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Offline Handwritten Character Recognition is a very challenging field to work upon, as the handwriting of an individual differs very much from another individual, even the handwriting of an individual may differ on different times. Studies have shown that recognition efficiency of characters depends on the ways the features are extracted and formulated as the feature vector. A lot of techniques have been proposed by the various research scholars for feature extraction. In this paper, a combinational approach of feature extraction is proposed as combinational feature vectors (Gradient features, Zernike complex moment features, and Wave based features) may contribute to improved recognition rate. For training and testing purpose, samples of Hindi numerals from 0 to 9 are taken. A feature vector of directional gradient histogram (DGH), a feature vector of Zernike complex moments (ZCM) and a feature vector of Wave features (WF) are feed to the Back-propagation based Neural Network classifiers for training and recognition rate of approx. 79.7%, 92.7% and 73% are attained respectively. By combining the feature vectors DGH, CZM, and WF, a higher recognition rate of 96.4% is obtained for isolated Hindi Numerals.

Character Recognition, Gradient features, Zernike Moments, Wave features, Backpropagation Neural Network

[1] M.B. Mendapara and M.M. Goswami, “Stroke Identification in Gujarati Text using Directional Feature”, International Conference on Green Computing Communication and Electrical Engineering, IEEE 2014.
[2] Anitha Mary, M.O. Chacko and P.M. Dhanya, “A Comparative Study of Different Feature Extraction Techniques for Offline Malayalam Character Recognition”, International Conference on Computational Intelligence in Data Mining, Springer, Vol. 2, pp.9-18, 2014.
[3] Ravindra S. Hegadi and Parshuram M. Kamble, “Recognition of Marathi Handwritten Numerals Using Multi-layer Feed-Forward Neural Network”, World Congress on Computing and Communication Technologies, IEEE, pp. 21-24, 2014.
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[7] Sandhya Arora, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu, Mahantapas Kundu, “Multiple Classifier Combination for Offline Handwritten Devanagari Character Recognition”, arxiv.org/pdf/1006.5913, pp. 01-06, June 2010.
[8] Xianjing Wang and Atul Sajjanhar, “Using a Circular grid for Offline Handwritten Character Recognition”, 4th International Congress on Image and Signal Processing, pp. 945-949, 2011.
[9] Ashutosh Aggarwal, Karamjeet Singh and Kamalpreet Singh, “Use of Gradient Technique for extracting features from Handwritten Gurumukhi Characters and Numerals”, International Conference of Information and Communication Technologies, Elsevier, pp. 1716-1723, 2014.
[10] Karbhari V. Kale, Prapti D. Deshmukh, Shriniwas V. Chavan, Majharoddin M. Kazi, Yogesh S. Rode, “Zernike Moment Feature Extraction for Handwritten Devanagari (Marathi) Compound Character Recognition”, International Journal of Advanced Research in Artificial Intelligence, Vol. 3, No.1, pp. 68-76, 2014.
[11] Kulkarni Sadanand A., Borde Prashant L., Manza Ramesh R., Yannawar Pravin L. “Offline MODI Character Recognition Using Complex Moments”, Second International Symposium on Computer Vision and the Internet (VisionNet’15), Procedia Computer Science -58 516–523, pp.516-523, 2015.
[12] Dayashankar Singh, Dr J.P. Saini and Prof. D.S. Chauhan, “Hindi Character Recognition Using RBF Neural Network and Directional Group Feature Extraction Technique”, International Conference on Cognitive Computing and Information Processing, IEEE 2015.
[13] Dayashankar Singh, Maitreyee Dutta and Sarvpal H. Singh, “Neural Network based Handwritten Hindi Character Recognition System”, 2nd Bangalore Annual Computer Conference, Article No. 15, ISBN 978-1-60558-476-8.
[14] Ajay Indian, Karamjit Bhatia, “Offline Handwritten Hindi ‘SWARs’ Recognition Using A Novel Wave Based Feature Extraction Method”, International Journal of Computer Science Issues, Volume 14, Issue 4, ISSN: 1694-0814, pp.08-14, July 2017.
[15] K. Radha Revathi1, A.N.L Kumar, Andey Krishnaji, “Neuro Recognizer: Neural Network Based Hand-Written Character Recognition”, International Journal of Computer Sciences and Engineering, E-ISSN: 2347-2693, Volume-3, Issue-9, pp.28-33, Sep.2015.
[16] Siddhartha Banerjee, Bibek Ranjan Ghosh, Arka Kundu, “Handwritten Character Recognition from Bank Cheque”, International Journal of Computer Sciences and Engineering, E-ISSN: 2347-2693, Vol.-4(1), pp.99-104, Feb 2016.
[17] Shen-Wei Lee and Hsien-Chu Wu, “Effective Multiple-features Extraction for Off-line SVM-Based Handwritten Numeral Recognition”, International Conference on Information Security and Intelligence Control, IEEE, pp. 194-197, 2012.
[18] G Raju, Bindu S Moni and Madhu S. Nair, "A Novel Handwritten Character Recognition System using Gradient-based Features and Run Length Count", Indian Academy of Sciences, Vol. 39, Issue 6, pp. 1333–1355, December 2014.
[19] L. Heutte, J. V. Moreau, T. Paquet, Y. Lecourtier, and C. Olivier, “Combining Structural and Statistical Features for the Recognition of Handwritten Characters,” Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria, Vol. 2, pp. 210-214, 1996.
[20] S. Arora, D. Bhattacharjee, M. Nasipuri, D. K. Basu and M. Kundu, "Combining multiple feature extraction techniques for handwritten Devanagari character recognition," IEEE Region 10 Colloquium and 3rd International Conference on Industrial and Information Systems, Dec. 2008.
[21] Y. Kimura, A. Suzuki, K. Odaka, “Feature Selection for Character Recognition using Genetic Algorithm,” IEEE Fourth International Conference on Innovative Computing, Information and Control (ICICIC), Kaohsiung , pp. 401-404, Dec. 2009.

Ajay Indian, Karamjit Bhatia, "A Combinational Approach of Feature Extraction for Offline Handwritten Hindi Numeral Recognition," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1-8, 2018.

In this paper, Multilayer Neuron model is used for classification of nonlinear problems. This conventional neuron model, is been taken for the analysis of while using different data sets. It is found, the Multilayer Neuron model showing its varying efficiency according to pattern of dataset. For analysis of model, various parameters of Artificial Neural Network like numbers of hidden neuron, number of attributes, learning rate, correlation coefficient, numbers of iteration, time elapse in training, mean square error etc. are being taken. After the analytical observation considering above various mentioned parameters, it is observed that there is no thump rule on behalf we can say that Multilayer Neuron Model follow the particular rule. The learning of model depends on the pattern of the dataset and the quality of data.

Multilayer Neuron, Classification, , analysis, Class

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[3] Abhishek Yadav “Dynamical aspects and learning in Biological Neuron Models” department of electrical engineering IIT Kanpur June 2005
[4] M. Balasubramanian, M. Fellows and V. Raman, unpublished
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[12] Deepak Mishra, Abhishek Yadav, & Prem K. Kalra, A Novel Neural Network Architecture Motivated by Integrate-And-Fire Neuron Model Department of Electrical Engineering Indian Institute of Technology Kanpur, India
[13] Deepak Mishra, Abhishek Yadav, & Prem K. Kalra, A Novel Multiplicative Neural Network Architecture Motivated by Spiking Neuron Model Department of Electrical Engineering Indian Institute of Technology Kanpur, India
[14] A. Yadav *, D. Mishra, R.N. Yadav, S. Ray, P.K. Kalra, Time-series prediction with single integrate-and-fire neuron, Science Direct, Applied Soft Computing 7 (2007) 739–745
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[17] Peter Dayan and L F Abbott, Theoretical Neuroscience “Computational and Mathematical modeling of Neural System, MIT Press Cambridge, London.
[18] Pankaj K. Kandpal, Ashish Mehta, comparison analysis of single Multiplicative neuron with conventional neuron model, IJET,660-666(2017), 2249-3255.
[19] Pankaj K. Kandpal, Ashish Mehta, comparison and analysis of Multiplicative neuron and Multilayer Perceptrons using three different datasets,IJERGS, Vol. 05, issue 03, 2017
[20] Pankaj K. Kandpal, Ashish Mehta, compatitive analysis of different Arificial neuron models,IJSAE, VOL. 05, Issue 07, Jul-2017.

Pankaj Kumar Kandpal, Ashish Mehta, "Analytical Observation for classification of Multilayer Neuron Models using different datasets," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.9-15, 2018.

The rapid development of internet communication has increased the rate at which digital data is being shared wideley as the digital data can be flawlessly copied and distributed over the internet. A Digital watermarking is a process in which a bit of information is added to the digital data like image video and audio. A CDMA based image watermarking method in Discrete Fourier Transform (DFT) is proposed in this paper for protecting the copyright and authentication of the digital image. The original digital image is divided into a non-overlapping block of 8X8 and one bit of binary information is embedded in the block using two highly uncorrelated Pseudo-random sequences in the coefficient of the magnitude domain of DFT. For watermark extraction, the original image is not required. Based on the correlation values between magnitude coefficient and Pseudo-random sequences the watermark is extracted from the watermarked image. The scheme is tested using Stimark Benchmarking tools. The Experimental result suggested that the method is robust against numbers of digital watermarking attacks.

Discrete Fourier Transform (DFT), CDMA Image Watermarking, Peak Signal to Noise Ratio (PSNR), Normalized Correlation (NC)

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Ningombam Jimson, Kattamanchi Hemachandran, "Digital Image Watermarking in Discrete Fourier Transform domain," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.16-22, 2018.

An Automatic Summary generation process creates a shortened version of the text using a Digital programming Technology, with the aim of holding the most advanced important points of the original text. In a Common Law system, previous judgments were referred to the current case arguments as well as decision making. Thus there is a need to view the previous judgments and to grasp and analyze the important points present in the legal judgments. Text Summarization technique helps the legal experts to read the key points present in a judgment just by reading the Head note generated by the system. Such techniques save the time as well as the manpower. In this paper, an automatic Legal Judgment Summarization system was implemented and tested by Fuzzy Logic, Classification and Segmentation techniques among that based on the experimental study Fuzzy Logic and Conditional Random Field Algorithm produces a meaningful summary.

Classification, CRF, LDA, Fuzzy Logic, Legal Judgement

[1] Mohammed Zaki J, Wagner Meira, “Data Mining and Analysis: Fundamental Concepts and Algorithms”, Cambridge University Press, 2014.
[2] Mani I, Maybury M, “Advances in Automatic Text Summarization”, Cambridge MIT Press, 1999.
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[4] Patil M.S, “A Hybrid Approach for Extractive Document Summarization Using Machine Learning and Clustering Technique”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (2), PP: 1584-1586, 2014.
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[7] Nenkova, Lucy Vanderwende, and Kathleen McKeown. "A compositional context sensitive multi-document summarizer: exploring the factors that influence summarization". In SIGIR 2006, New York, NY, USA, ACM, PP: 573-580, 2006.

[8] Kyoomarsi F, Khosravi H, Eslami E and Davoudi M, “Extraction-Based Text Summarization using Fuzzy Analysis”, Iranian Journal of Fuzzy Systems Vol. 7, No. 3, pp. 15-32, 2010.
[9] Ladda Suanmali, Naomie Salim, and Mohammed Salem Binwahlan, "Fuzzy Logic Based Method for Improving Text Summarization", (IJCSIS) International Journal of Computer Science and Information Security, Vol. 2, No. 1, 2009.

[10] Ravi Kumar V and Raghuveer K, “Legal Documents Clustering using Latent Dirichlet Allocation”, International Journal of Applied Information Systems, 2012, P: 34-37
[11] Pramod Pardeshi, Ujwala Patil, "Fuzzy Association Rule Mining- A Survey", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.6, pp.13-18, 2017.
[12] A. Yadav, V.K. Harit, "Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.1-7, 2016
[13] Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, "Sentiment Analysis of Twitter Streaming Data for Recommendation using, Apache Spark", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017.

S. Santhana Megala, "Classification of Legal Judgement Summary using Conditional Random Field Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.23-33, 2018.

Same secret key is used for encryption and decryption in Private Key cryptography. Many of the existing private key cryptography systems lacking their security as distribution of the private-key through public communication channel without interpretation is very hard to achieve. So here our focus is on the secret procedure which retrieves secret value used for encryption from the private-key rather than securing the actual private-key value. The secret value is being derived by taking the first or second item from Nth pair of Amicable number set. Where N is a user defined positive integer in the range of (1<=N<=1024). The counting of Nth pair is being started from a user defined base value towards its forward or backward direction. We impose another level of security by implementing some user defined sequence for positioning encrypted characters block wise in cipher text file. Thus an attempt is made to increase the security in great extant.

Amicable number, private key encryption, block cipher, character’s positioning sequence

[1] J. K. Mandal and Mangalmay Das, “Fibonacci Based Position Substitution (FBPS) Encoder for Secured Message Transmission”, IEEE International Advance Computing Conference (IACC) Patiala, India, pp.964-970, 6-7 March 2009.
[2] Udepal Singh and Upasna Garg, “An ASCII value based text data encryption System”, “International Journal of Scientific and Research Publications ( ISSN 2250-3153)”, Volume 3, Issue 11, pp. 1-5,November 2013.
[3] Laiphrakpam Dolendro Singh and Khumanthem Manglem Singh, “Implementation of Text Encryption using Elliptic Curve Cryptography”, “Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015)”,” Procedia Computer Science”, doi: 10.1016/j.procs.2015.06.009, Volume 54, pp. 73-82,2015.
[4] Nishtha Mathur and Rajesh Bansode, “AES Based Text Encryption Using 12 Rounds with Dynamic Key Selection”, “7th International Conference on Communication, Computing and Virtualization 2016”,” Procedia Computer Science”, doi: 10.1016/ j.procs. 2016 .03.131, Volume 79, pp. 1036-1043, 2016.
[5] Snehal Sherkhane, Amit Waghmare, Suraj Dalvi, Shreya Bamne, “Hybrid Data Encryption using Colour code and Armstrong number”, “International Journal of Engineering Science and Computing”, Volume 7, Issue 4, pp. 10300-10305,April,2017.
[6] Kiran Kumar R and Bharathi Devi P, “A Novel Text Encryption Algorithm Using DNA ASCII Table With A Spiral Approach”, “International Journal of Recent Scientific Research”, Volume 9, Issue 1, pp. 23588-23595, DOI: 10.24327/ijrsr.2018.0901.1495,2018.
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[10] Private Key cryptography on text [online] Available: https://koolspan.com/private-key-encryption/.

R. Das, S. Dutta, "A Private Key Encryption Scheme based on Amicable Number with User defined Cipher Block Sequencing Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.34-41, 2018.

The increase of digitization has led to growing concerns over preserving privacy of sensitive data. The ubiquity of sensitive information in data sources such as financial transactions, commercial transactions, medical records, network communication etc., steered towards development of different privacy preserving techniques. In this paper, a novel data transformation technique has been proposed for providing efficient privacy preservation in the data. Inorder to provide privacy to data, the numeric attributes are transformed to the range [-1,1] while the characters or strings are transformed to binary strings. Data analysis over the transformed dataset provides the same result as that of the original dataset. The performance of the data transformation technique is evaluated on the datasets before and after transformation. Experiments on five standard datasets indicate high data utility of the proposed technique. The proposed technique is also evaluated on the standard network intrusion dataset NSL-KDD dataset to study the effectiveness of the proposed technique in intrusion detection domain and the results are analyzed. Privacy measures are evaluated to ascertain the degree of privacy offered by the proposed technique.

Privacy Preservation, PPDM, Data Transformation, Network Intrusion Detection, Data Mining

[1] Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer,Muthuramakrishnan Venkitasubramaniam,
"l-diversity: Privacy beyond k-anonymity", ACM Transactions on Knowledge Discovery from Data (TKDD), Vol.1,No.1,pp.1-12,2007.
[2] Ninghui Li, Tiancheng Li and Suresh Venkatasubramanian,“t-closeness: Privacy Beyond k-anonymity and l-diversity”, IEEE 23rd International Conference on Data Engineering,IEEE, pp.1-10, 2007.
[3] A. Hussien, N. Hamza and H. Hefny, "Attacks on anonymization-based privacy-preserving: a survey for data mining and data publishing",Journal of Information Security, Vol. 4, No. 2, pp. 101-110, 2013.
[4] Yu Zhu and Lei Liu, "Optimal randomization for privacy preserving data mining", Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining,ACM, pp.761-766, 2004.
[5] Swapnil Kadam and Navnath Pokale, “Preserving Data Mining through Data Perturbation”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Vol. 4, No. 11, pp. 4128-4131,2015.
[6] Ashish. E. Mane and Sushma Gunjal, “Privacy preserving using additive perturbation based on multilevel trust in relational streaming data”, Multidisciplinary Journal of Research in Engineering and Technology(MJRET), Vol. 2, No. 2, pp. 392-397,2015.
[7] Wenliang Du and Mikhail J.Atallah, “Secure multy-party computation problems and their applications: a review and open problems”, Proceedings of the 2001 workshop on new security paradigms, ACM, pp. 13-22, 2001.
[8] Benny Pinkas,“Cryptographic techniques for privacy-preserving data mining”, ACM Sigkdd Explorations Newsletter,Vol. 4,No. 2, pp. 12-19, 2002.
[9] Syed Md. Tarique Ahmad, Shameemul Haque and Prince Shoeb Khan, ”Privacy Preserving in Data Mining by Normalization”, International Journal of Computer Applications, Vol. 96, No. 4, pp. 14-18, 2014.
[10] C.Saranya and G.Manikandan. ”A Study on normalization techniques for privacy preserving data mining”, International Journal of Engineering and Technology (IJET), Vol. 5, No.3, pp. 2701-2704, 2013.
[11] Yogendra Kumarjain and Santoshkumar Bhandare,” Min max normalization based data perturbation method for privacy protection”, International Journal of Computer & Communication Technology (IJCCT), Vol. 2, No. 8, pp. 45-50, 2011.
[12] Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, Ian H. Witten ,“The WEKA Data Mining Software: An Update”, SIGKDD Explorations, Vol. 11, No. 1, 2009.
[13] Vatsalan, Dinusha, Peter Christen and Erhard Rahm, "Scalable Multi-Database Privacy-Preserving Record Linkage using Counting Bloom Filters", arXiv preprint arXiv:1701.01232, 2017.
[14] Hillol Kargupta,Souptik Datta,Qi Wang and KrishnaMoorthy, ”Random-data perturbation technique and privacy-preserving data mining”, IEEE International Conference on Data Mining,IEEE, pp. 1-19, 2003.
[15] K.Muralidhar and R.Sarathy, “Perturbation methods for protecting numerical data: Evolution and evaluation”, Proceedings of the 5th Security Conference, 2006.
[16] Pirangela Samarati and Latanya Sweeney, “Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression”, Technical report, SRI International, pp. 1-19, 1998.
[17] Keke Chen and Ling Liu, “Privacy preserving data classification with rotation perturbation”, In Proceedings of the 5th IEEE International Conference on Data Mining (ICDM’05), IEEE, pp. 589–592, 2005.
[18] Zhengli Huang, Wenliang Du and Biao Chen.” Deriving private information from randomized data”, In Proc. of ACM SIGMOD’05, pp. 37-48, 2005.
[19] Rakesh Agrawal and RamaKrishnan Srikant, ”privacy preserving data mining”, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Vol. 29, No. 2, pp. 439-450, 2000.
[20] Li Liu, Murat Kantarcioglu and Bhavani Thuraisingham, “The applicability of the perturbation model-based privacy preserving data mining for real-world data”, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW`06), pp. 6-21, 2006.
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[22] Huy Anh Nguyen and Deokjai Choi, “Application of data mining to network intrusion detection: classifier selection model”, Asia-Pacific Network Operations and Management SymposiumSpringer Berlin Heidelberg, pp. 399-408, 2008.
[23] Phurivit Sangkatsanee, Naruemon Wattanapongsakorn and Chalermpol Charnsripinyo, “Real-time Intrusion Detection and Classification”, IEEE network, 2009.
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[26] Shuting Xu,Jun Zhang,Dianwei Han and Jie Wang, "Data distortion for privacy protection in a terrorist analysis system", International Conference on Intelligence and Security Informatics, Springer Berlin Heidelberg, pp.459-464, 2005.
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Uma Shankar Rao Erothi, Sireesha Rodda, "Data Transformation Technique for Preserving Privacy in Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.42-50, 2018.

There are many cryptographic techniques available for providing a secure communication. Encryption technique can be classified according to their encrypting process. They are substitution cipher and transposition cipher. Polyalphabetic cipher is based on substitution technique- the plaintext letters are encrypted differently depending upon their placement in the text and the keyword. Vigenere cipher is considered to be the most efficient and simplest Polyalphabetic substitution cipher. Due to its repeating nature of the keyword, it is vulnerable to attacks. To overcome this, here we are presenting a new cipher which uses multiple random Tables (26x26) for encryption. In this proposed cipher, the keyword is repeating until it is equal to the length of the plaintext. But here, whenever the keyword repeats, this cipher generates different 26x26 random tables for encryption. Also, each table will be completely independent of the previous table. So this proposed Polyalphabetic cipher is unbreakable.

Polyalphabetic Cipher, Vigenere Cipher, Vigenere Table, Kasiski Method, Index of Coincidence IC

[1] Sakshi Dubey, Darpan Anand, Jayash Sharma, "LFSR Based Block Cipher Technique for Text” International Journal of Computer Sciences and Engineering, Vol.6, Issue.2, pp.54-60, 2018.
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[6] Ranju S Kartha, Dr. Varghese Paul, “ Survey: Recent Modifications in Vigenere cipher”, IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 2, Ver. IX, Mar-Apr. 2014.
[7] Abdul Razzaq and Yasir Mahmood, Farooq Ahmed, “Strong Key Mechanism Generated by LFSR based Vigenère Cipher”, The 13th International Arab Conference on Information Technology, ACIT`’2012, Dec.10-13
[8] Alfred J. Menezes, Paul C. van Oorschot, Scott A., “Handbook of Applied Cryptography”, CRC Press, 1996.
[9] Navnish Goel, Amitabh Maurya, Babloo Kumar, “Information Security: Encryption and Decryption with Polyalphabetic Substitution Method”, International Journal of Computer Science and Communication, Vol. 2, January-June 2011.
[10] Prof.Ravindra Babu Kallam, Dr. S.Udaya Kumar, Dr. A.Vinaya babu, V.Shravan Kumar, “A Contemporary Polyalphabetic Cipher using Comprehensive Vigenere Table”, World of Computer Science and Information Technology Journal, Vol. 1, 2011.

Ranju S Kartha, Varghese Paul, "Polyalphabetic Substitution Cipher Using Multiple Random Table," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.51-58, 2018.

Data mining is that the method of move queries and extracting patterns, typically antecedently unknown from giant quantities of data using pattern matching several applications in security as well as for national security likewise as for cyber security. Research focus on Detecting Malicious Packet uses weka. Once network routers are a unit subverted to act during a malicious fashion. To observe the existence of compromised routers during a network, then take away them from the routing fabric. Our approach is to separate the matter into three sub-problems: 1) crucial the traffic data to record upon that to base the detection, 2) synchronizing routers to gather traffic data and distributing this data among them thus detection will occur, and 3) taking countermeasures once detection happens. Experimental results show that ready to observe and isolate a spread of malicious router actions with acceptable overhead and quality. Our work has ready to tolerate attacks on key network infrastructure elements.

Data mining, Malicious program, JRip, PART, OneR, Malicious classifier, classification, WEKA tool

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[20] Bhavani Thuraisingham, “Data Mining for Security Application”s, IEEE/IFIP International Conference on Embedded and Ubiquitous Computing,2008 .
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K. Thyagarajan, N. Vaishnavi, "Performance Study on Malicious Program Prediction Using Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.59-64, 2018.

Demand of energy in isolated parts of India is solved by extension of grid power supply but it is not economical at all as cost varies depending upon distance,land and load demand.In view of this problem,supply of power to remote area demands advanced skill with updated technical and economical strategies.Because of that expensive and insufficient grid power in rural places have been replaced by renewable energy sources.So this particular work chooses the best hybrid technology for rural electric generation for a village area in Bhubaneswar. The solution obtained from using HOMER software presents the economic feasibility of the hybrid generation system for a rural conglomerate in Ghatikia, Bhubaneswar with latitude 20.26 0 N and longitude 85.76 0E.This paper contains four different type of Hybrid configuration. The optimization result obtained by using a hybrid configuration composed of a wind energy system, a solar PV system and a diesel generator used as a backup system.

HOMER,Microgrid,TechnoEconomicAnalysis

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Sthitapragyan Mohanty, "Standalone Hybrid Power System for a Rural Destination in India: An Economic Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.65-72, 2018.

WiMax stands for World Wide Interoperability for Microwave Access. It is subjected on IEEE 802.16 air interface. WiMax enhances point to point and point to multipoint wideband wireless access and meets the requirements of millions of users demanding high speed at affordable cost. This paper presents four routing protocols namely Ad hoc On-Demand Distance Vector (AODV), Dynamic Source Routing (DSR), Optimized Link State Routing (OLSR), Zone Routing Protocol (ZRP) in different node density scenarios of (25, 50, 75 and 100). Their performance comparison is done by taking throughput, average jitter, and average end to end delay as performance metrics. All the scenarios are subjected on random way point mobility. Results are generated using QualNet simulator. This paper also presents a comparison of CBR and VBR traffic application taking protocol OLSR at 100 nodes.

WiMax, Routing protocols, jitter, throughput, delay

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[9]. Kirti Mor, Suresh Kumar, &Deepak Sharma, “Ad-Hoc Wireless Sensor Network Based on IEEE 802.15.4: Theoretical Review”. International Journal of Computer Sciences and Engineering. Vol.6. Issue 3, pp (219-224) March 2018. ISSN 2347-2693.doi: 10.26438/ijcse/v6i3.219224.
[10]. Sapna Khurana ,Dr. (Col.) Suresh Kumar, Deepak Sharma, “Performance Evaluation of Congestion Control in MANETs using AODV, DSR and ZRP Protocols”, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE),Vol. 7,Issue 6 June 2017 ,pp(398-403) ISSN: 2277 128X. DOI: 10.23956/ijarcsse/V7I6/0218 .
[11]. Deepak Sharma, Col. (Dr) Suresh Kumar, “A Comprehensive Review of Routing Protocols in Heterogeneous Wireless Networks”, International Journal of Enhanced Research in Management & Computer Applications, Vol. 4 Issue 8, Aug-2015,pp(105-121) ISSN: 2319-7471
[12]. Preeti Boora, Deepak Sharma Shamsher Malik, , “A Review of Routing Protocols in Wireless Sensor Network” International Journal of Enhanced Research in Management & Computer Applications, Vol. 4 Issue 5, May-2015,pp(34- 40) ISSN: 2319-7471
[13]. Deepak Sharma, “An overview of Wireless Sensor Networks” International Journal of Enhanced Research in Management & Computer Applications, Vol. 4 Issue 4, April-2015,pp(47-51) ISSN: 2319-7471.
[14]. Subramanya Bhat.M, Shwetha .D , Devaraju .J.T, “Performance study of proactive reactive and hybrid protocols using qualnet stimulator” International Journal of Computer Applications (0975 – 8887)Volume 28– No.5, August 2011
[15]. Payal, Deepak Sharma, Col. (Dr) Suresh Kumar, “Performance Evaluation of Reactive Routing Protocols Using IEEE 802.15.4 Application in Designed Wireless Sensor Network.”International Journal of Computer Sciences and Engineering. Vol.6. Issue 4, pp (90-96) March 2018. ISSN 2347-2693.doi: 10.26438/ijcse/v6i4.9096.

Nidhi Singh, Anil Sangwan, Harkesh Sehrawat, Rajat Malik, "Performance Evaluation of Routing Protocols in WiMax," International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.73-79, 2018.