IOSR Journal of Computer Engineering (IOSR-JCE)

Jan - Feb 2014Volume 16 - Issue 1

Version 1 Version 2 Version 3 Version 4 Version 5 6 7 8 9

Paper Type : Research Paper
Title : An Extended Approach for Online Testing of Reversible Circuits
Country : India
Authors : Anugrah Jain, Nitin Purohit, Sushil Chandra Jain
: 10.9790/0661-16110111      logo

Abstract: Reversible computing has tremendous benefits in terms of power consumption, less heat dissipation and packaging density. Because its applications are found in diverse fields including quantum computing, nanotechnology, low power CMOS designs and cryptography, Reversible computing has gained attraction of many researchers recently. In order to incorporate fault testing capability in reversible circuits, a number of offline and online approaches have been proposed. In order to extend online testability of reversible circuits, an analysis followed by a Peres gate substitution is presented here. The proposed extension has identified online testing capabilities of MCF gates and has made all available libraries including MCT+MCF, MCT+P online testable. Furthermore a conversion for parity-preserving reversible circuits is presented. Finally the paper is concluded by proposing a generic online testable substitution of n*n reversible gate.

Keywords: Reversible circuits, online testable reversible circuits, online testable reversible substitution.

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[3] R. Wille, M. Saeedi and R. Drechsler, Synthesis of Reversible Functions Beyond Gate Count and Quantum Cost, International Workshop on Logic Synthesis (IWLS), USA, 2009.
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[5] M. Arabzadeh, M. Saeedi, and M. Zamani, Rule-based optimization of reversible circuits, In Proceedings of Asia and South Pacific Design Automation Conference (ASPDAC), pages 849–854, 2010.
[6] J. Chen, X. Zhang, L. Wang, X. Wei, and W. Zhao, Extended Toffoli gate implementation with photons, In Proceedings of 9th International Conference on Solid-State and Integrated-Circuit Technology (ICSICT), pages 575–578, China, 20-23 Oct 2008.
[7] B. Parhami, Fault tolerant reversible circuits, In Proceedings of 40th Asimolar Conf. Signals, Systems, and Computers, Pacific Grove, CA, pp. 1726-1729, October 2006.
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[9] M. Haghparast and K. Navi, A novel fault tolerant reversible gate for nanotechnology based systems, Am. J. of App. Sci., vol. 5, no.5, pp. 519-523, 2008.
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Paper Type : Research Paper
Title : Detecting Good Neighbor Nodes and Finding Reliable Routing Path Based on AODV Protocol
Country : India
Authors : Supriya Bamane, Rajesh Singh
: 10.9790/0661-16111219      logo

Abstract: Wireless operations allow services, such as long-range communications, that are impossible or impractical to implement with the use of wires. It is supported by well-liked technique known as Adhoc Protocol [1]. The term is commonly used in the telecommunications industry to refer to telecommunications systems e.g. radio transmitters and receivers, remote controls etc. which use some form of energy e.g. radio waves, acoustic energy, etc. to transfer information without the use of wires.[1] Information is transferred in this manner over both short and long distances.In this, routes may be detached due to lively movement of nodes. So route assortment and topology grouping is not easy and demanding issue. This type of networks is more vulnerable to both internal and external attacks due to presence of wicked neighbour nodes[1][2s. Paper see the sights new method using AODV protocol to find out good neighbour node and finding reliable path according to their signal strength, flow capacity relative position of node in network.

Keywords: AODV routing protocol, ad-hoc network, signal Strength, flow capacity, relative position of node Routing Table..

[1] Supriya Bamane .Rajesh Singh. AODV Based Improved Method for Detecting Good Neighbor Nodes International Journal of Emerging Technology and Advanced Engineering (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 3, Issue 7, July 2013)
[2] Prof. M.N. Hoda and Umang Singh, GNDA: Detecting Good Neighbour Nodes in Ad-hoc Routing Protocol 2011 Second International Conference on Emerging Applications of Information Technology, IEEE.
[3] C. E. Perkins and E. M. Royer, âA.Ad hoc on demand distance vector(AODV) routing,âAI Internet- Draft, draft-ietf-manet-aodv-02.txt, Nov.1998
[4] C.Siva Ram Murthy and B.S.Manoj,A.Ad hoc Wireless Networks AI,Pearson 2005.ISBN 81-297- 0945- 7 Sridhar K N and Mun Choon Chan ,A.Stability and Hop-Count based Approach for Route Computation in MANET,AI , 0-7803-9428- 3/05/ 2005 IEEE.
[5] Youngrag Kim, Shuhrat Dehkanov, Heejoo Park, Jaeil Kim, Chonggun Kim, âA.The Number of Necessary Nodes for Ad Hoc Network Areas AI, 2007 IEEE Asia-Pacific Services Computing Conference
[6] [ Srdjan Krco and Marina Dupcinov, Improved Neighbor Detection Algorithm for AODV Routing Protocol IEEE COMMUNICATIONS LETTERS, VOL. 7, NO. 12, DECEMBER 2003.
[7] Qing Li, Cong Liu, Hang Hong Jiang, The Routing Protocol of AODV Based on Link Failure Prediction ICSP2008 Proceedings, 978-1-4244-2179- 4/08/25.00 2008 IEEE
[8] M. Usha, S. Jayabharathi, Wahida Banu R, REAODV: An enhanced routing algorithm for QoS Support in Wireless Ad-hoc Sensor Networks ,2011, IEEE International conference on Recent trends in Information Technology.
[9] Che-Aron, Al-Khateeb, Anwar, An Enhancement of Fault-Tolerant routing protocol for wireless sensor network, 2010, IEEE International conference on computer and communication engineering.
[10] Wang N, Cao Yewen, An Improved AODV protocol with lower route cost and smaller delay, 2011, IEEE fourth international conference on intelligent computation technology and automation.


Paper Type : Research Paper
Title : Alternate Sort
Country : India
Authors : Syed Azher Nadeem Pasha
: 10.9790/0661-16112022     logo

Abstract: Sorting algorithms are the main concepts of the subject Data Structures and It's Applications. These algorithms are designed in arranging the data elements in the sorted order. If the data elements are arranged in sorted order , then the searching is very easier. Some algorithms are comparison sort and some are non-comparison sort. The choice of a algorithm is based on the efficiency of the algorithm. I have designed one algorithm called as Alternate Sort. The main aspect is that different technique of comparisons is involed. I have presented the algorithm , It's working and the examples and finally my paper is consisting of the program listing.
Keywords: Alternate Array Efficiency Exchanges Sort.

[1] Website referring the Sorting algorithm from Wickepedia.


Paper Type : Research Paper
Title : Intelligent Fault Identification System for Transmission Lines Using Artificial Neural Network
Country : India
Authors : Seema Singh, Mamatha K. R., Thejaswini S.
: 10.9790/0661-16112331      logo

Abstract: Transmission and distribution lines are vital links between generating units and consumers. They are exposed to atmosphere, hence chances of occurrence of fault in transmission line is very high, which has to be immediately taken care of in order to minimize damage caused by it. This paper focuses on detecting the faults on electric power transmission linesusing artificial neural networks. A feed forward neural network isemployed, which is trained with back propagation algorithm. Analysis on neural networks with varying number of hidden layers and neurons per hidden layer has been provided to validate the choice of the neural networks in each step. The developed neural network is capable of detecting single line to ground and double line to ground for all the three phases. Simulation is done using MATLAB Simulink to demonstrate that artificial neural network based method are efficient in detecting faults on transmission lines and achieve satisfactory performances. A 300km, 25kv transmission line is used to validate the proposed fault detection system. Hardware implementation of neural network is done on TMS320C6713.
Keywords: Transmission Line, Asymmetric fault detection, Artificial neural network (ANN), Back Propagation algorithm, DSP processor TMS320C6713, Code Composer Studio, MATLAB/Simulink.

[1] Das R, Novosel D, "Review of fault location techniques for transmission and sub – transmission lines", proceedings of 54th Annual
Georgia Tech Protective RelayingConference, 2000.
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York, IEEE Std C37.114, 2005.
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Study committee 34 Colloquium and Meeting, Florence, 1999, paper 215.
[5] Tang Y, Wang HF, Aggarwal R K, "Fault indicators in transmission and distribution systems", Proceedings of International
conference on Electric Utility Deregulation and Restructuring and Power Technologies – DRPT, 2000, pp. 238-243.
[6] Reddy MJ, Mohanta DK, "Adaptive-neuro-fuzzy inference system approach for transmission line fault classification and location
incorporating effects of power swings", Proceedings of IET Generation, Transmission and Distribution, 2008, pp. 235 – 244.
[7] S. Haykin, Neural Networks, IEEE Press, New York, 1994.
[8] M. T. Hagan and M. B. Menhaj, "Training Feedforward Networks with the Marquardt Algorithm", IEEE Trans. on Neural
Networks, Vol. 5, No. 6, 1994, pp. 989-993.
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38.
[10] Thomas Behan, "Investigation of Integer Neural Networks for low cost embedded System", in Electrical and Computer Engineering
Commons", Jan 2009, pp 5-15.


Paper Type : Research Paper
Title : Clustering and Classification of Cancer Data Using Soft Computing Technique
Country : India
Authors : Mr. S. P. shukla, Mrs. Ritu Dwivedi
:    10.9790/0661-16113236   logo

Abstract: Clustering and classification of cancer data has been used with success in field of medical side. In this paper the two algorithm K-means and fuzzy C-means proposed for the comparison and find the accuracy of the result. this paper address the problem of learning to classify the cancer data with two different method and information derived from the training and testing .various soft computing based classification and show the comparison of classification technique and classification of this health care data .this paper present the accuracy of the result in cancer data.
Keywords: clustering, classification.

[1] MATLAB software in URL address:\\www.mathworks.com\\The Math Works,
[2] Using Cancer data set from UCI repository data set ,the URL address: \\WWW.UCI.com\\
[3] George j.klir / boyuan "fuzzy set and fuzzy logic" theory and application, year 2003, pages 50-61,
[4] MATLAB software in URL address: \\www.mathworks.com\\The Math Works, MATLAB 7.5.0(R2007b) help file.
[5] Zadeh, Lotfi A., "Fuzzy Logic, Neural Networks, and Soft Computing," Communications of the ACM, March 1994, Vol. 37 No. 3, pages 77-84.
[6] Takagi, H.: "Fusion Technology of Fuzzy Theory and Neural Networks: Survey and Future Directions" IIZUKA90: International Conference on Fuzzy Logic and Neural Networks. pp. 13-26, Iizuka, Japan 1990.
[7] Tanaka, Makoto: "Application of The Neural Network and Fuzzy Logic to The Rotating Machine Diagnosis" Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications. CRC Press LLC, CRC Press LLC, Boca Raton, FL, USA 1999.
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[9] Zadeh, Lotfi: "The Role of Soft Computing and Fuzzy Logic in the Conception, Design, Development of Intelligent Systems" Plenary Speaker, Proceedings of the International Workshop on soft Computing Industry. Muroran, Japan, 1996.
[10] Zadeh, Lotfi: "What is Soft Computing" Soft Computing. Springer-Verlag Germany/USA 1997.


Paper Type : Research Paper
Title : Unsupervised Clustering Classify the Cancer Data with the Help of FCM Algorithm
Country : India
Authors : Mr. S. P. shukla, Mrs. Ritu Dwivedi
: 10.9790/0661-16113741      logo

Abstract: There is structure in nature; also it is believed that there is an underlying structure in most of phenomena, to be understood. in image recognition ,mole biology applications such as protein folding and 3D molecular structure, cancer detection many others the underlying structure exists .by finding structure one classifies the data according to similar patterns, features and other characteristics .this general idea is known as classification. In classification, also termed clustering, the most important issue is deciding what criteria to classify against. This paper presents the fuzzy classification techniques to classify the data of cancer disease. Cluster analysis, cluster validity and fuzzy C-mean (FCM) technique are proposed to be discussed and applied in cancer data classification.
Keywords: classification, clustering, cancerdata.

[1] George j.klir / boyuan "fuzzy set and fuzzy logic" theory and application, year 2003, pages 50-61
[2] Gurney, Kevin: An Introduction to Neural Networks. UCL Press, London, UK 1999.
[3] Fausett, Laurene: Fundamentals of Neural Networks: Architectures, algorithms, and Applications. Prentice Hall, NJ, USA 1994.
[4] Hertz, J.A., Krogh, A. & Palmer, R. Introduction to the Theory of Neural Computation(Addison-Wesley, Redwood City, 1991)
[5] Tanaka, Makoto: "Application of The Neural Network and Fuzzy Logic to The Rotating Machine Diagnosis" Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms: Industrial Applications. CRC Press LLC, CRC Press LLC, Boca Raton, FL, USA 1999.
[6] Lee, S. and E. Lee: "Fuzzy Sets and Neural Networks" Journal of Cybernetics. Volume 4, No. 2, pp. 83-013, 1974.
[7] Zadeh, Lotfi: "The Role of Soft Computing and Fuzzy Logic in the Conception, Design, Development of Intelligent Systems" Plenary Speaker, Proceedings of the International Workshop on soft Computing Industry. Muroran, Japan, 1996.
[8] [172] Zadeh, Lotfi: "What is Soft Computing" Soft Computing. Springer-Verlag Germany/USA 1997.
[9] Kacpzyk, Janusz (Editor): Advances in Soft Computing. Springer-Verlag, Heidelberg, Germany, 2001.


Paper Type : Research Paper
Title : Energy Efficiency in IEEE 802.11 standard WLAN through MWTPP
Country : India
Authors : Anupam Das, Prof. Shikhar Kumar Sarma
: 10.9790/0661-16114246      logo

Abstract: The main goal of this work is to achieve the energy efficiency in 802.11 WLAN through minimizing the energy consumption in the network. In this proposed study, we introduced a modification in PCF for enhancing the performance of WLAN and it is achieved by giving a new definition for the PCF function of transmission. Generally, in PCF the way AP transmits for the various nodes is one-way during the process of polling. The proposed function modification for PCF enhances the IEEE 802.11 standard PCF Multi-Way Transmission PCF Protocol (MWTPP) with an improved version MWTPP it gives a low-complexity mechanism by which the active and non-active stations in the BSS save energy during the process of polling. With the inception of MWTPP transmissions are taken place in multi-way the access to the WLAN channel for mobile nodes in the list generated for polling with the SIFS interval whenever the transmission in receiving data packet from AP.
Keywords: MWTPP (Multi-Way transmission PCF Protocol), WLAN(Wireless LAN), PCF(Point Coordination Function).

[1] A Study on Energy Consumption in WLAN and Improving its efficiency through NBE-algorithm by A Das & Prof. Shikhar Kr. Sarma
[2] Energy Efficiency of Ad hoc Wireless Networks with selfish users by Vikrma Srinivasan, Pavan Nagcahlli.
[3] Energy Efficiency Analysis of IEEE 802.11 DCF with variable Packet length was carried out by Bo Gao, Yuhang Yang Huiye Ma
[4] Modeling Energy Efficiency Secure Wireless Neyworks using Network Simulation By Ramesh Puri and Piyush Misra.


Paper Type : Research Paper
Title : An Overview of Intrusion Detection and Prevention Systems (IDPS) and Security Issues
Country : India
Authors : Ahmad Sharifi, Freshteh Farokh Zad, Farnoosh Farokhmanesh, Akram Noorollahi, Jallaledin Sharifi
: 10.9790/0661-16114752      logo

Abstract: Technical solutions, introduced by policies and implantations are essential requirements of an information security program. Advanced technologies such as intrusion detection and prevention system (IDPS) and analysis tools have become prominent in the network environment while they involve with organizations to enhance the security of their information assets. Scanning and analyzing tools to pinpoint vulnerabilities, holes in security components, unsecured aspects of the network and deploying of IDPS technology are highlighted.
Keywords: Detection, intrusion, prevention, security, vulnerability.

[1] U. A. Sandhu, S. Haider, S. Naseer and O. U. Ateeb, A Survey of Intrusion Detection & Prevention Techniques, International Conference on Information Communication and Management IPCSIT: IACSIT Press, Singapore 2011.
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[3] B. Menezes, Network Security and Cryptography (Patparganj, New Delhi: Cengage Learning India Pvt. Ltd, 2010).
[4] J. R. Vacca, Computer and information security handbook (New York: Morgan Kaufmann, Elsevier 2009) 39-66, 133-166, 255-267, 293-306, 349-393, 469-496.
[5] A. Fuchsberger, Intrusion Detection Systems and Intrusion Prevention Systems, Published by Elsevier: Information Security Technical Report 10, 2005, 134-139.
[6] P. Innella, http://www.symantec.com /connect/articles/ managing-intrusion-detection-systems-large-organizationspart-one.
[7] EC-Council, Ethical Hacking and Countermeasures Version 6 Module XVII Web Application Vulnerabilities: International Council of E-commerce Consultants, 2008.
[8] R. E. Overill, ISMS insider intrusion prevention and detection, Published in Elsevier, Information security technical report 13, 2008, 216-219.
[9] N. Godbole, Information Systems Security: Security Management, Metrics, Frameworks and Best Practices (New Delhi: WIELY INDIA, 2009).
[10] N. F. Mir, Computer and Communication Networks (New York: Prentice Hall, 2006), 57-60, 101-125.


Paper Type : Research Paper
Title : Fuzzy Querying Based on Relational Database
Country : India
Authors : Anupriya, Prof. Rahul Rishi
: 10.9790/0661-16115359     logo

Abstract: The traditional query in relational database is unable to satisfy the needs for dealing with fuzzy linguistic values. In this paper, a new data query technique composed of fuzzy theory and MS-SQL is provided. Here, the query can be implemented for fuzzy linguistic variables query via an interface to Microsoft ASP.NET. It is being applied to an realistic instance i.e. questions could be expressed by fuzzy linguistic values such as young age, high blood pressure, average heart beat etc in Patients' relational database. This could be widely used to realize the other fuzzy query based on database.
Keywords: ASP.NET, Fuzzy query, Fuzzy theory, MS-SQL, Relational database.

[1] Z.M. Ma,"A conceptual design methodology for fuzzy relational databases", Journal of Database Management , Vol. 16, No. 2 ,pp 66-83 , 2005.
[2] Anupriya and Rahul Rishi, " Review of Fuzzy Logical Database Models" , IOSR Journal of Computer Engineering , Vol. 8, No. 4, pp 24-30, 2013.
[3] E. F. Codd, "The relational model for database management", Version 2. Reading, MA: Addison-Wesley, 1990.
[4] L. A. Zadeh, " Fuzzy sets", Information and Control, Vol. 8, pp 338-353, 1965.
[5] J. Galindo, A. Urrutia and M. Piattini ," Fuzzy Databases: Modeling Design and Implementation" , IDEA Group Publishing, Hershey, USA , 2006.
[6] A. Adeli and M. Neshat, "A Fuzzy expert system for heart disease diagnosis", Proceedings of International MultiConference of Engineers and Computer Scientists(IMECS) ,Vol. 1, 2010
[7] P. Bosc and 0. Pivert, "On the efficiency of the alpha-cut distribution method to evaluate simple fuzzy relational queries", Advances in Fuzzy Systems Applications and Theory, Vol. 4, pp 251-260, 1995.


Paper Type : Research Paper
Title : Protection of Direct and Indirect Discrimination using Prevention Methods
Country : India
Authors : S. Rajeswari, R. Poonkodi, Dr. C. Kumar Charlie Paul
:  10.9790/0661-16116065      logo

Abstract: Along with privacy, discrimination is a very important issue when considering the legal and ethical aspects of data mining. It is more than observable that the majority people do not want to be discriminated because of their gender, nationality, religion, age and so on, particularly when those aspects are used for making decisions about them like giving them a occupation, loan, insurance, etc. determining such possible biases and eliminating them from the training data without harming their decision-making utility is therefore extremelypopular. For this reason, antidiscrimination methods containing discrimination detection and prevention have been introduced in data mining. Discrimination prevention consists of suggestmodels that do not lead to discriminatory decisions even if the original training datasets are essentially biased. In this section, by focusing on the discrimination prevention, we present taxonomy for classifying and examining discrimination prevention schemes. Then, we begin a group of pre-processing discrimination prevention schemes and indicate the special features of each approach and how these approaches deal with direct or indirect discrimination. A production of metrics used to estimate the performance of those approaches is also specified. In conclusion, we finish our learn by specifying interesting future directions in this research body.

[1] R. Agrawal and R. Srikant, "Fast Algorithms for MiningAssociation Rules in Large Databases," Proc. 20th Int'l Conf. VeryLarge Data Bases, pp. 487-499, 1994.
[2] T. Calders and S. Verwer, "Three Naive Bayes Approaches forDiscrimination-Free Classification," Data Mining and KnowledgeDiscovery, vol. 21, no. 2, pp. 277-292, 2010.
[3] European Commission, "EU Directive 2004/113/EC on Anti-Discrimination," http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?ui=OJ:L:2004:373:0037:0043:EN:PDF, 2004.
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[5] S. Hajian, J. Domingo-Ferrer, and A. Martı´nez-Balleste´, "DiscriminationPrevention in Data Mining for Intrusion and CrimeDetection," Proc. IEEE Symp. Computational Intelligence in CyberSecurity (CICS '11), pp. 47-54, 2011.
[6] S. Hajian, J. Domingo-Ferrer, and A. Martı´nez-Balleste´, "RuleProtection for Indirect Discrimination Prevention in DataMining," Proc. Eighth Int'l Conf. Modeling Decisions for ArtificialIntelligence (MDAI '11), pp. 211-222, 2011.
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Paper Type : Research Paper
Title : Filtering Unwanted Messages from Online Social Networks (OSN) using Rule Based Technique
Country : India
Authors : Sujapriya S., G. Immanual Gnana Durai, Dr. C. Kumar Charlie Paul
: 10.9790/0661-16116670   logo

Abstract: Online Social Networks (OSNs) are today one of the most popular interactive medium to share, communicate, and distribute a significant amount of human life information. In OSNs, information filtering can also be used for a different, more responsive, function. This is owing to the fact that in OSNs there is the possibility of posting or commenting other posts on particular public/private regions, called in general walls. Information filtering can therefore be used to give users the ability to automatically control the messages written on their own walls, by filtering out unwanted messages. OSNs provide very little support to prevent unwanted messages on user walls. For instance, Facebook permits users to state who is allowed to insert messages in their walls (i.e., friends, defined groups of friends or friends of friends). Though, no content-based partialities are preserved and therefore it is not possible to prevent undesired communications, for instance political or offensive ones, no matter of the user who posts them. To propose and experimentally evaluate an automated system, called Filtered Wall (FW), able to filter unwanted messages from OSN user walls.
Keywords: Information filtering, online social networks, Short text classification, policy-based personalization.

[1] A. Adomavicius and G. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
[2] M. Chau and H. Chen, "A Machine Learning Approach to Web Page Filtering Using Content and Structure Analysis," Decision Support Systems, vol. 44, no. 2, pp. 482-494, 2008.
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Paper Type : Research Paper
Title : Manual Unpacking Of Upx Packed Executable Using Ollydbg and Importrec
Country : India
Authors : Asha Devi, Gaurav Aggarwal
: 10.9790/0661-16117177     logo
Abstract: A 'Packer' is a compression routine that compress an executable file. Packers are used on executable for two main reasons: to shrink programs or to thwart detection or analysis. When malware has been packed, an analyst typically has access to only the packed file, and cannot examine the original unpacked program or the program that packed the malware. In order to unpack an executable, we must undo the work performed by the packer, which requires that we understand how a packer operates. All packers take an executable file as input and produce an executable file as output. The packed executable is compressed, encrypted, or otherwisetransformed, making it harder to recognize and reverse-engineer. Unpacked executable are loaded by the OS. With packed programs, the unpacking stub is loaded by the OS, and then the unpacking stub loads the original program. The code entry point for the executable points to the unpacking stub rather than the original code. The original program is generally stored in one or more extra sections of the file.
Keywords: Packing, Unpacking stub,PE header,Sections.

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