 414

International Conference on Information Security & Privacy (ICISP2015), 11-12 December 2015,

Nagpur, INDIA

Symmetric Key Encryption Technique: A Cellular Automata based Approach in Wireless Sensor Networks

Satyabrata Roya, Jyotirmoy Karjeeb, U. S. Rawata, Dayama PratikN.c, Nilanjan Deyd

a,b,cSchool of Computing and IT, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India bTechno India College of Technology, Kolkata, West Bengal, 700156, India

Abstract

Cellular Automata (CAs) is one of the most engrossing field for encrypting sensor data applied in Wireless Sensor Networks (WSN). In wireless communications, requirement of security and privacy of information is must. Therefore, transmission of sensor data over wireless communication channel plays a crucial role using cryptography techniques. The usage of cryptography characteristics of cellular automata are still not much explored in WSN. Hence in this paper, we present a symmetric key cryptography technique of block cipher using cellular automata (CAs) rules applied to sensor data in WSN. This cryptography technique uses non- complemented cellular automata rules and hybrid CA rule vector to form a group cellular automata that would be used to encrypt and decrypt sensor data. Proposed methodology has been implemented in C to validate the results.

©2016 The Authors.PublishedbyElsevierB.V. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of organizing committee of the ICISP2015

Keywords: Cryptography technique, cellular automata, encrption, decryption, plaintext, ciphertext, wireless sensor networks.

1. Introduction

The use of Micro-Mechanical Systems (MEMS) in wireless sensor nodes [1] plays an important role for extracting raw data in space and time. Sensor nodes extract raw data (e.g. temperature, humidity, seismic event, pressure, etc.) in Wireless Sensor Networks (WSN) from an event [2]. Thus, WSN are event based networks which collectively extract raw data in any geographical region. Once the raw data is extracted, WSN transmits it's to base station (B.S). Since the main objective of WSN to collect event features and transmit data through wireless channel, sensor nodes are much more vulnerable to attacks while transmitting data wirelessly. Hence a secure channel must be established between each sensor node and the B.S while transmitting sensor data in WSN. Thus sensor data must be encrypted before transmitting through wireless channel and decrypted at the B.S in WSN.

1877-0509 © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.Org/licenses/by-nc-nd/4.0/).

Peer-review under responsibility of organizing committee of the ICISP2015

doi:10.1016/j.procs.2016.02.082

In WSN, sensor data may have prior knowledge of signal statistics or with unknown signal statistics [3]. Prior knowledge of signal statistics means the sensor systems have the knowledge of variance and covariance of the signal extracted in the environment. This type of system modelling is quite easy to model in WSN. But modelling of signal statistics with unknown variance and covariance of signal is very complex, since sensor data is extracted at real time application in WSN. Thus secure transmission of sensor data at real time application is a challenging task as it is always needed for various confidential operations or many other private data sharing operations in WSN. Transmitting sensor data and keeping that safe from the intruders, cryptography is one of the most approached techniques in WSN. There are two types of techniques used for encryption and decryption - first is symmetric key cryptography and second is asymmetric key cryptography. Encryption may be achieved by two types of ciphering schemes - stream cipher and block cipher as mentioned in [4]. John Von Neumann [5] proposed the concept of cellular automata (CAs).

In the past two decades many area of applications of cellular automata (CAs) is being explored by many researchers as mentioned in [6]-[13], Applications of cellular automata are used in different fields like: - Physics, Chemistry, Mathematics, Biology, Computer Science, Communication and Engineering, etc. Cellular Automata (CAs) have some specific characteristics like- randomness, correlation-immunity, nonlinearity, easy to implement, etc. These characteristics agree with the essential cryptographic properties. In this paper, a new idea is represented in which a CA based cryptosystem is generated for sensor data in WSN. This cryptosystem shows high quality of randomness of the patterns which have similar significances regarding the older computational techniques of cryptography [4] applied in WSN. Further enhancement of quality of randomness can be embedded with the help of using programmable cellular automata (PCA) [6], [7]. The suggested cryptographic technique in this paper uses a single block of 1-D PCA applied in WSN.

The paper is organized as follows. In section 2, we motivate our work using symmetric key algorithm applied in WSN where a cluster of sensor nodes transmits its encrypted raw data to the B.S. In section 3, we describe mathematical model of the proposed algorithm. We validate and conclude our work in section 4 and 5, respectively.

2. Motivation

In symmetric key cryptosystem, only one key is used for the performing encryption and decryption. This key is kept secret between sender and receiver. The sensor data has been encrypted using L2D-CASK in [14] where FPGA were used. In this paper the authors has proposed a scheme that used a key-length of 128 bits. Here in our scheme, a hybrid CA rule vector [6] has been applied on an 8-bit block that uses simple EXOR operations either on 3 bits or on 2 bits of this block. Sensor nodes are basically low computing device with less memory space. Hence our proposed scheme plays more efficient role in terms of memory and computational complexities without compromising with generated cipher text as CA has inherent chaotic sequence generation property [15]. Our scheme gives equivalent encryption efficiency as that of AES [16]. Besides, the scheme is resistant to various cryptanalysis attacks like brute force attack [14], linear cryptanalysis attack and its variants. In [17], the authors have applied Reversible Cellular Automata (RCA) based asymmetric key encryption approach using more than one byte key length whereas in our work we have used symmetric key encryption with one byte block length only at a time because of the memory limitations at sensor nodes. Since, a cluster of sensor nodes transmits its data to CH node, we use a single key at the CH node for encryption and at the B.S for decryption whereas in [17] and the key distribution is much more complex. In [18], cellular automata is used for authentication of each node in WSN.

Cellular automata is an infinite lattice of cells capable of storing one bit at a time. Each cell has the capability to transit into a new state depending on its own state and that of its neighbours [7]. Formally, a cellular automaton is defined as three tuple (S, T, N) where S is the finite and non-empty set of states, T is the finite and non-empty set of transition rules, and N is the non-empty and finite set of neighbourhood cells. The transition of each state is described under some transition rules, some examples of which are shown in Table 1.

Table 1. Example ofnext state transition rule

Rule No 111 110 101 100 Oil 010 001 000

51 0 0 1 1 0 0 1 1

60 0 0 1 1 1 1 0 0

102 0 1 1 0 0 1 1 0

150 1 0 0 1 0 1 1 0

153 1 0 0 1 1 0 0 1

195 1 1 0 0 0 0 1 1

Characteristic matrix of CA is denoted by T [4]. It contains rules for every cell. It is a matrix of order n*n (for n cells), formed based on the next state transition rule of each cell. Thej-th row denotes a rule applicable for thej-th cell. If a cell's next state is dependent on a specific cell, then its position is represented as '1' in the matrix T, otherwise it is represented as '0'. Mathematically, the next state transition can be represented as follows:

[ZM(x)] = [T]x[Zt (x)] (1)

where Zt+1(x) is the state of cell i at t+1 timestamp and Zt(x) is the state of cell i at timestamp t. The rules can be represented as follows:

Rule 60: ZM(x) = Zt(x)©Zt(x-1) (2)

Rule 150: ZtJx) = Z(x-1) ®Zt(x)®Zt (x+1) (3)

A total of 256 such rules of can be formed for a one dimensional, 3-neighborhood cellular automata with radius r = 1 [12]. The CAs can be of many types viz. additive, non-additive, periodic boundary, null boundary, programmable CA, Group CA etc. as reported in [19].

A cellular automaton is called a group cellular automaton if it generates the initial configuration again after a certain number of repetitions by using a specific rule vector. Mathematically,

\l]" = I ( I is the identity matrix) (4)

[Zt„(x)]=[T]" x[zt(x)] (5)

where n is the order of the group [4].

There is total of 256 such rule combinations [12] where 12 is the order of the group. We have used the combination <11110000> where '1' denotes rule 150 and '0' denotes rule 60 for the proposed scheme. The order of the group is 12 for this combination.

3. System Model

In a wireless network scenario, let W set of sensor nodes are deployed randomly. Once an event (e.g fire) has occurred, out of W nodes, M set of sensor nodes woke up and rest of the nodes are in sleeping mode in WSN. A set of M sensor nodes are assumed to be a single cluster where a Cluster Head [20] node is selected random in the network. Cluster Head node collects the data from each node in the cluster and transmits data toB.Sin the network.

Each sensor node i within M, collects observed data ui from sensed data si under noisy environment given by

ui = si + ni (6) where node i extracts the observed data ui under Additive White Gaussian Noise (AWGN) [2] channel. Once each sensor node extract the observed data ui, it transmits ui to Cluster Head node of the cluster at each time stamp t. The Cluster Head node stores the observed data ui in a matrix U. U is a matrix where the observed data ui is stored as a block of sensed data under a given time interval t given by

where N is a block of data extracted by each sensor node i under time stamp t. Once this matrix U is obtained, a noise is added to each value UiJ. Then it is encrypted using group cellular automata rule vector. The non-complemented rules used are rule 150 and rule 60. Logical expression for both the rules are described by equations (3) and (2) respectively. These two rules are used for each 8-bit binary number obtained from the matrix U in the following fashion:

where '1' denotes rule 150 and '0' denotes rule 60. Here null boundary concept is considered, i.e., the neighbour of the extreme cells are considered having the value 0. This group cellular automata has order 12, i.e., they regenerates the initial configuration after 12 iterations. This concept is used for encryption and decryption. What has been done is each element Uj of the matrix U as represented in equation (7) is converted into a 8-bit binary number and the rule vector is applied for 6 iterations to generate encrypted 8-bit binary value. Then, each encrypted binary number is again converted into decimal to obtain the encrypted matrix Ue. This Ue is then sent to the B.S. from cluster head. At the receiving side, i.e., at B.S. the matrix Ueis received and again each value is converted into 8-bit binary number to apply CA rule vector for 6 more iterations to obtain decrypted binary number of the original matrix. Each of these binary number has again been converted into decimal to obtain the original matrix, U collected at cluster head from the sensor nodes ofthe cluster. Detailed algorithm for encryption and decryption is described below.

Encryption Algorithm

Input: Observed matrix, U (after applying equation (6) for each value) from Cluster Head.

Output: Encrypted matrix, Ue.

Step 1. Get ceiling value C(i,j) foru^ ofU.

Step 2. Repeat step 1 for each value ofU.

Step 3. Convert C(i,j) into 8-bit binary bit number.

Step 4. For first 4 bits, apply rule 150 and for rest 4 bits, apply rule 60 using null boundary CA principle [4].

Step 5. Store the result into a new matrix Ue in decimal form.

Step 6. Repeat steps 2to5 for each values ofU.

Step 7. Send the matrix Ue containing encrypted values to B.S.

Decryption Algorithm

Input: Encrypted matrix, Ue from B. S Output: Decrypted matrix, U.

Step 1. Convert into binary 8-bit number each values of Ue.

Step 2. For first 4 bits, apply rule 150 and for rest 4 bits, apply rule 60 using null boundary CA principle [4].

Step 3. Store the result into a new matrix Ud-

Step 4. Repeat steps 2 and 3 for each values ofUe.

Step 5. Output the matrix Ud containing decrypted values equal to U.

4. Result and Analysis

< 1 1 1 1 0000>

The input to the algorithm is a matrix U of dimension 25 x 8, where M=25 sensor nodes are used with each sensor

node generate #=8 observations at each time instant. Thus, the total sensor data is divided into blocks of 25 x 8 matrix U transmitted from Cluster Head node to the B.S at each timestamp. The algorithm used is easy to implement and is equivalent to AES and DES as described in [16]. Besides, this algorithm satisfies randomness property required for any cryptosystem [16]. There are total 28 CA rules, out of which only two rules are used. There are 28 combinations of these two rules in an 8-bit binary number. Hence, the intruder needs to test 2568 combinations which is computationally infeasible in case of brute-force attack [13]. The original data and encrypted data is shown in Table 2 and Table 3.

Table 2. Original matrix, U after adding noise

Table 3. Encrypted matrix, Ue

19 19 19 18 18 17 18 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 17 19

19 19 19 18 18 17 18 19

19 19 19 18 18 17 18 19

19 19 19 18 18 17 18 19

19 19 19 18 18 18 18 19

19 19 19 18 18 17 18 19

19 19 19 18 18 17 18 19

19 19 19 18 18 17 18 19

19 19 19 18 123 17 18 19

19 19 19 18 18 17 19 19

19 19 19 18 18 17 19 19

19 19 19 18 18 17 19 19

19 19 19 18 18 17 19 19

19 19 19 18 18 17 19 19

255 255 255 186 186 85 186 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 85 255

255 255 255 186 186 85 186 255

255 255 255 186 186 85 186 255

255 255 255 186 186 85 186 255

255 255 255 186 186 186 186 255

255 255 255 186 186 85 186 255

255 255 255 186 186 85 186 255

255 255 255 186 186 85 186 255

255 255 255 186 151 85 186 255

255 255 255 186 186 85 255 255

255 255 255 186 186 85 255 255

255 255 255 186 186 85 255 255

255 255 255 186 186 85 255 255

255 255 255 186 186 85 255 255

The transitions of each bit of the 8-bit binary number is shown in Figure 2(a) and 2(b). Here neighbours of the extreme cells are considered as '0'.

150 150 150 150 SO SO SO 60

0 1 0 0 0 0 1 1 {

11 10 0 10 1

0 10 11111

1 1 0 O 0 0 O 1

0 0 1 0 0 0 1 1

0 1110 10 1

150 150 150 150 60 60 60 60

1 1 0 1111

1 0 1 0 0 0 0 1

1 0 1 1 0 0 1 1

1 0 0 0 0 1 0 1

1 1 0 0 1 1 1 1

0 0 1 1 0 0 0 1

0 1 0 0 0 1 1

Fig. 1. (a) Transitions in first six iterations; (b) Transitions in next six iterations

5. Conclusions and Future Work

In a wireless network scenario, the sensor data are encrypted at the Cluster Head node of a cluster using a -symmetric key block cipher encryption technique and transmits the encrypted data to the B.S. The proposed methodology shows privacy of real time sensor data within a cluster of WSN. The proposed methodology shows better performances in terms of implementation complexity and generation of cipher text and is resistant to brute-force attacks and linear cryptanalysis attacks. It also reduces the memory space in Cluster Head node of a cluster in WSN. This work might be extended in distributed clustering algorithms where each cluster encrypts the sensor data with different keys at different timestamp and decrypts data at the B.S. accordingly.

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