A Genetic Based Non-Invertible Cryptographic Key

ISSN:2229-6093 Manisha Mehta et al, Int. J. Comp. Tech. Appl., Vol 2 (6), 3019-3022

A Genetic Based Non-Invertible Cryptographic Key Generation From Cancelable Biometric in MANET

Manisha Mehta, Hiteishi Diwanji, Jagdish S Shah Computer Engineering Department, L. D. College of Engineering, GTU, Ahmedabad [email protected]

Abstract Mission critical applications uses mobile ad hoc network. Security is required to protect this data while transmitting in network. Biometric characteristic such as face, fingerprint, voice, iris, and retina can be potential alternative to generate a cryptographic key and enhance the security. In this paper non-invertible key generation using cancelable fingerprint is discussed. From the receivers fingerprint minutiae are generated. Applying one way transformation cancelable template is generated and stored into vectors. After shuffling vector data, a genetic operator two point crossover is applied and 256 bit key is generated for the encryption AES-256 algorithm. Authentication is done by watermarked face of the sender. The proposed model is analysed against security attributes authentication, confidentiality, integrity. It is also analyse for brute force attack. It is found that it survive against all attack.

Keywords: MANET, Fingerprint Minutiae, Crossover Operator, Authentication, Security 1. Introduction Mobile Ad-Hoc Network (MANET) is a wireless network paradigm. Any mission critical applications uses MANET but required to secure data transmission. Dynamic changing topologies, open medium, cooperative algorithms, lack of centralize monitoring are the features of MANET and it makes it vulnerable to security attack. Implementation if hard-cryptographic algorithms are interesting and challenging against the security attacks. So in this paper we present the approach to generate a genetic based non-invertible cryptographic key from the cancelable fingerprint minutia template Proposed model is discussed in section II to generate a cryptographic key from the cancelable fingerprint

IJCTA | NOV-DEC 2011 Available [email protected]

minutia template. We analyze the various network security attributes in section III. We discuss implementation and result analysis in section IV and finally we conclude in section 5.

2. Proposed Model This paper proposed cryptographic key generation from the cancelable fingerprint. Firstly, minutiae are extracted from the fingerprint and then apply the one way transform function to obtain transformed point. In next point this transformed minutia are used to generate the cryptographic key. To randomize the key two point crossover genetic operator is applied. The main purpose of the proposed system is to enhance the data security in MANET.

2.1 Overall Process for the Proposed System In this approach we assumed that face images and finger print images of group members are stored in the database. The minutiae points are generated from the receivers finger print and transformed into cancelable template. After shuffling cancelable template, two points cross over genetic point is applied and noninvertible key is generated as shown in figure-1. The sender’s watermarked face image attached to data and encrypted using generated cryptographic key. At the receiver side reverse process takes place. Receiver’s fingerprint is used for the decryption. The senders face is extracted and watermarked image checked for the authentication.

2.2 Minutiae Extraction from Fingerprints Everyone have unique, immutable fingerprints [1]. A fingerprint is made of a series of ridges and furrows on the surface of the finger. Pattern of ridges and furrows as well as minutiae can be used to determine the uniqueness of fingerprint. Minutiae points are local ridge characteristics that occur at either a ridge bifurcation or a ridge ending. Fingerprint is

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preprocessed to remove the noise and irrelevant information. This is implemented using Mat lab. Preprocessing consist of the following steps.

one-value neighbor, then the central pixel is a termination (figure-2), it is marked. If the central is 1 and has 3 one-value neighbor, then the central pixel is a bifurcation (ridge branch) as shown in figure-3, it is also marked. At this point the average inter-ridge width D is estimated.

Figure:2 Termination detection mask

Figure-3: Bifurcation detection mask Spurious minutiae is removed if the distance between a termination and a bifurcation is smaller than D, remove this minutiae. If the distance between two bifurcations is smaller than D, remove this minutia process. If the distance between two terminations is smaller than D, remove this minutiae. Figure: 1 Process Diagram Image Normalization is a process to improve the quality of image by eliminating noisy and correcting it by changes the range of pixel intensity values. Here it is performed to remove the gray-level background and effect of sensor noise. Binarization is to convert the gray scale image in binary image, so that the intensity of the image has only two values: black, representing the ridges are highlighted with black color and furrows are highlighted with white color. Here we have used global threshold value to perform binarize the image. This method transforms a pixel value to 1 if value is larger than intensity value otherwise to 0. Ridge thining is to eliminate the redundant pixels of ridges till the ridges are just one pixel wide. An important property of thinning is the preservation of the connectivity and topology which however can lead to generation of small bifurcation artifacts and consequently to detection of false minutiae. This is done using built in Mat lab function for morphological operations on binary images. Marking of minutiae is done by following procedure. For each 3x3 window, if the central is 1 and has only 1


Region of Interest (ROI) is used to remove the image area without effective ridges and furrows. Then the remaining effective area is sketched out. We used inbuilt mat lab morphological function open and close to achieve this. Once ROI is defined extrema minutiae are suppressed. Finally marked x, y position of minutiae are stored in the file which is further used to transform into cancelable fingerprint.

2.3 Cancelable Fingerprint Generation from Transformed Minutiae Point This section explains the method of transformation of extracted minutiae points into transformed points and generation of cancelable fingerprint. It is generated as explained by N. Lalithamani et. Al. [2]. This is implemented in Mat Lab. The extracted minutia points are represented as, Mp = {Pi } where i=1..n and their equivalent X, Y co-ordinates are represented as Mpi (Xi ,Yi) where i=1..n This x,y co-ordinates are converted and stored as vector Cv=[x1 y1 x2 y2 …. xn yn] Find the next corresponding prime no of each value in vector Cv and store it into vector Pv. Pv=[ x’1 y’1 x’2 y’2 …. x'n y’n ]

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Then the discrete exponential function [3] is used generate the vector PDE. It is applied on individual element of Cv with their corresponding values in Pv. If the discrete exponential value DE DE=2 Cv(i) mod Pv(i); i=1…n is computed prime then value is appended to a vector PDE otherwise the next corresponding prime number is obtained and appended to PDE.

PDE=[Px1 Py1 Px2 Py2 Px3 Py3 Pyn ]

Then values are sorted into a separate array. From this sorted array unique values are taken out. The array is represented as:

….. Pxn

Next step is formation of RP is done by random pair selection form PDE. The indexes for random selection of pairs from PDE are computed by the below mathematical operation. The random pairs selected are removed from PDE and process is repeated until PDE is empty.

and the values obtained are denoted as vector form D where D = [D1 D2 . …. Dn ] These values are sorted further,

The main title (on the first page) should begin 1-3/8 inches (3.49 cm) from the top edge of the page, centered, and in Times 14-point, boldface type. Capitalize the first letter of nouns, pronouns, verbs, adjectives, and adverbs; do not capitalize articles, coordinate conjunctions, or prepositions (unless the title begins with such a word). Leave two 12-point blank lines after the title. Rand() mod (|PDE|- k ) ; where k=0,2,4,6 … | PDE|.

Where unique values are represented as,

So this way created UD referred as the cancelable fingerprint template. Using this UD we generate a noninvertible cryptographic key.

The selected pair are represented as (R1,R2). The pair taken out from the PDE are represented as RP = {(R11,R12), (R21,R22), ……. (Rn1,Rn2)} The pair of values in each pair are selected is prime numbers and represented as (R1,R2). The transformed point vector is denoted as TP={P1, P2, P3, ……….. Pn} where Pi= (Ri1,Ri2) for i=1…n (R1,R2) are prime numbers so resultant no is also prime number and which is almost infeasible to factorize, as described in RSA factoring challenge[4]. The utilization of prime number factoring and discrete exponential guarantees that, obtaining minutiae point co-ordinates from transformed points is extremely complex. Subsequently the distance between each point with respect to each other point is computed. The distance calculation between two points is given by the following equation.


2.4 Genetic Based Non-Invertible Cryptographic Key Generation The above generated UD is divided into two equal which are presented as vectors

Our purpose is to generate 256 bits key. First 128 elements of each vector UD1 and UD2 are stored in PUD1 and PUD2 using following method. All even index position elements of UD1 are stored at odd position of PUD2 and all odd index position elements are stored at even index position. Same way elements of UD2 are stored in PUD1. Finally genetic operator

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two point crossover is applied as shown in figure 4 to PUD1 and PUD2 and combined into CPUD.

Figure-4: Two Point Cross Over

Next binary vector NIK is generated using following formula. That is our required key .

This key is used to encrypt the data using AES-256 algorithm. For the authentication watermarked face image is appended to data and it is encrypted using key.

3. SECURITY ANALYSIS Following network security attributes are analysed. Confidentiality: In proposed model we have used one way transformed function to get the cancelable version of fingerprint minutiae. If attacker wants to read the original message, he needs a key. The key can be generated from the fingerprint of the receiver. So it is computationally infeasible to generate the key and confidentiality is maintained. Authentication: In proposed model, group users of ad hoc network can authenticate each other using watermarked face biometric. After decrypting message, receiver can extract to verify the authenticity of legitimate sender after retrieving watermark from the face. Integrity: In proposed mode, the original message is not recovered if it is tempered. By the property of one way transform it is computationally infeasible to modify the cipher text by the attacker. Man-in-the-Middle Attack: In our proposed system, original message is secured using genetic two point crossover, so attacker cannot view the original message from the available cipher text.

required to key generation is 0.04 ms. Size of the fingerprint was of 200x200 pixels. As the key size is 256 bit it resist against all cryptanalytic attack. Brute force attack is possible, but 3×1051 years to exhaust the 256-bit key space for AES-256 bit algorithm. Moreover mobile device has limited capability, less power so it impossible to brute force attack.to generates the key.

5. Conclusion and future work MANET requires high security for data transmission. The proposed model, non-invertible key is generated using cancelable biometric and applying genetic algorithm which is strong against any attack. Authentication is also provided using watermarked image. Further data are secured using AES-256 encryption algorithm. The computation power required is high compare to other small key size algorithm, highest security is provided and it can be easily deploy in mission critical applications like military. This is very basic model. This work can be extended using voice, retina, iris, Eigen faces to implement bimodal or multimodal systems.

6. Acknowledgement I am highly obliged to my guide Prof. Hiteishi Diwanji for her invaluable guidance. I am also thankful to my head of the department Dr. Jagdish S. Shah who gave me opportunity to work under him. I am also thankful to Prof. S. S. Pathan.idance. Lastly I am thankful to my class mates who always stood by me.

7. References [1] S. Pankanti, S. Prabhakar, A.K. Jain, “On the individuality of fingerprints”, IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 24, No. 8, pp.1010–1025, 2002.Bowman, M., Debray, S. K., and Peterson, L. L. 1993. Reasoning about naming systems. [2] N. Lalithamani and K.P. Soman, An Efficient Approach For Non-Invertible Cryptographic Key Generation From Cancelable Fingerprint Biometrics. International conference on Advances in Recent Technologies in communication and Computing, 2009. 978-0-7695-3845-7/09 © 2009 IEEE Pg.47-52 [3] http://en.wikipedia.org/wiki/One-way_function [4] “RSA Factoring Challenge” from http://en.wikipedia.org/wiki/RSA_Factoring_Challenge [5]Neeraj Kumar, Investigations in Brute Force Attack on Cellular Security Based on Des and Aes, IJCEM International Journal of Computational Engineering & Management, Vol. 14, October 2011.

4. Result Analysis The proposed system is implemented using mat lab. The minutia points are generated and transformed into the cancelable version. Then cryptography key is generated from the cancelable version. The time


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