Enhanced Authentication Mechanism in WLAN via ...

INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY EDUCATIONAL RESEARCH ISSN : 2277-7881; IMPACT FACTOR - 2.972; IC VALUE:5.16 VOLUME 3, ISSUE 4(7), APRIL 2014

Enhanced Authentication Mechanism in WLAN via MMBSPS Avala Ramesh1

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Dept. of Computer Sc. & Systems Engg. Andhra University College of Engineering, Andhra University, Visakhapatnam, India

Dept. of Computer Sc. & Systems Engg. Andhra University College of Engineering, Andhra University, Visakhapatnam, India 1

The ability to provide a Quality of Service (QoS) is one of the challenging aspects of any Wireless Network. This paper concentrates in improvising the speedy authentication mechanism in Wireless Local Area Network (WLAN). To fulfill the specified important issue, this work introduces a novel Multi Merged Bio-Cryptographic Security-Aware Packet Scheduling (MMBSPS) algorithm. In merging the different biometric images, it is commenced with the new merging mechanism called Triple Equally Segmented BioImage (TESB) algorithm and later it is encrypted with the RSA algorithm for efficient security. Matlab tool is used for conducting the simulations on Multi Merged Bio-Images (MMBI) and Bio-Images. The results of MMBSPS algorithm is presented in contrast with the EMBSPS and EBSPS algorithms. In the results, it is observed that, MMBSPS algorithm is working better than existing EMBSPS and EBSPS algorithms with respect to the speedy authentication besides assuring security in WLAN. It is also observed the overall performance of MMBSPS is improved by approximately 23% in terms of authentication mechanism in WLAN.

General Terms System Security and Wireless communications

Keywords Bio-Cryptography, Quality-of-Security, Biometrics, Security Level, Multi Merged Bio-cryptic Security- Aware Packet Scheduling-Algorithm, Bio-cryptic Security-Aware Packet Scheduling-Algorithm.

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[email protected]

ABSTRACT

INTRODUCTION

The security of Wireless local area network has come a long way since the early days. Wireless networks are less sensitive to the security attacks. There are enormous security attacks that happened on the WLAN like Eaves dropping, Unauthorized Access, Man-In-The-Middle attacks, and so on are some of the examples [1]. According to the recent survey report on WiFi Adoption and Security Survey 2012 conducted by Hong Kong Wireless Technology Industry Association, the flourishing of Smartphone market is evident in the result that out of 283 respondents, 276 were Smartphone users. iOS Smartphone stands as market leader with 40 percent users. This is followed by Android Smartphone with 38 percent of

S. Pallam Setty2

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[email protected]

respondents. 55 percent shared their Smartphone as Wi-Fi tethering or Wi-Fi Hotspots. Whereas, 10 percent of users use shared connections for security and 60 percent respondents WPA Personal (60 percent). Also it seemed that 29 percent uses WPA2 which was fully transitioned. Moreover encryption algorithm is concerned, 76 percent of users has opted for WEP at home. A few proportion of people are aware of their usage 11 percent by AES and 18 percent uses TKIP [2]. The preceding survey clearly states that, the necessity of security in WLAN. In the recent research work to strengthen the security in WLAN, the security level were designed with the incorporation of Bio-Cryptic authentication [3][4][5][6][7]. Bio-Cryptography is branch of science that combines the biometric and cryptography for secure authentication by the users. It inherits the best properties of both and provides a strong means of security against biometric system attacks [8][9]. Even the strong authentication mechanism can be provided by means of security levels with respect to Bio-cryptography, but yet Quality-of-Service (QoS) in WLAN is a big challenge for scholar and scientists. QoS represents in terms of bandwidth data rate, and security. In previous work, we have reduced the authentication credential data size by introducing the novel Enhanced Merged Bio-cryptic Security- Aware Packet Scheduling-Algorithm resulted in the speedy authentication. The details were discussed in the next section 2. The important contributions of this work consist of: (1) a requirement and survey of a multi level merging of biometric images for wireless LAN according to security levels; (2) a novel Enhanced Multi Merged bio-cryptic security-aware packet scheduling; (4) a new performance analysis of speedy authentication and performance; (5) a simulator where the MMBSPS algorithm is implemented and evaluated. The reminder of the paper is structured as follows. Section 2 describes preceding works in the area of Security Levels, BioCryptography and Advanced Radius authentication server. Section 3 discusses the novel MMBSPS algorithm, system model and architecture. In section 4, it is represented with the performance analysis of our MMBSPS. Lastly, the paper is concluded with future work discussed in Section 5.

2.

RELATED WORKS

A survey report was presented by Li Bin and team on diverse edge detection operators like Canny, Roberts, prewitt and sobel. In authors work it states that Canny operator detects weak edges better [10]. Gaochang Zhao et.al, explained the image encryption system using RSA algorithm [11]. In the preceding work of ours, it is analysed that, canny edge gives stronger security than Laplacian and Zero cross [27].

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Sulakshana Bhariya et.al, described the implementation of the bio-cryptography encryption and decryption process and discussed the Image security improvements [12]. Xiao Qin et.al has introduced the importance and necessity of having different security levels in WLAN. Their work discusses that, common security is not applicable for the data which is confidential and need high security. They resolved this problem by introducing the different levels of security through Security aware packet scheduling (SPSS) algorithm. But there are some flaws like not clear with the security levels and how to assign security which will reflect on Load on the Network Switch (LNS) [3]. To make security level assignment to the specific WN user, Rajesh Duvvuru et.al, has came up with a solution via ASPS algorithm. In this algorithm they have designed a novel authentication server called Advanced Radius authentication server (ARAS). This ARAS played a major role in assigning the security to the specific level of security with help of WN IP address. Here the ARAS will check the IP address and assign security level to the specific WN. Due to automatic assignment of security, misuse of security level can be prevented and also LNS is also reduced abruptly [4]. In continuation with the preceding work, once again Rajesh Duvvuru and team to strengthen the security in WLAN had brought up with a Bio-crypted Security-Aware Packet Scheduling-Algorithm (BSPS) to strengthen the security in WLAN. In accordance with the BSPS algorithm they made the authentication mechanism in WLAN stronger [5]. BSPS comprises of encrypted text password and cryptic biometrics like thumb and iris. But the BSPS contains only three level securities, which is not sufficient for confidential user. Later to the preceding work, Rajesh Duvvuru et.al has improvised the security level by introducing the Enhanced Bio-crypted Security-Aware Packet Scheduling-Algorithm (EBSPS). EBSPS designed for five levels of security and contains cryptic based password, thumb, iris, palm and facial biometric images. In EBSPS they have added two more security level in the form of palm print and facial [6]. Next to that, we found that, BSPS and EBSPS are performing well at strong authentication process. But due to requesting packet authentication packet overhead, which leads to the slow process of authentication. In our preceding work, we have proposed a solution through Enhanced Merged Biocryptic Security-Aware Packet Scheduling-Algorithm for speedy authentication process. In that work, we reduced the load of authenticated packet by using equally half merged biometric images. After encrypting the biometric images, we observed overall 20% improvement in the size reduction compared to the EBSPS algorithm [7]. Presently, the system is designed for the improvement in fast authentication by using MMBSPS.

3. MULTI MERGED BIOCRYPTOGRAPHIC SECURITY-AWARE PACKET SCHEDULING ALGORITHM (MMBSPS) 3.1 Assumptions and Notations It is assumed with a set of security levels in a network for MMBSPS and also it is assumed five security levels.

MMBSPS fallowed EMBSPS and EBSPS in fixing up security levels as five. In addition to this, we also inherited complete structure and properties of Request IP address (RqIA) packets, Response Authentication (RsA) packets and Response authentication status [7]. But, we replaced Request Authentication Packets (RqA) with Enhanced Request Authentication (ERqA) Packets.

3.1.1

Enhanced Request Authentication (ERqA) Packets

Once the WN receives the RsA packets from ARAS then ERqA starts working. ERqA packets are newly assumed and it is used instead of RqA packets. ERqA packets are of five different types. They are (1) Enhanced Request Authentication packet at security level 1 (ERsAV1) (2) Enhanced Request Authentication packet at security level 2 (ERsAV2) (3) Enhanced Request Authentication packet at security level 3 (ERsAV3) (4) Enhanced Request Authentication packet at security level 4 (ERsAV4) (5) Enhanced Request Authentication packet at security level 5 (ERsAV5). The details are as follows:  







ERqA1 comprises a set of three fields (1, Password, ARASIP). 1 specifies security level 1 and cryptic password. ERqA2 contains a tuple of four fields (2, Password, thumb, ARASIP). 2 specifies security level 2 and also contains a cryptic password and Bio-cryptic (thumb image). ERqA3 is a tuple of four fields (3, password, Merge {thumb, iris}, ARASIP). 3 specifies security level 3 and it comprises of a textual Cryptic password and added with Multi Merge Bio-Cryptic image (thumb and iris). ERqA4 is a tuple of four fields (4,pass, merge{Thumb, Iris, Palm print}, ARASIP). 4 specifies security level 4 and it comprises of a textual cryptic password and includes with Multi Merge Bio-Cryptic image (Thumb, Iris and Palm print). ERqA5 is a set of four fields (5, pass, Merge { Thumb, Iris, Palm print, Face}, ARASIP).5 specifies security level 5 and it includes of a textual password and comprises of Multi Merge BioCryptic image (Thumb, Iris, Palm print and Face).

ARASIP represents the address of Advanced Radius Authentication Server. This is a common field in the ERqA packets.

3.2 The Packet Model The Wireless data packet (WDP) model was adopted from the literature. The WDP comprises with a set of fields (ATi,PTi,SLi,Di)[3]. Here, ATi and PTi represents arrival time and processing time of packet i and SLi and Di is denoted as security level and deadline of the packet i Multi Merged Bio-Cryptic Computation time at WN(CTWi) can be defined as: CTMMWi = TEDi +TMMRSAEi

---- (1)

Where TED is computation time for edge detection of packet i and TMMRSAE is computation for Multi Merged Bio-

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Cryptic RSA encryption at WN of packet i. Equation no. 2 represents Computation Time of Multi Merged Bio-Cryptic at ARAS (CTARAS) is calculated as:

ELSE IF with RsAV2; ELSE IF with RsAV3; ELSE IF with RsAV3;

CTMMARASi = TMMRSADi +TPMi

--- (2)

Where, TMMARSAD is Multi Merged Bio-Cryptic computation time to decrypt the biometric samples at ARAS of packet i and TPM represents computation time for pattern matching in the database. Then TPM is assumed as with the Radom probability distribution and computation time of TMMARASD is similar to TED. Total Computation Time of Multi Merged Bio-Cryptic (TAT), can be acquired through eq 3. TATi = CTMMWi + CTMMARASi +AD

--- (3)

Where, AD is the authentication delay in the network. The AD was assumed using Radom probability distribution function.

ELSE IF with RsAV4; ELSE IF with RsAV5; Step 4: WN Receives RsAV packet from ARAS . Step 4.1: IF ERqA==1, then Step 4.2.1: Read input from the user as Password. Step 4.1.2: „TEXT PASSWORD‟ will be encrypted using RSA Algorithm and ARASIP Address is incorporated and WN Send ERqA1 Packet to ARAS Step 4.1.3: GOTO step 5.

ELSE IF ERqA ==2, then Step 4.2.1: Read input from the user as Password and Thumb. Step 4.2.2: „TEXT PASSWORD‟ will be encrypted using RSA Algorithm GOTO Step 4.2.4. Step 4.2.3: GOTO step 4.2.3.1. Step 4.2.3.1: Triple Vertical Segmentation performed on Thumb Image.

is

Step 4.2.3.2: The middle segment of Thumb Image is considered and feature extraction is applied using CANNY EDGE DETECTION. Step 4.2.3.3: Encryption is performed on Edge detected Thumb image using RSA Algorithm GOTO Step 4.2.4. Step 4.2.4: The results of Step 4.2.1 and Step 4.2.3.3 are combined in addition with ARASIP Address, then WN Send ERqA2 Packet to ARAS. Step 4.2.4: GOTO step 5. ELSE IF ERqA ==3, then Figure 1: Scheme of Network

Step 4.3.1: Read input from the user as Password Thumb and Iris.

3.3 The MMBSPS Algorithm

Step 4.3.2: „TEXT PASSWORD‟ will be encrypted using RSA Algorithm GOTO Step 4.2.4.

As it was specified in the previous section MMBSPS is proposed for reducing delay in the authentication time between WN and ARAS. The rest of the algorithm is as follows: Step 1: Initially the RqIA packet is sent to the ARAS. Step 2: ARAS will check RqIA packet is valid or not through Security Identification Adapter (SIA) [4]. IF valid, GOTO step 4 else step3. Step 3: ARAS will respond to WN with RsANV packet. Step 4: ARAS will respond to WN with RsAV1;

Step 4.3.3: GOTO step 4.3.31. Step 4.3.3.1: Triple Vertical Segmentation is performed on Thumb Image and Iris individually. Step 4.3.3.2: Next the middle portion of Thumb and Iris images are merged together. Step 4.3.3.3: feature extraction is applied on th resultant merged images of Thumb and Iris using CANNY EDGE DETECTION. Step 4.3.3.4: Encryption is performed on Edge detected merged Thumb and Iris images using RSA Algorithm GOTO Step 4.3.4.

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Step 4.3.4: The results of Step 4.3.2 and Step 4.3.3.4 are combined in addition with ARASIP Address, then WN Send ERqA3, then 3 Packet to ARAS. Step 4.3.5: GOTO step 5 ELSE IF ERqA ==4, then Step 4.4.1: Read input from the user as Password Thumb, Iris and Palm print.

Step 5: RsAV packets receives at ARAS. Step 6: Data decryption of ERqAV packet will be done by ARAS. Step 7: The resultant Image will be checked in the database of ARAS. IF ERqAV is valid, RsASG(Response for Authentication: Granted) to WN else RsASD(Response for Authentication: Denied) is issued to WN.

Step 4.4.2: „TEXT PASSWORD‟ will be encrypted using RSA Algorithm GOTO Step 4.2.4.

Step 8: Once RsASG packet receives, then the algorithm fallows SPSS algorithm for guarantee ratio and packet scheduling.

Step 4.4.3: Region_of_Interest (ROI) segmentation algorithm is applied on Palm print Image.

Step 9: Terminate all connections.

Step 4.4.4: GOTO step 4.4.4.1. Step 4.4.4.1: Triple Vertical Segmentation is performed on Thumb Image, Iris and Palm print individually. Step 4.4.4.2: Next the middle portion of Thumb, Iris and Palm print images are merged together. Step 4.4.4.3: feature extraction is applied on the resultant merged images of Thumb, Iris and Palm print using CANNY EDGE DETECTION. Step 4.4.4.4: Encryption is performed on Edge detected merged Thumb, Iris and Palm print images using RSA Algorithm GOTO Step 4.4.5. Step 4.4.5: The results of Step 4.4.2 and Step 4.4.4.4 are combined in addition with ARASIP Address, then WN Send ERqAV4 Packet to ARAS. Step 4.4.6: GOTO step 5 ELSE Step 4.5.1: Read input from the user as Password Thumb, Iris, Palm print and Face. Step 4.5.2: „TEXT PASSWORD‟ will be encrypted using RSA Algorithm GOTO Step 4.2.4. Step 4.5.3: Region_of_Interest segmentation algorithm is applied on Palm print Image. Step 4.5.4: GOTO step 4.4.5.1. Step 4.5.5.1: Triple Vertical Segmentation is performed on Thumb Image, Iris, Palm print and Face individually. Step 4.5.5.2: Next the middle portion of Thumb, Iris Palm prints and Face images are merged together. Step 4.5.5.3: feature extraction is applied on the resultant merged images of Thumb, Iris, Palm print and Face using CANNY EDGE DETECTION. Step 4.5.5.4: Encryption is performed on Edge detected merged Thumb, Iris, Palm print and Face images using RSA Algorithm GOTO Step 4.3.4. Step 4.5.6: The results of Step 4.5.2 and Step 4.5.5.4 are combined in addition with ARASIP Address, then WN Send ERqAV5 Packet to ARAS.

Figure 2: Triple Vertical Segmentation of Thumb print and Iris biometric images and Merging of Middle portion of both images.

4. SIMULATIONS AND RESULTS 4.1 Simulations of MMBSPS using Matlab It was discussed about MMBSPS algorithm in the earlier section. In this work we simulated MMBSPS in three different phases. The very first phase is Triple vertical segmentation and merging of images. Next it is followed by edge detection using Canny operator and RSA Image Encryption. Simulation of each phase was described below. We considered the biometric samples images from the Biometric Research Laboratory, Indian Institute of Technology, Delhi and Facial image data samples were gathered from the University of Massachusetts Amherrest , United States of America [18][19][22][21]. In MMBSPS and EMBSPS algorithm was tested on 22 face Images, 15 finger prints, 54 Iris and 44 Palm prints. Figure explains the complete procedure of MMBSPS at security level 5.

Step 4.5.7: GOTO step 5

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Simulation of Triple Vertical Segmentation (TVS) and Merging of biometric images

In this work, the Biometric images are segmented into three equal portions. The middle portion of every image is considered for edge detection. For instance in figure 2, we performed TPS on Thumb (Size 47 KB) and Iris (Size 225 KB) images. Whereas after extracting the middle portion of both images and we merged both the images. Here, it is observed that the size of the resultant merged image is reduced abruptly to 17 KB. We used the some important function like Imresize(), concatenation and Image(x:y,x1:y1) in Matlab for image segmentation and merging of image. Image Figure 3 represents the different image sizes at different levels. It is also observed that the size merged image is very less that the individual images.

4.1.2

15

225

24

NO

27

17

273

5

15

225

24

27

30

20

291

From the table 1 it is observed that, when size of the image is at SL3 and SL4 are same for the Thumb and the iris. In SL4 an extra biometric option was taken into consideration i,e., Palm print size of 255 KB, due that the size has increased from 255 KB to 273 KB at Bio-crypt (Colum 8). It also observed that, where ever more edges are detected the size of the encryption image increases. For instance observe at SL 2, the thumb image has more edges was detected, due to that; the size of the SL-2 is almost equal to SL-5.

4.2 Result and Analysis of Multi Merged Bio-Cryptography

Edge detection (ED) of Multi Merged Biometric images

In the previous work, we simulated edge detection using Canny operator. Now, the same edge detection technique is applied on the Multi Merged Biometric images. In the earlier discussion, it is observed that, Canny performs better edge detection, where the images highly disturbed or more attenuated to noise. In Matlab we used the Canny ED. i.e., edge(fin_his_eq,'canny'); for clear edge detection [11].

4.1.3

4

RSA Algorithm Encryption on Edge Detected Multi Merged-Biometric images

The literature of biometric encryption, it is possible to enrypt with the Chaos-Based Elliptic curve cryptography (ECC) and RSA algorithm [24] [25] [26]; ECC and Chaos based results better encryption than RSA algorithm biometric encryption. But here we have gone with the popular RSA encryption scheme. In RSA algorithm we considered two distinct prime numbers p and q randomly and then computed the prime numbers p and q for the key generation. [12].Given m, can recover the original message M by reversing the padding scheme. By using Matlab inbuilt function .i.e. cipher(j,k)= mod(M(j,k)^e,n); where M is the edge detected multi merged biometric image. Table 1 defines the data size in KB at every instant of the security level. Here colum1 represents Security level, next it is followed by the Thumb print (column 2) , Iris (column 3), Palm (column 4) and Face (column 5). Whereas column 6 contains the Triple vertical segments (TVS) and Multi merged Biometric image size. Subsequently it go after, Edge detection of Multi merged Biometric size at column 7 and lastly Multi merged Biometric RSA encrypted image size is represented at column 8. This data that was presented in the table is considered average of 20 samples.

The analysis was presented on the basis of the Matlab simulations.

4.2.1

Impact of Merging Images 90 80 70 60 50 40 30 20 10 0

SL

Thumb

Iris

Palm

Face

TPS & Merged

Edge detect

BioCrypt

1

NO

NO

NO

NO

NO

NO

NO

2

47

NO

NO

NO

16

11

294

3

15

225

NO

NO

17

12

255

MMBSPS EMBSPS EBSPS

SL1

SL2

SL3

SL4

SL5

Security Levels

Figure 3: Impact of Merging on MMBSPS, EMBSPS and EBSPS.

4.2.2 Table 1 Data Size (in KB) at every Security Level (SL) of MMBSPS.

Triple Vertical Segmentation Vs Double Equally Segment Biometric Image

The impact of TVS was introduced in the MMBSPS. Whereas equally segmented was used in EM|BSPS. MMBSPS and EMBSPS compared in terms of data size as a parameter. It is observed that, TVS performing approximately 28 % better than EMBSPS .Figure 3 shows very clearly the difference between the EMBSPS and EBSPS.

Size of the Data

4.1.1

Impact of Computation Time (CT)

Computation time of the MMBSPS algorithm was compared with the existing to the EMBSPS and EBSPS. It is observed that, the computation of the MMBSPS is less than the EMBSPS. Figure 4 describes the computation time at each level of security. We also observed there is gradual deterioration in the CT at SL-4 and SL-5. It is observed that, if edges are more, the encryption pixels are also more. MMBSPS brought significant change in the CT.

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CT at WN and ARAS Computation Time

2.5 2

1.5

MMBSPS

1

EMBSPS

0.5

EBSPS

0 SL1

SL2 SL3 SL4 Security Levels

SL5

Figure 4: Computation time at WN between MMBSPS, EMBSPS and EBSPS algorithms.

4.2.3

Impact of Data rate and Data Size

Whereas data rate completely depends upon the data size. If the data size of the packet is larger, the data rate is slower. The data size is small, the data transmission is fast either in wired or wireless communications. The general phenomenon is that the data size is directly proportionate to data transmission in wireless network. The results were discussed in the previous work [5].

4.3 Overall Impact of MMBSPS The overall performance of MMBSPS is good compared to the EMBSPS and EBSPS in terms of authentication delay, besides assuring the security levels. The overall performance is discussed in detail in our preceding work [6]. The major metrics that was considered are of five which are as follows: (1) Guarantee ratio (GR) (2) level of security (LS) (3) Loadon-Switch (LOS) (4) Authentication time (TATi) and (5) Overall performance (OP), and total Overall performance can be designed by following Eq-4: OP= (GR * LS) + LOS + TATi

Firstly we merge the biometric images. Next, mergedbiometric images are submitted for the edge detection using canny edge operator. Lastly we applied the RSA algorithm in the TVS merged images. Which has produced a good amount of data size reduction. The simulations were performed in the Matlab. The data size reduction is more advantageous in speedy authentication process in the WLAN through ARAS. Analysis was made on Matlab results. It is observed that, the proposed MMBSPS performed better than EMBSPS and also EBSPS in fast authentication scheme due to the data packet size reduction. It is observed that the authentication time in wireless network made significant achievement in saving the approximately 23% time. In addition to this, the data size was also reduced it is also observed that improvement in the fast authentication. In future, it is possible to encrypt the image with more strong encryption techniques like ECC or Choas based algorithms. Also, it is having a chance of introducing one more security level.

---- (4)

5. CONCLUSIONS AND FUTURE SCOPE Security of network plays a major role for better Quality of Service in wireless networks. In this work, our results prove that the specified goal is fulfilled with the MMBSPS algorithm. We applied the TVS procedure on the biometric images, and it has shown that a huge reduction has taken place in the size of the images.

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Figure 5: MMBSPS procedure at security level-5

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[14] Li Bin and Mehdi Samiei yeganeh, “Comparison for Image Edge Detection Algorithms,” IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278 - 0661 Volume 2, Issue 6, pp. 01-04, July-Aug. 2012. [15] Sulakshana Bhariya, Guide Jagveer, “A Bio-Cryptography Approach for Improving the Security of Image Encryption and Decryption,” International Journal of Technology, Vol. 2: Issue 1, pp. 01-04, 2012. [16] Bing Wang, ShaoSheng Fan, "An Improved CANNY Edge Detection Algorithm," Second International Workshop on Computer Science and Engineering, 2009,IEEE, iwcse, vol. 1, pp.497-500, 2009 [17] Samoud Ali and Cherif Adnen, “RSA algorithm implementation for ciphering medical imaging,” International Journal of Computer and Electronics Research ,Volume 1, Issue 2, August 2012 . [18] Sankaran, M. Vatsa, and R. Singh, Hierarchical Fusion for Matching Simultaneous Latent Fingerprint, In Proceedings of International Conference on Biometrics: Theory, Applications and Systems, 2012. [19] Ajay Kumar and Arun Passi, "Comparison and combination of iris matchers for reliable personal authentication,” Pattern Recognition, vol. 43, no. 3, pp. 1016-1026, Mar. 2010 [20] Ajay Kumar, Sumit Shekhar, "Personal Identification using Rank-level Fusion," IEEE Trans. Systems, Man, and Cybernetics: Part C, pp. 743-752, vol. 41, no. 5, Sep. 2011 [21] Vidit Jain and Amitabha Mukherjee, "The Indian Face Database",2002,(http://viswww.cs.umass..edu/$\sim$vidit/{I}ndian{F}ace{D}atabase). [22] Kai-Wen Chuang, Chen- Chung Liu, Sheng-Wen Zheng, “A Region-of-Interest Segmentation Algorithm for Palmprint Images,” In Proc. of The 29th Workshop on Combinatorial Mathematics and Computation Theory”, pp. 96-102, April, 2012. [23] Christian Rathgeb and Andreas Uhl, “A survey on biometric cryptosystems and cancelable biometrics”, Journal on Information Security 2011, Springer, pp.1-25, 2011. [24] Zhongjian Zhao and Xiaoqiang Zhang. “ECC-Based Image Encryption Using Code Computing..” Proceedings of the 2012 International Conference on Communication, Electronics and Automation Engineering Advances in Intelligent Systems and Computing, Volume 181, Springer, pp 859-865, 2013 [25] Yaobin Mao and Guanrong Chen. “Chaos-Based Image Encryption.” Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics, Springer, pp 231-265, 2005. [26] Gambi, E. et. Al. “Chaos-Based Radars for Automotive Applications: Theoretical Issues and Numerical Simulation.” In IEEE Transactions, Vehicular Technology, Nov, 2008. [27] Avala Ramesh and S. Pallem Setty, “Analysis on Biometric Encryption using RSA algorithm,” Published In the IJMER, Volume 2, Issue 11(2), pp. 302-307, October 2013.

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