A NEW DNA BASED APPROACH OF GENERATING KEYDEPENDENT SHIFT ROWS TRANSFORMATION

International Journal of Network Security & Its Applications (IJNSA) Vol.7, No.1, January 2015
DOI : 10.5121/ijnsa.2015.7107 79
A NEW DNA BASED APPROACH OF GENERATING KEY-
DEPENDENT SHIFT ROWS TRANSFORMATION
Auday H. Al-Wattar1
, Ramlan Mahmod2
, Zuriati Ahmad Zukarnain3
, and Nur
Izura Udzir4
,
1
Faculty of Computer Science and Information Technology
Universiti Putra Malaysia,
43400 UPM, Serdang, Selangor.
ABSTRACT
The use of key-dependent shiftRows can be considered as one of the applied methods for altering the
quality of a cryptographic algorithm. This article describes one approach for changing the ShiftRows
transformation employed in the algorithm AES. The approach employs methods inspired from DNA
processes and structure which depended on the key while the parameters of the created new ShiftRows have
characteristics identical to those of the original algorithm AES in addition to increase its resistance
against attacks. The proposed new ShiftRows were tested for coefficient correlation for dynamic and static
independence between the input and output. The NIST Test Suite tests were used to test the randomness for
the block cipher that used the new transformation.
KEYWORDS
ShiftRows, Block cipher, DNA, AES, NIST, Coefficient correlation
1.INTRODUCTION
Through the rapid expansion of the Internet and increasing the reliance upon in all areas of life,
the need for a highly effective way to achieve security is fateful and crucial. Cryptography has
been and remains the most efficient approach used to achieve security. Rijndael is a symmetric
key block cipher that was chosen by (NIST) (National Institute of Standards and Technology), [1]
in 2001 as (advanced Encryption Standard, FIPS 197) AES.This encryption is dependent on
substitution-permutation network Shannon (SPN).
Generally, it is based on repeated rounds of transformation that convert input plaintext to
ciphertext (encrypted text) output. Each round includes several procedures and always involves a
relying on the private cryptographic key. Multiple cycles determine inverse transforming
ciphertext into the original, using the same cryptographic key. AES has fixed block size (128-bit),
and a key length is 128, 192 or 256 bits, relating to the number of rounds for the algorithm. It is
runs a byte array called state of 4x4 size in each round of encryption / decryption methods. Most
of the algorithm calculations are achieved in finite fields [2,3].
In AES, the ShiftRows is transformation is one of linear unit of symmetric encryption
algorithms.It is a transposition action where each row of the state cyclically shifted a number of
times. The aim of this function is to scramble the byte order within each 128-bit block, to supply

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diffusion of bits across multiple rounds. This transformation includes shifting the state rows as:
The first row in the state does not move, the second row is circular left shifted by 1 byte, the
Figure 1. AES ShitRows transformation
third row is circular left shifted by 2 bytes, and the last row is circular left shifted by 3 bytes.
Figure 1 shows the AES ShiftRows transformation.
According to [4], AES without ShiftRows stage are more than that of AES, their values
differ very little for different rounds. This is assign of poor encryption quality
The most challenging for AES algorithm is the linear and differential cryptanalysis where,
Rijndael can be issue to standard techniques of differential and linear cryptanalysis.
From the analysis of resistance against differential and linear cryptanalysis, it was found that the
arbitrary unknown and key-dependent substitution and permutation transformations is consider as
a good factor in enhancing the resistance of block cipher against the differential and linear
attacks, since the differential and linear attacks need known transformations. The specific
properties of substitution and permutation functions, specifically the structure that is entirely
dynamic and unknown to the cryptanalyst, aid and support the block cipher to be resistance
against attacks. For the attacker, differential and linear trail over multiple rounds is consider as a
vital requirement, and in the existence of key-dependent dynamic transformations output
differences rely on additional key used. In addition for any additional different key values, there
are unrelated differentials over multiple rounds and therefore diverse linear differential trails. As
result it is difficult for the attacker to utilize the current linear and differential techniques of
cryptanalysis.
Although all previous works on enhancing the security of AES block cipher against attacks, no
one has proposed or suggested a ShiftRows that is designed or created using DNA structure and
bio-inspired techniques.
This paper proposed a novel technique for gaining a powerful key-dependent ShiftRows based on
operations that have been inspired from really biological DNA structure and processes.
Subsequently, it tested the new ShiftRows using the correlation coefficient test, the NIST
randomness tests for the cryptosystem that used the DNA-based ShifRows and finally performed
a cryptanalysis for the proposed transformation.
2. DNA BACKGROUND
DNA (Deoxyribo Nucleic Acid) is a molecule that represents the genetic material for all living
organisms. It is the information carrier of all life forms, and considered as the genetic drawing of
living or existing creatures. DNA contains two such chains, twisted around each other to form a
double-stranded helix with the bases on the inside.
A single-strand of DNA consists of a chain of molecules known as bases, defined as four

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characters {A, C, G, and T}[5,6]. One of the most basic attributes of the DNA strand series is that
it is oriented; consequently, CGTAGGA is distinct from AGGATGC.
The DNA strands subsist as pairs as (A) associated with (T) and (C) associated with (G) forming
units named base pairs, as shown in Figure 2. The reverse DNA strand represents the opposite of
the strand bases; for example, GCATAA becomes AATACG, while the complement of the
strands can be represented as A¯ T and C¯ G. Accordingly, the reverse complementary of
the strands can be represented in two actions: reverse the basis of the strands
Figure 2. DNA bases reverse complement technique
and the second complements the resulting bases, such that (A) becomes (T) and (C) becomes (G)
and vice versa. Therefore, GCATAA will become TTATGC [7]. The reverse complement of the
DNA strand bases is one of the main methods that characterize the biology DNA system. The
reverse complement method is composed of two stages. The first stage is the reverse which
means reverse the order of the DNA bases since the DNA strands of DNA sequences which hold
the bases are oriented such that they have two opposite sides known as 3’ end and 5’ end [8] .All
the processes that take place over DNA occurs from the 3' end to the 5' end, and verse versa,
accordingly, TTCA is distinct from ACTT. The second stage is the complement which is
comparable to the complement operation in binary code but within the letters rather than digits
Thus the reverse complement of AGCTTGAC is GTCAAGCT[9]. In this paper, the reverse
complement process will be used as inspiration for the new key-dependent ShiftRows
transformation.
3. THE PROPOSED METHOD
In this paper the proposed ShiftRows transformation will refer to as a key-dependent dynamic
transformation ( ).
The process is inspired by DNA-strands structures including its nature, orientation as
well as DNA-bases and reverse-complement process.
In this transformation the cipher key Kr at round r is used as key for applying the reverse-
complement over state bytes (byte level). This technique makes the byte transposition process
dynamic rather than static, depending on the Kr. Four specific bytes of Kr will be used to obtain
four values ranging between 0 and 3, in such a way that each single key value (N1, N4) is
obtained by one byte. According to key value the number of bytes that form the DNA-base will
be specified. Note that in this transformation the DNA-bases will be represented by a variable
number of bytes instead of two bits as previously. The key value (N1, N1) would specify the
number of bytes that form one DNA-base. The state STE is considered as four DNA-strands such
that each row forms one DNA-strand, but the number of DNA-bases that form each DNA-strand
differs depending on key value.
Every key (N1, N4) has four values, where each value represents the number of bytes for single
STE row (DNA-strand) that can be used to represent a single DNA base. The cases of key are
shown in Table 1,these values of the key will be used to specify the mechanism of byte level

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Table 1: Transposition key values
reverse-complement of state rows, where a, b, c and, d are 4 bytes, representing a single row of
stat STE; note that a single row represents a single DNA-strand. Every DNA-base can be
represented by one, two or four bytes, according to the key values and after the representing is
done, the reverse-complement method is performed over the resulting DNA-bases. If every DNA-
base is formed by one byte (key value = 0) then the reverse complement will be achieved on the
state row in byte by byte level; however if one DNA-base is formed by two bytes (key value = 2)
then the reverse complement will be done on state row as every two bytes, Finally, if every DNA-
base is formed using four bytes (key value = 3) then the reverse-complement will be achieved on
the whole row as one part. There is only one case (key value =1) where there is no reverse but,
just complement for the state row. Note that each row has a unique key value as illustrated in
Figure 3
Figure 3. The state row transposition process depending on key
The whole method for the at round r using the key cipher Kr according to the process
described in Figure 3 is illustrated in Figure 4.

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Figure 4. Key-dependent transposition inspired by DNA processes
4. TEST AND RESULT
4.1 Correlation coefficient
The correlation coefficient assumes values ranging between -1 and +1. According to [10] and
[11] the subsequent values are in an agreeable range for explicating the correlation coefficient as
declared in Table 2.Note that this paper considers the two variables of plaintext (p) and ciphertext
(c)
Table 2. Accepted range values for interpreting the correlation coefficient
The following equation describes the use of the correlation coefficients functions:
where: s is the total number of bits , , are the series of s measurements for p and c, p is bits
value of original bits or plaintext, c is bits value of change bits or ciphertext , E( c) is mathematic
expectation of c.

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The variance of p can be represented within this equation:
Finally, the related coefficients can be described in this equation:
With a view to test the coefficient of block cipher, the experiment was separated into two
different types of testing which are:
Test on permutation transformation only.
Tests on all transformations of whole block cipher which used the proposed permutation
transformation.
4.1.1 Correlation coefficient on permutation transformation
To evaluate the diffusion property of the block cipher byte permutation, the experiment was
examined transformation in the AES block cipher. Figure 6 illustrates the laboratory
experiment process of correlation coefficient of the transformation in the AES block
cipher.
Figure 6. Laboratory experiment process of correlation coefficient function.
The analysis on the data from Table 3 is accomplished, in which the correlation value for the
plaintext before and after the function in the block cipher denoted as newT are recorded.

85
A scatter chart of the results is presented in Figure 7. It shows that there is no sequences from the
function in AES block recorded the correlation values between 0.7 and 1.0 which
indicates a weak positive (or negative) linear relationship. It also reported that 42 of 128
sequences from the function in AES block cipher recorded the correlation values between
0.3 and 0.7, which indicates that (32%) percentage of correlation has a moderate non-linear
relationship of the AES block cipher for transformation.
Table 3. Correlation test, r_xy of byte permutation function for sequence number 1- 128

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Figure 7. Scatter chart of the correlation test results on function only
From the result analysis, it can deduce that this function has a good confusion performance
between the plaintext and the ciphertext. This infers that it has a good security.
4.1.2 Correlation coefficient on all transformations
The experiment examined all functions of the block cipher. The analyses of the data, in which
correlation value for round 1,2 and 3 for each sequence is recorded.
Scatter chart of the results is presented in Figure 8. It illustrates that the majority of correlation
values, at different rounds through the whole block cipher are near to 0, which indicates a strong
Figure 8.Scatter chart of the correlation test results on whole block cipher
positive (or negative) non-linear relationship. Only 0.007% is near to +1 or -1, in round 1,
representing a weak positive (or negative) non-linear relationship. From the results, it can be
inferred that the block cipher has an improved confusion performance.

87
4.2 NIST Suite Randomness test
The randomness test is one of the security analysis to measure the confusion and the diffusion
properties of the new encryption algorithm , as carried out in[12 – 14][15] and[16].
NIST Suite [17] is a statistical test suite for randomness by NIST used to assess the cryptography
algorithm. The suite test assesses whether the outputs of the algorithms under certain test
condition exhibits properties that would be expected randomly generated outputs.
In order to evaluate the randomness of ciphertext, an experiment that included a set of data as
random plaintext and random 16 byte key in the ECB mode was conducted. The 128 sequences of
1,059,584 bits were constructed and examined.
The proportion of sequences that passed a specific statistical test should lie above the proportion
value p, defined in Equation (4).
as:
where ∝ is the significant value, n is the number of tested sequences.
For this experiment, the proportion value is:
where n= 128, and ∝= 0.01.
The list of statistical tests applied throughout the experiments is illustrated in Table 4.
Table 4. Breakdown of 15 statistical tests applied during experimentation

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The p-value readings for each round constructed is illustrated in Figure 9. This figure
demonstrates the randomness test for 15 statistical tests of block cipher that used the proposed
ShiftRows for the rounds 2 and 3, respectively. From this figure, at the end of the second and
third rounds, all of the 41 statistical tests fall over 96.3616%, which is evidence that the output
from the algorithm is completely random.
4.3 CRYPTANALYSIS
The new permutation transformation is dynamic and its changing at each round according to
round key-value this feature make the job more hard for the attackers since the analysis of
dynamic unit is more difficult than the static one. For the dynamic ShiftRows the attacker have
the possibility of which consider a high number, that increasing the resistance of the
algorithm against the attacks.
5. CONCLUSION
In this a new dynamic key-dependent permutation transformation was developed with chosen
byte from key and existing AES state. The new ShiftRows transformation is not fixed, but
changeable at each round according to the round key values. This permutation unit was tested
with the correlation coefficient and the 15 statistical randomness tests of NIST Test Suite.
Analyzing the results demonstrates that the characteristics of the new ShiftRows are more secure
and hard the job for attackers, on which concluded that it is potential to employ it for an
encryption. This will increases the stability of AES against linear and differential cryptanalysis.
An algorithm for designing this permutation transformation is proposed, as it is based on an
inspiration of biology DNA operations.
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Authors
Auday H.Al-Wattar obtained his B.Sc. degree in Computer Science from Mosul University
and his M.Sc. Degree from Technology University-Baghdad in 2005 .He presently pursing
his Ph.D. From Universiti Putra Malaysia, Malaysia under the guidance of Prof. Dr. Ramaln
bin Mahmod at Computer Science and Information Technology Faculty. He works as lecturer
at Mosul University (since 2005), at Computer Science and Mathematics Faculty - Computer Science
Department. His area of interest includes
Ramlan B Mahmod obtained his B.Sc. in computer Science, from Western Michigan,
University, U.S.A. in 1983, and M.Sc. degree in Computer science, from Central Michigan,
University, U.S.A. and Ph.D. degree in Artificial Intelligence from, Bradford University,
UK in 1994. His previous workings Experience/Position are as: System Analyst,
PETRONAS, 1979-1980. Lecturer, Mathematic Department, Faculty of Science, UPM,
1985-1994. Lecturer, Department of Multimedia, Faculty of Computer Science & Information
Technology, UPM, 1994 – 2002. Associate Professor, Department of Multimedia, Faculty of Computer
Science & Information Technology, UPM, 2002 – 2010. Deputy Dean, Faculty of Computer Science &
Information Technology, UPM, 1998 – Nov 2006. Dean, Faculty of Computer Science & Information
Technology, UPM, 2010-2013. Professor, Faculty of Computer Science & Information Technology, UPM,
2012 - now. His research interest includes Neural Network, Artificial Intelligence, Computer Security and
Image Processing.
Nur Izura binti Udzir obtained her B.Sc. in computer Science, from Universiti Pertanian
Malaysia, in 1995, and M.Sc. degree in Computer science, from Universiti Putra Malaysia,
in 1998, and Ph.D. degree in Computer Science from, University of York, UK in
2006.Associate Professor, Head Department of Computer Science, Faculty of Computer
Science & Information Technology, UPM. Her research interest includes Computer
security, secure operating systems, Access control, Distributed systems, Intrusion detection systems.
Zuriati Binti Ahmad Zukarnain obtained her B.Sc. in Physics,, from Universiti Putra
Malaysia, in 1997, and M.Sc. degree in Information Technology, from Universiti Putra
Malaysia, in 2000, and Ph.D. degree in Quantum Computation and Quantum Information
from, University of Bradford, UK, 2006,UK in 2006. Associate Professor, Department Of
Communication Technology and Networking, Faculty of Computer Science & Information
Technology, UPM. Her research interest includes information, computer and communication technology
(ICT), Quantum Information Systems and Distributed Systems, Quantum Computing, Computer Networks
and Distributed Computing

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