iosr-JCE   Volume-15 ~ Issue-1

Abstract: Fingerprint recognition is one of the most popular and successful methods used for person identification which takes advantage of the fact that the fingerprint has some unique characteristics called minutiae which are points where a extracts the ridges and bifurcation from a fingerprint image. A critical step in studying the statistics of fingerprint minutiae is to reliably extract minutiae from the fingerprint images. However fingerprint images are rarely of perfect quality. Fingerprint image enhancement techniques are employed prior to minutiae extraction to obtain a more reliable estimation of minutiae locations. Fingerprint matching is often affected by the presence of intrinsically low quality fingerprints and various distortions introduced during the acquisition process. In this paper we have used the rural fingerprints database which is collected from IIIT Delhi research lab which consists of 1634 fingerprints images. Out of which we have preprocess 600 sample preprocessing extracts the ridges and bifurcation from a fingerprint image and tried to improve the quality of images. The Resultant images quality is verified by using different quality measures.

Keywords: minutiae extraction, extracts the ridges and bifurcation, rural fingerprint authentication..

[1] BabasahebV. Bhalerao, Ramesh R. ManzaYogesh M. Rajput Department of CSand IT, Dr.BabasahebAmbedkar Marthwada University, Aurangabad.(MS), India" Use of Quality Measures for Rural Indian Fingerprint Image Database Enhancement and Improve the Recognition Rate International Journal of Computer Applications (0975– 8887) Volume 70– No.18, May 2013.

[2] M. Vatsa, R. Singh, S. Bharadwaj, H. Bhatt, R. Mashruwala "Analyzing Fingerprints of Indian Population Using Image Quality": A UIDAI Case Study 1 - IIIT Delhi, India 2 - UIDAI, India.

[3] C. Puri, K. Narang, A. Tiwari, M. Vatsa, and R. Singh,"On Analysis of Rural and Urban Indian Fingerprint Images" IIIT Delhi, India.

[4] D. Maltoni, D. Maio, A. Jain, and S. Prabhakar, "Hand-book of fingerprint recognition". Springer Verlag, 2003.

[5] R. M.Mandi,S. S. Lokhande, Rotation "Invariant Fingerprint Identification System "International Journal of Electronics Communication and Computer Technology (IJECCT) Volume 2 Issue 4 (July 2012). [6] Rohit Singh (Y6400), Utkarsh Shah (Y6510), Vinay Gupta (Y6534) , "Fingerprint Recognition", Department of Computer Science & engineering Indian Institute of technology, Kanpur. Computer Vision and Image. Processing (CS676).

[7] Graig T. Diefenderfer,"Fingerprint Recognition "Naval PostgraduateSchool Monterey, California.

[8] ChiragDadlani, Arun Kumar Passi ,Herman SahotaMitinKrishan Kumar, "Fingerprint recognition using minutiae based feature" As part of EE851: Biometrics.

[9] Mary Lourde R, and DushyantKhosla "Fingerprint Identification in Biometric Security Systems" International Journal of Computer and Electrical Engineering, Vol. 2, No. 5, October, 2010 1793-8163.

[10] D.Maio and D. Maltoni. Direct gray-scale minutiae detection in fingerprints.IEEE Trans. Pattern Anal. And Machine Intell., 19(1):27-40, 1997.

Abstract: Conventional personal identification techniques for instance passwords, tokens, ID card and PIN codes are prone to theft or forgery and thus biometrics isa solution thereto. Biometrics is the way of recognizing and scrutinizing the physical traits of a person. Automated biometrics verification caters as a conducive and legitimate method, but there must be an assurance to its cogency. Furthermore, in most of the cases unimodal biometric recognition is not able to meet the performance requirements of the applications. According to recent trends, recognition based on multimodal biometrics is emerging at a greater pace. Multimodal biometrics unifies two or more biometric traits and thus the issues that emerge in unimodal recognition can be mitigated in multimodal biometric systems. But with the rapid ontogenesis of information technology, even the biometric data is not secure. Digital watermarking is one such technique that is implemented to secure the biometric data from inadvertent or premeditated attacks.This paper propounds an approach that is projected in both the directions of improving the performance of biometric identification system by going multimodal and, increasing the security through watermarking. The biometric traits are initially transformed using Discrete Wavelet and Discrete Cosine Transformation and then watermarked using Singular Value Decomposition. Scheme depiction and presented outcomes justifies the effectiveness of the scheme.

Keywords: Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Multimodal biometrics, Singular Value Decomposition, Watermarking

[1] A. K. Jain, U. Uludag, Hiding Biometric Data,IEEE Trans. Pattern Analysis and Machine Intelligence, 25(11), Nov. 2003, 1494 – 1498.

[2] A. K. Jain, A. Ross, and S. Prabhakar, An introduction to biometric recognition,IEEE Transactions on Circuits and Systems for Video Technology,14(1),200,4-20.

[3] U. Uludag, S. Pankanti, S. Prabhakar, and A.K. Jain, Biometric cryptosystems: issues and challenges, Proceedings of IEEE, 92( 6),2004, 948-960.

[4] Y. Dodis, L. Reyzin, and A. Smith, Fuzzy extractors: how to generate strong keys from biometrics and other noisy data, Eurocrypt2004, 523-540.

[5] A.Nagar, K.Nandakumar, A. K.Jain, Multibiometric Cryptosystems Based on Feature-Level Fusion, IEEETrans. Inf. Forensics Security, 7( 1), Feb. 2012, 255 – 268

[6] Z. huiming, Z.Huile, A technology of hiding fingerprint minutiae in image,Research & progress of solid state electronics, 26(2),2006, 197-200. [7] C. I. Podilchuk and E. J. Delp, Digital Watermarking: Algorithms and Applications, IEEE Signal Processing Magazine, July 2001, 33-46. [8] I. J. Cox, M. L. Miller, and J. A. Bloom, Digital Watermarking (Morgan Kaufmann Publishers, 2002).

[9] E. T. Lin, A. M. Eskicioglu, R. L. Lagendijk and E. J. Delp, Advances in Digital Video Content Protection, Proceedings of the IEEE, Special Issue on Advances in Video Coding and Delivery, 2004.

[10] G.C. Langelaar, I. Setyawan, R.I. Lagendijik, Watermarking digital image and video data, IEEE Signal Processing Magazine 17 (5) 2000, 20–46.

Abstract: Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fibre layer can be assessed using optical coherence tomography, scanning laser polarimetry, and Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma detection using an Haralick Texture Features from digital fundus images. K Nearest Neighbors (KNN) classifiers are used to perform supervised classification. Our results demonstrate that the Haralick Texture Features has Database and classification parts, in Database the image has been loaded and Gray Level Co-occurrence Matrix (GLCM) and thirteen haralick features are combined to extract the image features, performs better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than 98%. The impact of training and testing is also studied to improve results. Our proposed novel features are clinically significant and can be used to detect glaucoma accurately.

Keywords: Glaucoma, Haralick Texture features, KNN Classifiers, Feature Extraction

[1] R. Haralick, K. Shanmugam, and I. Dinstein, (1973) "Textural Features for Image Classification", IEEE Trans. on Systems, Man and Cybernetics, SMC–3(6):610–621
[2] Sumeet Dua, Senior Member, IEEE, U. Rajendra Acharya, Pradeep Chowriappa "Wavelet-Based Energy Features for Glaucomatous Image Classification" VOL. 16, NO. 1, JANUARY 2012
[3] U.Rajendra Acharya, Sumeet Due, Xian Du,and Vinitha Sree S "Automated Diagnosis of Glaucoma Using Textural and Higher Order Spectra Features"
[4] R. C. Gonzales, R. E.Woods, and S. L. Eddins. Digital Image Processing Using MATLAB.
[5] J. M. Miquel-Jimenez et al., "Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram," Med. Eng. Phys., vol. 32, pp. 617–622, 2010
[6] Bino Sebastian V, A. Unnikrishnan and Kannan Balakrishnan "grey level co-occurrence matrices: generalisation and some new features" (IJCSEIT), Vol.2, No.2, April 2012
[7] F. I. Alam, R. U. Faruqui, (2011) "Optimized Calculations of Haralick Texture Features", European Journal of Scientific Research, Vol. 50 No. 4, pp. 543-553
[8] Celina Rani George "Glaucomatous Image Classification Using Wavelet Based Energy Signatures And Neural Networks" (IJERT) ISSN: 2278-0181 Vol. 2 Issue 3, March – 2013
[9] Miguel-Jiménez J M , Blanco R, "Glaucoma detection by wavelet-based analysis of the global flash multifocal electroretinogram," Med. Eng. Phys., vol. 32, pp. 617–622, 2010.
[10] P. C. Chen and T. Pavlidis, "Segmentation of texture using correlation," IEEE Trans. Pattern Anal. Machine Intell., vol. PAMI-5, pp. 64–69, Jan. 1983.

Abstract: Communication is the important part in any type of network for making it possible to transfer data from one node to another. Communication needs quality and security for better performance and for acceptance of users and client companies. Data integrity is quite an issue in security and to maintain that integrity we tend to improve as to provide the better encryption processes for security. In our proposed work, an innovative and practical authentication system using Diffie-Hellman and AMA (Anonymous Micro payments Authentication) are designed for micropayments in mobile data network. Through AMA the customer and the merchant can authenticate each other indirectly, at the same time the merchant doesn't know the customer's real identity. A customer can get fast micropayments not only from his local domain but also from a remote domain without increasing any burden on his mobile phone/smartcard. Diffie-Hellman Encryption Algorithm adds more security to the proposed work.

Keywords: AMA, Authentication, Diffie-Helman, Provate key, Public key

[1] Zhi-Yuan Hu, Yao-Wei Liu, Xiao Hu, Jian Hua Li, "Anonymous micropayments authentication(AMA) in mobile data network",IEEE INFOCOM 2004.
[2] Vishal Garg, Rishu," Improved Diffie-Hellman algorithm for network secuiryt enhancement", IJCTA,vol 3(4),1327-1331
[3] Eun-Jun Yoon and Kee-Young Yoo, " An Efficient Diffie-Hellman-MAC Key Exchange Scheme", 2009 Fourth International conference on Innovative computing, Information and Control.
[4] Emmanuel Bresson, Olivier Chevassut, David Pointcheva, Jean-Jacques Quisquater, "Authenticated Group Diffie-Hellman Key Exchange", Computer and Communication Security- proc of ACM CSS'01, Philadelphia, Pennsylvania, USA, Pages 255-264, ACM Press, November 5-8, 2001.
[5] Mario Cagaljm, Srdjan Capkun and Jean-Pierre Hubaux," Key agreement in peer-to-peer wireless networks", Ecole Polytechnique F´ed´erale de Lausanne (EPFL), CH-1015 Lausanne.
[6] T. Pedersen "Electronic payments of small amounts" In Fourth Cambridge Workshop on Security Protocols. Springer Verlag, Lecture Notes in Computer Science, April 1996.
[7] P.M. Hallam-Baker. Micro Payment Transfer Protocol (MPTP) Version Novermber 1995. Http://www.w3.org/TR/WD-mptp-951122.
[8] M. Peirce, "Multi-party Micropayments for Mobile Communications", PhD Thesis, Trinity College Dublin, Ireland, Oct. 2000
[9] G. Horn and B. Preneel, "Authentication and payment in future mobile systems" Computer Security - ESORICS'98, Lecture Notes in Computer Science 1485, 1998, pp. 277-293.
[10] Mihir Bellare and Phillip Rogaway "Entity Authentication and key distribution" Extended abstract in Advances in Cryptology – CRYPTO 93, Lecture Notes in Computer Science Vol. 773, D. Stinson ed, Springer-Verlag, 1994.

Abstract: A biometric system provides automatic recognition of an individual based on a unique feature or characteristic possessed by the individual. These biometric characteristic may physiological or behavioral. Unlike other identification methods such as id proof, tokens and password, the distinct aspect of biometric recognition comes into light from randomly distributed features in human being. In this paper, I describe the novel comparison based upon various aspects to make easy selection for biometric device deployment in specific environment. This paper proposes a comparison among all kind of biometric system available in the society. The existing computer security systems used at various places like banking, passport, credit cards, smart cards, PIN , access control and network security are using username and passwords for person identification. Biometric systems also introduce an aspect of user convenience; it means one can be authorized by representing himself or herself. In this paper, the main focus is on working principal of biometric technique, the various biometrics systems and their comparisons.

Keywords: Biometrics, authentication, identification, recognition.

[1] Himanshu Srivastava, "Personal Identification Using Iris Recognition System, a Review," International Journal of Engineering Research and Applications (IJERA), vol. 3, no. 3, pp. 449-453, 2013.
[2] K P Tripathi, "Comparative Study of Biometric Technologies with Reference to Human Interface," International Journal of Computer Applications(IJCA), vo.l 14, no.5, 2011.
[3] Simon Liu and Mark Silverman, "A Practical Guide to Biometric Security Technology," IT Pro 27-32, 2001.
[4] http://www.amazingincredible.com/show/82-the-incredible-human-eye [Online] [26 April, 2013].
[5] Debnath Bhattacharyya, "Biometric Authentication: A Review," International Journal of u- and e- Service, Science and Technology, vol. 2, no. 3, 2009.

Abstract: The subject of quantum computing brings together ideas from classical information theory, computer science, and quantum physics. This review aims to summarize not just quantum computing, but the whole subject of quantum information theory. Information can be identified as the most general thing which must propagate from a cause to an effect. It therefore has a fundamentally important role in the science of physics. However, the mathematical treatment of information, especially information processing, is quite recent, dating from the mid-20th century. This has meant that the full significance of information as a basic concept in physics is only now being discovered. This is especially true in quantum mechanics. The theory of quantum information and computing puts this significance on a firm footing, and has led to some profound and exciting new insights into the natural world. Among these are the use of quantum states to permit the secure transmission of classical information (quantum cryptography), the use of quantum entanglement to permit reliable transmission of quantum states (teleportation), the possibility of preserving quantum coherence in the presence of irreversible noise processes (quantum error correction), and the use of controlled quantum evolution for efficient computation (quantum computation). The common theme of all these insights is the use of quantum entanglement as a computational resource.

Keywords: quantum bits, quantum registers, quantum gates and quantum networks

[1] Jacob west http://www.cs.rice.edu/~taha/teaching/05F/210/news/2005_09_16.htm

[2] Jozef Gruska http://www.fi.muni.cz/usr/gruska/quantum/chapters.html

[3] Julian Voss-Andreae's "Quantum Objects http://physics.about.com/od/quantumphysics/f/quantumcomp.html

[4] Pearson IBM press http://www.ibmpressbooks.com/articles/article.asp?p=374693&seqNum=6

[5] Scott Aaronson MIT, Dave Bacon University of Washington

[6] Svore-aho-cross-chuang-markovhttp://research.microsoft.com/pubs/144689/qc-svore-aho-cross-chuang-markov-computer-magazine-article-a-layered-software-architecture-for-quantum-computing-design-tools-jan06-svore.pdf

[7] University of OXFORD http://www.maths.ox.ac.uk/groups/mathematical-physics/research-areas/quantum-information

[8] Webopedia http://www.webopedia.com/TERM/Q/quantum_computing.html

Abstract: Autonomic Computing refers to the self-managing characteristics of distributed computing resources, adapting to unpredictable changes while hiding intrinsic complexity to operators and users. An autonomic system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. As widely reported in literature, an autonomic computing framework might be seen composed by autonomic components interacting with each other. An Autonomic Computing can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness. The goal of autonomic computing is to create systems that run themselves, capable of high-level functioning while keeping the system's complexity invisible to the user. General Terms: Autonomic systems, Self-configuration, Self-healing, Self-optimization, Self-protection.

Keywords: Know itself, reconfigure, recover from extraordinary events, expert in self-protection,

[1] IBM, "Autonomic Computing: IBM's Perspective on the State of Information Technology"; http://www.research.ibm.com/autonomic/
[2] IBM White Paper, "An architectural blueprint for autonomic computing", IBM
[3] Sterritt, Roy and M. Hinchey, "Tutorial proposal: autonomic computing in real-time systems".
[4] IBM, "Autonomic Computing: IBM's Perspective on the State of Information Technology"; http://www-1. ibm.com/industries/government/doc/content/resource/ thought/278606109.html.
[5] Abdullah, Gani and G. Manson, "Towards a Self-Healing Networking Controlling Access to Network Applications", Informing Science.
[6] Philip Koopman, "Elements of the Self-Healing System Problem Space", ICSE
[7] http://searchcio-midmarket.techtarget.com/definition/autonomic-computing
[8] Journal of Global Research in Computer Science, 3 (5), May 2012

Abstract: The open nature of the wireless medium leaves it too weak to intentional interference attacks, typically defined as jamming. This intentional interference with wireless transmissions can be used as a launch pad for mounting Denial-of-Service attacks on wireless networks. Typically, jamming has been introduced under an external threat model. However, intruders with internal knowledge of protocol specifications and network secrets can launch low-effort jamming attacks that are difficult to detect and counter. In this work, we address the problem of selective jamming attacks in wireless networks. In these attacks, the hacker is active only for a short period of time, selectively targeting messages of high importance. We demonstrate the advantages of selective jamming in terms of network performance degradation and hacker effort by presenting two case studies; a selective attack on TCP and one on routing. We show that selective jamming attacks can be forwarded by performing real-time packet classification at the physical layer. To reduce these attacks, we develop three schemes that prevent real-time packet classification by combining cryptographic primitives with physical-layer attributes. We analyze the security of the proposed methods and evaluate their computational and communication overhead.

[1] T. X. Brown, J. E. James, and A. Seth. Jamming and sensing of encrypted wireless ad hoc networks. In Proceedings of MobiHoc, pages 120–130, 2006.

[2] M. Cagalj, S. Capkun, and J.-P. Hubaux. Wormhole-based antijamming techniques in sensor networks. IEEE Transactions on Mobile Computing, 6(1):100–114, 2007.

[3] A. Chan, X. Liu, G. Noubir, and B. Thapa. Control channel jamming: Resilience and identification of traitors. In Proceedings of ISIT, 2007.

[4] T. Dempsey, G. Sahin, Y. Morton, and C. Hopper. Intelligent sensing and classification in ad hoc networks: a case study. Aerospace and Electronic Systems Magazine, IEEE, 24(8):23–30, August 2009.

[5] Y. Desmids. Broadcast anti-jamming systems. Computer Networks, 35(2-3):223–236, February 2001.

[6] K. Gaj and P. Chodowiec. FPGA and ASIC implementations of AES. Cryptographic Engineering, pages 235–294, 2009.

[7] O. Goldreich. Foundations of cryptography: Basic applications. Cambridge University Press, 2004.

[8] B. Greenstein, D. McCoy, J. Pang, T. Kohno, S. Seshan, and D. Wetherall. Improving wireless privacy with an identifier-free link layer protocol. In Proceedings of MobiSys, 2008.
[9] IEEE. IEEE 802.11 standard. http://standards.ieee.org/getieee802/ download/802.11-2007.pdf, 2007.

[10] A. Juels and J. Brainard. Client puzzles: A cryptographic countermeasure against connection depletion attacks. In Proceedings of NDSS, pages 151–165, 1999.

Abstract: Hadoop-MapReduce is a powerful parallel processing technique large for big data analysis on distributed commodity hardware clusters such as Clouds. For job scheduling Hadoop provides default FIFO scheduler where jobs are scheduled in FIFO order. But this scheduler might not be a good choice for some jobs. So in such situations one should choose an alternate scheduler. In this paper we conduct a study on various schedulers. Hopefully this study will stimulate other designers and experienced end users understand the details of particular schedulers, enabling them to make the best choices for their particular research interests.

Keywords: Cloud Computing, Hadoop, HDFS, MapReduce, schedule

[1] http://www.ibm.com/developerworks//library/os- hadoop-scheduling/

[2] http://blog.cloudera.com/blog/2008/11/job-scheduling-in-hadoop/

[3] http://cps516project2donghe.weebly.com/fifo.html

[4] http://hadoop.apache.org/docs/current/hadoop yarn/hadoop-yarn-site/CapacityScheduler.html

[5] http://hadoop.apache.org/docs/stable/fair_scheduler.html

[6] "Survey on Improved Scheduling in Hadoop MapReduce in Cloud Environments " by b thirumala rao

Abstract: In Mobile Ad hoc Network (MANET) mobile node is responsible for route establishment using wireless link where each node may behave like both as a host and router. MANET encounters number of security threats because of its open entrusted environment, with little security arrangement, security over MANET can be enhance up to some satisfactory level because of its inherent characteristics. Among some of the prominent security threats wormhole attack is considered to be a very serious security threat over MANET. In wormhole two selfish node which is geographically very far away to each other makes tunnel between each other to hide their actual location and give the illusion that they are true neighbours and attract other nodes to make conversation through the wormhole tunnel. Many researchers focused on detecting wormhole attack and its prevention mechanism. It seems that in the previous technique there is a need to improve their results in the brink of false negative rate, routing overhead etc. The present paper has proposed the hybrid model in order to detect and prevent the wormhole attack. This approach has been work with neighbour node and hop count method.

Keywords: Mobile Ad hoc Network, Selfish node, Malicious node, AODV

[1] Pallavi Sharma, Prof. Aditya Trivedi "An Approach to Defend Against Wormhole Attack in Ad Hoc Network Using Digital Signature" in IEEE ,2011
[2] Sanjay Kumar Dhurandher and Isaac Woungang "E2SIW: An Energy Efficient Scheme Immune to Wormhole Attacks in Wireless Ad Hoc Networks" in 26th International Conference on Advanced Information Networking and Applications Workshops in IEEE,2012
[3] Sebastian TerenceJ "Secure Route Discovery against Wormhole Attacks in Sensor Networks using Mobile Agents" in IEEE 2011
[4] Bing Wu, Jianmin Chen, Jie Wu and Mihaela Cardei, "A Survey on Attacks and Countermeasures in Mobile Ad Hoc Networks", Springer Wireless/Mobile Network Security2007, pp 103-135
[5] Rusheel Jain, Murali Parameswaran and Chittaranjan Hota, "An Efficient On-Demand Routing Protocol for MANETs using Bayesian Approach", IEEE 2011
[6] Fei Shi, Dongxu Jin, Weijie Liu and JooSeok Song, "Time-based Detection and Location of Wormhole Attacks in Wireless Ad Hoc Networks" IEEE 2011, pp 1721-1726.
[7] Binxiao Yu and Tongqiang Li, "Study of Wormhole Attack Detecting Based on Local Connectivity Information in Wireless Sensor Networks", IEEE 2011, pp 3585-3588.
[8] Ronggong song, Peter c. Mason, Ming li "Enhancement of frequency-based wormhole attack detection" in military communications conference, 2011 - milcom ,ieee,2011
[9] Jin Guo, Zhi-yong Lei, "A Kind of Wormhole Attack Defense Strategy ofWSN Based on Neighbor Nodes Verification", IEEE 2011, 564-568.

Abstract: class as "a collection of data and methods." One of the important object-oriented techniques is hiding the data within the class and making it available only through the methods. This technique is known as encapsulation because it seals the data (and internal methods) safely inside the "capsule" of the class, where it can be accessed only by trusted users. Another reason for encapsulation is to protect your class against accidental or willful stupidity. A class often contains a number of interdependent fields that must be in a consistent state. If you allow a users to manipulate those fields directly, he may change one field without changing important related fields, thus leaving the class in an inconsistent state. If, instead, he has to call a method to change the field, that method can be sure to do everything necessary to keep the state consistent. Similarly, if a class defines certain methods for internal use only, hiding these methods prevents users of the class from calling them. The goal is : All the fields and methods of a class can always be used within the body of the class itself. Java defines access control rules that restrict members of a class from being used outside the class that is done through keeping the data (and internal methods) safely inside the "capsule" of the class, where it can be accessed only by trusted users. Keywords: Encapsulation, Access Control, Classes, Constructors, Methods.

[1] L. Gong and G. Ellison, Inside Java(TM) 2 Platform Security: Architecture, API Design, and Implementation. Pearson Education, 2003.

[2] L. Gong, M. Mueller, H. Prafullchandra, R. Schemers, and S. Microsystems, ―Going beyond the sandbox: An overview of the new security architecture in the Java development kit 1.2,‖ Proceedings of the USENIX Symposium for Secure Systems, 1997.

[3] N. Hardy, ―The confused deputy,‖ SIGOPS Operating Systems Review, vol. 22, no. 4, pp. 36–38, 1988.

[4] I. Welch and R. Stroud, ―Supporting real world security models in Java,‖ in Distributed Computing Systems, 1999. Proceedings. 7th IEEE Workshop on Future Trends of Distributed Computing Systems, 1999, pp. 155–159.

[5] J. Viega, J. Bloch, and P. Chandra, ―Applying Aspect-Oriented programming to security,‖ Cutter IT Journal, vol. 14, no. 2, pp. 31–39, Feb. 2001.