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To ensure secure communication on the network, the principles of confidentiality, integrity and accessibility must be carried out in unity. To prevent external attacks on the network, many threats are eliminated by using firewalls between the external network and the internal network. However, different security scenarios need to be implemented against threats that may come from within the local area networks. For this reason, the factors that may cause security vulnerabilities in the internal network should be checked and the security should be considered. One of the most important problems encountered here is the unnecessary network traffic that occurs in internal networks, slowing the system and making it inoperable. In this article, using the GNS3 (Graphical Network Simulator) network simulation program, MAC (Media Access Control) flood, DHCP (Dynamic Host Configuration Protocol) starvation and spoofing and Arp (Address Resolution Protocol) poisoning attacks are detected and a working example has been carried out to prevent it.

GNS3, Network Security, MAC flood, DHCP attacks, ARP poisoning

[1] Singer, P. W., Friedman, A, Cybersecurity: What everyone needs to know. Oxford University Press. pp.102-104, 2014.
[2] Bella, G., Bistarelli, S. , “Soft constraints for security protocol analysis: Confidentiality. In International Symposium on Practical Aspects of Declarative Languages”. Springer, Berlin, Heidelberg,pp. 108-122, 2001.
[3] Heberlein, L. T., Dias, G. V., Levitt, K. N., Mukherjee, B., Wood, J., Wolber, D, “A network security monitor. In Research in Security and Privacy”, Proceedings., IEEE Computer Society Symposium on, pp. 296-304,1990.
[4] Wullems, C., Tham, K., Smith, J., Looi, M., “A trivial denial of service attack on IEEE 802.11 direct sequence spread spectrum wireless LANs”, In Wireless Telecommunications Symposium, pp. 129-136, 2004.
[5] Chordiya, A. R., Majumder, S., Javaid, A. Y., “Man-in-the-Middle (MITM) Attack Based Hijacking of HTTP Traffic Using Open Source Tools.”, In 2018 IEEE International Conference on Electro/Information Technology (EIT), pp. 438-443, 2018.
[6] Nayak, G. N., Samaddar, S. G., “Different flavours of man-in-the-middle attack, consequences and feasible solutions.”, In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on, Vol. 5, pp. 491-495, 2010.
[7] Ansari, A., Waheed, M. A., “Flooding attack detection and prevention in MANET based on cross layer link quality assessment.”, In Intelligent Computing and Control Systems (ICICCS), International Conference on IEEE, pp. 612-617,2017.
[8] Tripathi, N., Hubballi, N. , “Exploiting DHCP server-side ip address conflict detection: A dhcp starvation attack.”, In Advanced Networks and Telecommuncations Systems (ANTS), International Conference on IEEE, pp. 1-3, 2015.
[9] Aggarwal, R. K., “A Security Approach and Prevention Technique against ARP Poisoning.”, In International Conference on Information and Communication Technology for Intelligent Systems, Springer, Cham, pp. 39-49, 2017.
[10] Kumar, S., & Tapaswi, S., “A centralized detection and prevention technique against ARP poisoning.”, In Cyber Security, Cyber Warfare and Digital Forensic (CyberSec), International Conference on IEEE, pp. 259-264, 2012.
[11] Khurana, S., Kaur, R.: A security approach to prevent ARP poisoning and defensive tools. Int. J. Comput. Commun. Syst. Eng. (IJCCSE), 2(3), 431–437, 2015.
[12] Guha, R. K., Furqan, Z., Muhammad, S., “Discovering man-in-the-middle attacks in authentication protocols.”, In Military Communications Conference, MILCOM, IEEE, pp. 1-7, 2007.
[13] Conti, M., Dragoni, N., Lesyk, V., “A Survey of Man In The Middle Attacks.”, IEEE Communications Surveys & Tutorials, pp. 2035, 2016.
[14] Yılmaz, T., Karaarslan, E., Akın, G., “GNS3 Tabanlı Ağ Emülasyonlarının Bilgisayar Ağları Eğitiminde Kullanımı: Senaryolar ve Öneriler”, Researchgate, 2013.

Sezer YILDIZ, Umut ALTINIŞIK, "Detecting and Preventing Cyber Attacks on Local Area Networks : A Working Example," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.1-6, 2018.

Machine Learning Algorithm aims at providing computational method for accumulating, changing and updating knowledge in health care systems. In particular learning mechanism will assist us to procure knowledge from the data set. The classification of machine learning algorithm is used not only to detect diseases, but also measure better fidelity. This article emphasizes on codification of disease symptoms on thyroid disease among the public. Thyroid disease is rampant worldwide. There are feasibility of thyroid disease and disorder including thyroiditis and thyroid cancer. We used 7200 sample thyroid dataset from the University of California Irvine Machine Learning Repository, a large and highly imbalanced dataset that comprises both discrete and continuous attributes. In this work, we collate machine learning classifiers such as Logistic Regression, Linear Discriminant Analysis, Naive Bayes, k-Nearest Neighbours, Classification and Regression Tree, Support Vector Machine using python to classify the disease symptoms. This work is carried out using different classifiers to achieve more verisimilitude. The selected algorithms are evaluated using five performance metrics namely accuracy, sensitivity, specificity, F1-score and kappa, and also estimated from the confusion matrix produced by the selected classifier.

CART Decision Tree; KNN algorithm; Support Vector Machine; Thyroid Disease Diagnosis; Linear Regression; Linear Discriminant Analysis

[1] Li.LN.ouyang JH,Chen HL,Liu DY. A computer aided diagnosis system for thyroid disease using extreme learning machine. J Med Syst 2012;36:3327-37.
[2] Chen HL, Yang B, Wang G, Liu J Chen YD, Liu DY. A three stage expert system based on support vector machines for thyroid disease diagnosis. J Med Syst 2012;36;1953-63.
[3] Dogantekin E.Dogantekin A,Avci D. An expert system based on Generalized Discriminant analysis and Wavelet Support Vector Machine for diagnosis of thyroid disease. Expert Syst Appl 2011;38:146-50.
[4] Dogantekin E.Dogantekin A,Avci D. An Automatic diagnosis system based on thyroid gland; ADSTG. Expert Syst. Appl 2010;37;6368-72.
[5] Pasi L Similarity classifier applied to medical data sets. 2004. 10 sivua. Fuzziness in Finland ’04. International conference on soft computing, Helsinki. Estonia: Finland & Gulf of Finland & Tallinn; 2004.
[6] Feyzul lah Temurtas” A comparative study on thyroid disease diagnosis using neural networks”, Elsevier, Expert Systems with Applications 36 (2009) 944–949.
[7] Serpen G. Jiang H, Allred L. Performance analysis of probabilistic potential function neural network classifier. In Proceedings of artificial neural networks in engineering conference viol. 7 : 1997. p.471-6.
[8] Ozyilmaz, L., Yildirim,T.(2002). Diagnosis of thyroid disease using artificial neural network methods. In proceedings of ICONIP’02 9th international conference on neural information processing (pp.2033-2036). Singapore: Orchid Country Club.
[9] Liu DY. Chen HL Yang B.XE L, Li LN, Liu J. Design of an enhanced fuzzy k-nearest neighbour classifier based computer aided diagnostic system for thyroid disease. J Med Syst 2012;36:3243-4354.
[10] Ng SK. McLachlan GJ. Extension of mixture-of-experts networks for binary classification of hierarchical data. Artif Intell Med 2007;41:57-67.
[11] Chang WW.Yeh WC, Huang PC, A hybrid immune-estimation distribution of algorithm for mining thyroid gland data. Artif Intell Med 2007;41:57-67.
[12] Kodaz H. Ozsen S,Arslan A,Gunes S. Arslan A, Gunes S, Medical application of information gain based artificial immune recognition system(AIRS): Diagnosis of thyroid disease. Expert syst. Appl 2009;36:3086-92.
[13] Keles A.Keles A.ESTDD: expert system for thyroid disease diagnosis.Expert Syst Appl. 2008;34:242-6.
[14] Duch W.Adamezak R. Grabezewski K. A new methodology of extraction,optimization and application of crisp and fuzzy logic rules. IEEE Trams Neural Netw 2001;12:277-306.
[15] Bologna G. A model for single and multiple knowledge based networks. Artif Intell Med 2003;28:141-63.
[16] Abe S.Thawonmas R. A fuzzy classifier with ellipsoidal regions. IEEE Trans Fuzzy Syst 1997;5:358-63.
[17] Abe S. Thawonmas R, Kayama M, A fuzzy classifier with ellipsoidal regions for diagnosis problems. IEEE Trans Syst Man Cybern C Appl Rev 1999;29:140-9.
[18] Sharaf-EI-Deen DA, Moawad IF,Khalifa ME. A new hybrid case-based reasoning approach for medical diagnosis systems. J Med Syst 2014;38;9-19.
[19] Falco ID. Differential evolution for automatic rule extraction from medical databases. Appl soft Comput 2013;13;1265-83.
[20] Zhu P.Hu Q. Rule extraction from support vector machine based on consistent region covering reduction. Knowl Based Syst 2013;42;1-8.
[21] Napierala K. Stefanowski J. BRACID: a comprehensive approach to learning rules from imbalanced data. J Intell Inf Syst 2012;39;335-73.s
[22] Yongchuan Tang, Wuming Pan, Haiming Li and Yang Xu. Fuzzy Naïve Bayes Classifier based on fuzzy clustering. IEEE International Conference on System, Man and Cybernetics, Vol.5, 2002.
[23] Yaguang Ji, Songnian Yu and Yafeng Zang. A Novel Naïve Bayes Model: Packaged Hidden Naïve Bayes. Sixth IEEE Joint International Conference on Information Technology and Artificial Intelligence. pp.484-487, 2011.
[24] Park, Hyeoun-Ae, An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain, J Korean Acad Nurs Vol.43 No.2 April 2013
[25] Le,Cessie S, Van Houwelingen JC.Ridge estimators in logistic regression. Applied statistics.1992;p.191-201.doi:10.2307/2347628
[26] Jiawei Han , Micheline Kamber, Data Mining Concepts and Techniques. Published by Elsevier 2006.
[27]University of California, Irvine Learning Repository. (http: //archive.ics.uci.edu/ ml/machine-learning-databases/ thyroid-disease/)
[28] Anupam Shukla, Prabhdeep Kaur, Ritu Tiwari and R.R. Janghel, Diagnosis of Thyroid disease using Artificial Neural Network. In Proceedings of IEEE IACC 2009.
[29] Umar Sidiq, “Comparative Study of Existing Techniques for Diagnosing Various Thyroid Ailments”, Global Journal of Computer Science and Technology: E Network, Web & Security Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 0975-4172 & Print ISSN: 0975-4350
[30] Azar AT,Hassanien AE, Kim T(2012) Expert system based on neural fuzzy rules for thyroid disease diagnosis springer berlin.
[31] Liu B, Fang L, Liu F, Wang X, Chen J, Chou KC. Identification of real microRNA precursors with a pseudo structure status composition approach. PloS one. 2015;10(3):e0121501 doi: 10.1371/journal.pone.0121501 [PMC free article] [PubMed [32]
[32] Liu B, Fang L, Liu F, Wang X, Chou KC. iMiRNA-PseDPC: microRNA precursor identification with a pseudo distance-pair composition approach. Journal of Biomolecular Structure and Dynamics. 2016;34(1):223–235. doi: 10.1080/07391102.2015.1014422 [PubMed]
[34] Shiliang Sun, Rongquing Huang. An adaptive k-nearest neighbor. IEEE Seventh International Conference on Fuzzy System and Knowledge Discovery. pp.91-94, 2010.

[35] Thyroid disease diagnosis via hybrid architecture composing rough data sets theory & machine learning algorithms, V. Prasad, T. Srinivasa Rao, M.Surendra Prasad Babu, Springer, soft comput(2016) 20:1179-1189, DOI 10.1007/s00500-014-1581-5
[36] L.Breiman, J.Friedman, R. Olshen,et al; Classification & Regrssion trees, chapman & Hall, London(1984)
[37] L.Rokach, O.Maimon, classification trees, Data Mining & Knowledge Discovery, Springer New York Dordrecht Heidelberg London,(2010) 149-174
[38] Derrac, J., Chiclana, F., Garcia, S., & Herrera, F.(2016). Evloutionary fuzzy k-nearest neighbours algorithm using interval-valued fuzzy sets. Information sciences, 329, 144-163.
[39] Tom Fawcett, An introduction to ROC analysis, Institute for the study of learning and expertise,2164 staunton court, palo alto, CA 94306, USA,Elsevier
[40] Pontius, Robert; Millones, Marco (2011). "Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment". International Journal of Remote Sensing. 32: 4407–4429.
[41] Galton, F. (1892). Finger Prints Macmillan, London.
[42] Smeeton, N.C. (1985). "Early History of the Kappa tatistic". Biometrics. 41: 795. JSTOR 2531300.
[43] Powers, David M. W. (2012). "The Problem with Kappa" (PDF). Conference of the European Chapter of the Association for Computational Linguistics (EACL2012) Joint ROBUS-UNSUP Workshop.
[44] Nitin Aji Bhaskar. Performance Analysis of Neural Network and Support Vector Machine in Detection of Myocardial Infarction. International Conference on Information and Communication Technologies, pp.20-30, 2015.
[45] Kotsiantis, S. B., D. Kanellopoulos, and P.E. Pintelas. “Data preprocessing for supervised learning” International Journal of Computer Science 1.2(2006):111-117.
[46] Guyon, I., Weston, J., Barnhill, S. and Vapnik, V. Gene selection for cancer classification using support vector machines, Machine Learning, Vol.46, Issue 1-3, Pp.389-422,2002

M. Saktheeswari, T. Balasubramanian, "Performance Analysis of Classifier Models to Predict Thyroid Disease," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.7-14, 2018.

In the present work a generalized queuing model has been developed to investigate the various queuing characteristics in steady state. The model consists of two global servers having three servers each which are connected in tri-cum biserial way. The comprehensive governing equations has been given in mathematical formulation which has been used to find the various output parameters i.e., queue lengths, variances, joint probabilities, traffic intensities, average waiting time for customers. The present model is named a generalized queuing model because several models available in the literature can be developed as the special cases.

Queue length, Average waiting time, Poisson Law, Moment generating function, Probability

[1] R.R.P. Jackson, “Queuing systems with phase-type service”, Operational Research Quarterly, 5, pp. 109-120, 1954.
[2] P.L. Maggu, “Phase type service queues with two servers in biseries”, Journal of Operational Research Society of Japan, Vol. 13, No.1, pp. 6-16, 1970.
[3] K.L. Arya, “System of two servers in biseries with a serial service channel and phase type service” Zeitschrift fur Operations Research, Vol. 16 B, pp. 115-122, 1972.
[4] M. Singh, “Steady state behaviour of serial queuing processes with impatient customers”, Math, Operations forsch. U. statist. Ser., Vol. 15, No.2, pp. 289-298, 1984.
[5] R. Hassin, M. Haviv, “To Queue or Not to Queue: Equilibrium Behavior in Queueing Systems”, International Series in Operations Research & Management Science, Vol. 59, pp. 109-122, 2003.
[6] D. Gupta, T.P. Singh, R. Kumar, “Analysis of a network queue model comprised of biserial and parallel channel linked with a common server” Ultra Science, Vol. 19, No. 2 M, 407-418, 2007.
[7] T.P. Singh, V. Kumar, R. Kumar, “On transient behaviour of a queuing network with parallel biserial queues”, JMASS, Vol.1, No.2, pp.68-75, 2005.
[8] V. Kumar, T.P. Singh, R. Kumar, “Steady state behaviour of a queue model comprised of two subsystems with biserial linked with common channel”, Reflection des ERA., Vol.1, No.2, pp.135-152, 2007.
[9] M.S. El-Paoumy, “On Poisson Bulk Arrival Queue: M X /M / 2 / N with Balking, Reneging and Heterogeneous servers”, Applied Mathematical Sciences, Vol. 2, No. 24, 1169 – 1175, 2008.
[10] S.K. Agrawal, B.K. Singh, “Computation of various queue characteristics using tri-cum biserial queuing model connected with a common server”, International Journal of Mathematics Trends and Technology (IJMTT), Vol. 56, No. 1, pp. 81-90, 2008. doi: 10.14445/22315373/IJMTT-V56P510.
[11] S.K. Agrawal, B.K. Singh, “A Comprehensive study of Various Queue Characteristics using Tri-Cum Biserial Queuing Model”, International Journal of Scientific Research in Mathematical and Statistical Sciences (IJSRMSS), Vol. 5, Issue 2, pp. 46-56, 2008. doi: 10.26438/ijsrmss/v5i2.4656.
[12] S.K. Agrawal, B.K. Singh, “An Investigation of Tri-Cum Biserial Queuing Model Connected with Three Servers”, International Journal of Emerging Technologies and Innovative Research (JETIR), Vol. 5, issue 9, pp. 493-509, 2018. Doi: 10.1729/Journal.18346.
[13] S.K. Agrawal and B.K. Singh, “Influence of Reneging and Jockeying on Various Queuing Characteristics of Tri-Cum Biserial Based Queue Model”, International Journal of Mechanical Engineering and Technology (IJMET), Vol. 9, Issue 10, pp. (1062)-(1073), 2018.

Sachin Kumar Agrawal, B.K. Singh, "Development and Analysis of Generalized Queuing Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.15-32, 2018.

Automatic human authentication by machine has been an active research area.This research work focus on development of Voice and Iris based Multimodal biometric system for human Authentication. The experimental work divided in three part, first and second part develop unimodel Voice and Iris biometric system and third experiment carried out for multimodal biometric fusion .we have achieve above form Multimodal biometric system results using KVKRG Voice and KVKR Iris database is FAR1.4% ,FRR 0.8%, Accuracy is 97.8%, ASV2015 voice and MMU Iris Database is FAR1.0% ,FRR 0.6%, Accuracy is 98.4% , Regional Voice Database and KVKR Iris database is FAR1.1% ,FRR 0.6%, Accuracy is 98.3%.

Multimodel ,Voice ,Iris, MFCC, Fusion

[1] Campbell J.P. and Jr. “Speaker recognition: A Tutorial” Proceeding of the IEEE. Vol 85, 1437- 1462 1997.
[2] DOUGLAS O’SHAUGHNESSY, “Interacting with Computers by Voice: Automatic Speech Recognition and Synthesis”, Proceedings of the IEEE, VOL. 91, NO. 9, September 2003, 0018-9219/03 2003 IEEE.
[3] L.V. Birgale and M.Kokare, ”Iris Recognition Using Discrete Wavelet Transform”, IEEE Proceedings of the International Conference on Digital Image Processing (ICDIP’09), 2009.
[4] Amir azizi, Hamid reza P, “Efficient iris recognition through improvement of feature extraction and subset selection,” international journal of computer science and information security,vol2,no 1 June 2009.
[5] Aloui, K., Naït-Ali, A., Fournier, R. and Naceur, S.,“Introduction to Iris Biometrics, in Signal and Image Processing for Biometrics”, 2012,John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118561911.ch6.
[6] Anil Jain, Karthik Nandakumara, Arun Ross, “Score normalization in multimodal biometric systems” Elsevier Journal of Pattern Recognition, Pattern Recognition 38 (2005) 2270 – 2285,2005.
[7] Kazi M.M. “Multimodal Biometric System Using Face and Signature: A score Level Fusion Approach “Advances in Computational Research, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, 2012, pp.-99-103.
[8] Zhao F., Huang Q. and Gao W. (2006) IEEE Int. Conference on Acoustics, Speech and Signal Processing 2, 14-19.
[9] Ulery B., Fellner W., Hallinan P., Austin Hicklin, Watson C. Studies of Biometric Fusion report, National Institute of Standards and Technology, 1-14. (2006).
[10] Richard M.," Multimodal Biometric Human Recognition for Perceptual Human—Computer Interaction", IEEE, Vol. 40, No. 6, November 2010 .

Vijay K.Kale, Prapti D.Deshmukh, "A Coupling of Voice and Iris Based Multimodal Biometric System for Person Authentication," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.33-38, 2018.

Now days every organization has importance of time in its day to day working. To save time, the task should be executed in distributed manner and also to adopt the procedure to perform, parallel or concurrent execution method. Concurrency means, more than one transactions are performing at the same time then they are interleaving to each other. When the transactions are inter-leaving for short span they cause the different types of problems like lost update, dirty read etc. To control these types of problems, there are several methods like Locking Methods, Time-stamp Methods and Optimistic Methods. In this paper we will study the 2-Phase Locking Protocol which comes under locking method. With the help of 2-PL, we shall reveal how to prevent the problem arise due to concurrency with the help of suitable examples. It will help the students and research scholars to understand that how to prevent the concurrency problems with the help of 2-Phase Locking Protocol (2-PL) method.

2-PL, Growing, Shrinking, Locks, Concurrency, Dirty Read, Lost Update, inconsistent analysis

[1]. Kedeml C.et. al., “An Efficient Deadlock Removal Scheme for Non-Two-Phase LockingProtocols”, Proceedings of the Eighth International Conference on Very Large Data Bases, Mexico City, September, 1982.
[2]. C. MOHAN et. al., “Lock Conversion in Non-Two-Phase Locking Protocols”, IEEE Transactions on Software Engineering, vol. se-11, No. 1, p.p. 15-22, January 1985
[3]. Stefano Ceri, Giuseppe Pelagatti, “ Distributed Database Principles and Systems”, Tata McGraw-Hill Edition, ISBN 0-07-026511-9, p.p. 195
[4]. M. Tamer Ozsu, Patrick Valduriez, “Principles of Distributed Database Systems”,Pearson Education, 2nd Editions, ISBN 81-7758-177-5, p.p. 262 , 2008
[5]. Singh Anil Kumar, “A study of Concurrent transaction execution and their problems in Distributed Database System”, International Journal ofComputer Sciences and Engineering, Volume-6 , Issue-10, Page no. 810-813, ISSN 2347-2693(E), Oct-2018

Anil Kumar Singh, "A study of Preventing Concurrency’s Problems using 2-Phase Locking Protocols (2-PL)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.39-42, 2018.

In recent years, clustering has become well known for various researchers due to various application fields like communication, wireless networking, and biomedical domain and so on. So, much different research has already been made by the researchers to develop an improved algorithm for clustering. An optimization is one of the well-known processes that has been effectively utilized for clustering. In this paper, Invasive weed optimization (IWO) based centroid initialization for fuzzy c-means clustering (FCM) in medical image segmentation (KFCM-IWO-MIS) is proposed. For MRI brain tissue segmentation, KFCM is most preferable technique because of its performance accuracy. The major limitation of the conventional KFCM is random centroids initialization, because it leads to raising the execution time to reach the best resolution. In order to accelerate the segmentation process, IWO is used to adjust the centroids of required clusters. The quantitative measures of results were compared using the metrics are number of iterations and processing time. The number of iterations and processing of KFCM-IWO-MIS method take minimum value while compared to conventional KFCM. The KFCM-IWO-MIS method is very efficient and faster than conventional KFCM for brain tissue segmentation.

clustering, centroid initialization, Invasive weed optimization(IWO), Kernel fuzzy C-means (KFCM), MRI brain tissue segmentation

[1] Pilleron, S., Sarfati, D., Janssen‐Heijnen, M., Vignat, J., Ferlay, J., Bray, F. and Soerjomataram, I., 2018. Global cancer incidence in older adults, 2012 and 2035: A population‐based study. International journal of cancer.
[2] Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D.M., Forman, D. and Bray, F., 2015. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer, 136(5), pp.E359-E386.
[3] Thambi, R. and Kandamuthan, S., 2017. Histopathological Analysis of Brain Tumors-A Seven Year Study from a Tertiary Care Centre in South India. Journal of clinical and diagnostic research: JCDR, 11(6), p.EC05.
[4] Lacroix, M., Abi-Said, D., Fourney, D.R., Gokaslan, Z.L., Shi, W., DeMonte, F., Lang, F.F., McCutcheon, I.E., Hassenbusch, S.J., Holland, E. and Hess, K., 2001. A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. Journal of neurosurgery, 95(2), pp.190-198.
[5] Sanai, N., Polley, M.Y., McDermott, M.W., Parsa, A.T. and Berger, M.S., 2011. An extent of resection threshold for newly diagnosed glioblastomas. Journal of neurosurgery, 115(1), pp.3-8.
[6] McGirt, M.J., Mukherjee, D., Chaichana, K.L., Than, K.D., Weingart, J.D. and Quinones-Hinojosa, A., 2009. Association of surgically acquired motor and language deficits on overall survival after resection of glioblastoma multiforme. Neurosurgery, 65(3), pp.463-470.
[7] Kut, C., Chaichana, K.L., Xi, J., Raza, S.M., Ye, X., McVeigh, E.R., Rodriguez, F.J., Quiñones-Hinojosa, A. and Li, X., 2015. Detection of human brain cancer infiltration ex vivo and in vivo using quantitative optical coherence tomography. Science translational medicine, 7(292), pp.292ra100-292ra100.
[8] Rygh, O.M., Selbekk, T., Torp, S.H., Lydersen, S., Hernes, T.A.N. and Unsgaard, G., 2008. Comparison of navigated 3D ultrasound findings with histopathology in subsequent phases of glioblastoma resection. Acta neurochirurgica, 150(10), p.1033.
[9] Ji, M., Orringer, D.A., Freudiger, C.W., Ramkissoon, S., Liu, X., Lau, D., Golby, A.J., Norton, I., Hayashi, M., Agar, N.Y. and Young, G.S., 2013. Rapid, label-free detection of brain tumors with stimulated Raman scattering microscopy. Science translational medicine, 5(201), pp.201ra119-201ra119.
[10] Özduman, K., Yıldız, E., Dinçer, A., Sav, A. and Pamir, M.N., 2014. Using intraoperative dynamic contrast-enhanced T1-weighted MRI to identify residual tumor in glioblastoma surgery. Journal of neurosurgery, 120(1), pp.60-66.
[11] Zausinger, S., Scheder, B., Uhl, E., Heigl, T., Morhard, D. and Tonn, J.C., 2009. Intraoperative computed tomography with integrated navigation system in spinal stabilizations. Spine, 34(26), pp.2919-2926.
[12] Muñoz, X., Freixenet, J., Cufı, X. and Martı, J., 2003. Strategies for image segmentation combining region and boundary information. Pattern recognition letters, 24(1), pp.375-392.
[13] Krinidis, S. and Chatzis, V., 2010. A robust fuzzy local information C-means clustering algorithm. IEEE Transactions on Image Processing, 19(5), pp.1328-1337.
[14] Shi, J. and Malik, J., 2000. Normalized cuts and image segmentation. IEEE Transactions on pattern analysis and machine intelligence, 22(8), pp.888-905.
[15] [15]. Andersson, M., Gudmundsson, J. and Levcopoulos, C., 2004. Approximate distance oracles for graphs with dense clusters. Lecture notes in computer science, 3341, pp.53-64.
[16] Govaert, G. and Nadif, M., 2005. An EM algorithm for the block mixture model. IEEE Transactions on Pattern Analysis and machine intelligence, 27(4), pp.643-647.
[17] Wang, X.F. and Huang, D.S., 2009. A novel density-based clustering framework by using level set method. IEEE Transactions on knowledge and data engineering, 21(11), pp.1515-1531.
[18] Zhang, J. and Kerekes, J., 2015. An adaptive density-based model for extracting surface returns from photon-counting laser altimeter data. IEEE Geoscience and Remote Sensing Letters, 12(4), pp.726-730.
[19] Ester, M., Kriegel, H.P., Sander, J. and Xu, X., 1996, August. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd (Vol. 96, No. 34, pp. 226-231).
[20] Omrani, A. and Santhisree, K., 2011, May. Clustering sequential data with OPTICS. In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on (pp. 591-594). IEEE.
[21] De Marsico, M., Nappi, M., Riccio, D. and Wechsler, H., 2013. Robust face recognition for uncontrolled pose and illumination changes. ieee transactions on systems, man, and cybernetics: systems, 43(1), pp.149-163.
[22] Sheng, W., Chen, S., Xiao, G., Mao, J. and Zheng, Y., 2015. A biometric key generation method based on semisupervised data clustering. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45(9), pp.1205-1217.
[23] Zhu, L., Chung, F.L. and Wang, S., 2009. Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39(3), pp.578-591.
[24] J.C.Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Kluwer Academic Publishers, Norwell, MA, USA, 1981.
[25] Yang, Y., Zheng, C. and Lin, P., 2005. Fuzzy C-means clustering algorithm with a novel penalty term for image segmentation. Optoelectronics Review, 13(4), p.309.
[26] Girolami, M., 2002. Mercer kernel-based clustering in feature space. IEEE Transactions on Neural Networks, 13(3), pp.780-784.
[27] Chen, S. and Zhang, D., 2004. Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(4), pp.1907-1916.
[28] Maulik, U. and Bandyopadhyay, S., 2000. Genetic algorithm-based clustering technique. Pattern recognition, 33(9), pp.1455-1465.
[29] Premalatha, K. and Natarajan, A.M., 2008. A new approach for data clustering based on PSO with local search. Computer and Information Science, 1(4), p.139.
[30] Wan, M., Li, L., Xiao, J., Wang, C. and Yang, Y., 2012. Data clustering using bacterial foraging optimization. Journal of Intelligent Information Systems, 38(2), pp.321-341.
[31] Selim, S.Z. and Alsultan, K., 1991. A simulated annealing algorithm for the clustering problem. Pattern recognition, 24(10), pp.1003-1008.
[32] Zhang, C., Ouyang, D. and Ning, J., 2010. An artificial bee colony approach for clustering. Expert Systems with Applications, 37(7), pp.4761-4767.
[33] Senthilnath, J., Omkar, S.N. and Mani, V., 2011. Clustering using firefly algorithm: performance study. Swarm and Evolutionary Computation, 1(3), pp.164-171.
[34] Shang, R., Tian, P., Jiao, L., Stolkin, R., Feng, J., Hou, B. and Zhang, X., 2016. A spatial fuzzy clustering algorithm with kernel metric based on immune clone for SAR image segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(4), pp.1640-1652.
[35] Elazab, A., Wang, C., Jia, F., Wu, J., Li, G. and Hu, Q., 2015. Segmentation of brain tissues from magnetic resonance images using adaptively regularized kernel-based fuzzy-means clustering. Computational and mathematical methods in medicine, 2015.
[36] Chen, Y.T., 2017. Medical Image Segmentation Using Independent Component Analysis-Based Kernelized Fuzzy-Means Clustering. Mathematical Problems in Engineering, 2017.
[37] Li, H. He, and Y. Wen, “Dynamic particle swarm optimization and K-means clustering algorithm for image segmentation”, Optik - International Journal for Light and Electron Optics, Vol.126, No.24, pp.4817-4822, 2015.
[38] H. Ali, M. Elmogy, E. El-Daydamony and A. Atwan, “Multi-resolution MRI brain image segmentation based on morphological pyramid and fuzzy c-mean clustering”, Arabian Journal for Science and Engineering, Vol.40, No.11, pp.3173-3185, 2015.

K. Venkatesh Sharma , "Invasive weed optimization and Kernel Fuzzy C-Means Based MRI brain tissue segmentation," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.43-50, 2018.

Parallel computing is a variant of computation in which many calculations or execution of many processes are performed concurrently. Large and complex problems can be divided into smaller sub problems, which can be then solved at the same time. Network topology is the key factor of performance for any parallel computer. There are many proposed interconnection network topologies in order to achieve high performance. To trace out the better topology on the basis of standard parameter’s performance analysis, we have compared cube based networks and star based networks for the same parameters such as diameter, cost, average distance, message density. In this comparative study various aspects are discussed while designing an efficient multiprocessor interconnection network.

Topology, Cube Based, Star Based, Average Distance, Message Density, Parallel Computing, Interconnection Network

[1] M. Alam and M. T. Cse, “A Comparative Study of Interconnection Network,” vol. 127, no. 4, pp. 37–43, 2015.
[2] A. Samad, J. Siddiqui, and Z. A. Khan, “Properties and Performance of Cube- Based Multiprocessor Architectures,” vol. 7, no. 1, pp. 63–78, 2016.
[3] Z. A. Khan, J. Siddiqui, and A. Samad, “Topological Evaluation of Variants Hypercube Network,” Asian J. Comput. Sci. Inf. Technol., vol. 9, pp. 125–128, 2013.
[4] J. Wu and Y. Wang, “Hypercube-Based Multipath Social Feature Routing in Human Contact Networks,” vol. 63, no. 2, pp. 383–396, 2014.
[5] A. El-amawy and S. Latifi, “Properties and Performance of Folded Hypercubes,” vol. 2, no. January, pp. 31–42, 1991.
[6] I. Transactions, O. N. Parallel, and D. Systems, “The Crossed Cube Architecture for Parallel Computation,” vol. 3, no. 5, pp. 513–524, 1992.
[7] N. Adhikari and C. R. Tripathy, “Folded Dualcube: A New Interconnection Topology for Parallel Systems,” no. v.
[8] N. Adhikari, “The Folded Crossed Cube : A New Interconnection Network for Parallel Systems,” vol. 4, no. 3, pp. 43–50, 2010.
[9] D. Arai, “Generalized-Star Cube : A New Class of Interconnection Topology for Massively Parallel Systems,” 2015.
[10] B. Nag, D. Pradhan, N. K. Swain, and N. Adhikari, “Star varietal cube : A New Large Scale Parallel Interconnection Network,” no. Ii, pp. 37–44, 2011.
[11] N. Adhikari and C. R. Tripathy, “Star-crossed cube : an alternative to star graph,” pp. 719–734, 2014.

Faizan Nasir, Jamshed Siddiqui, "Comparative Analysis of Cube and Star Based Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.51-59, 2018.

Intrusion detection in wireless sensor network (WSN) is a challenging research area, as the WSN has vast area, and lot of nodes. The wireless communication among the nodes, and the battery life of the nodes, makes the researchers difficult to establish a proper communication through the routing mechanism. This research develops the intrusion detection model by using the Neuro fuzzy model. The proposed Intrusion detection using Whale Neuro-Fuzzy System (WNFS) (ID-WNFS) is developed here for detecting the intruders present in the WSN environment. The proposed ID-WNFS has two components, sniffer for creating the log file, and detector for anomaly detection. The sniffer creates the log file by examining the transmission information and extracts the necessary features. The extracted features are sent to the detector, which has the WNFS for the anomaly detection. The proposed WNFS is created by including the properties of the whale optimization algorithm (WOA) with the Neuro fuzzy architecture. The optimization algorithm selects the appropriate fuzzy rules for the detection. The proposed ID-WNFS notifies the simulation protocol about the anomaly behaviour, and thus the routing path is built for the WSN. The entire simulation of ID-WNFS is done by introducing various attacks on nodes and the result reveal that, the ID-WNFS has achieved with the network lifetime as 43.989, energy as 7.106808 and the detection accuracy as 0.787191.

Intrusion detection, wireless sensor network (WSN), routing, Neuro-Fuzzy System, whale optimization algorithm

[1] N. Assad, B.Elbhiri, M. A. Faqihi, M. Ouadou , D. Aboutajdine, "Efficient deployment quality analysis for intrusion detection in wireless sensor networks", Wireless Networks, vol. 22, Issue. 3, pp991-1006, 2016.
[2] H. Moosavi,F. M. Bui, "A Game-Theoretic Framework for Robust Optimal Intrusion Detection in Wireless Sensor Networks", IEEE Transactions on Information Forensics and Security, vol. 9, Issue. 9, pp. 1367-1379, 2014.
[3] A. K. Sagar,D. K. Lobiyal, "Probabilistic Intrusion Detection in Randomly Deployed Wireless Sensor Networks", Wireless Personal Communications, vol. 84, no. 2, pp. 1017-1037, 2015.
[4] L. Gheorghe, R. Rughinis , R. Tataroiu, "Adaptive Trust Management Protocol based on Intrusion Detection for Wireless Sensor Networks", In proceedings of IEEE International Conference on Networking in Education and Research, pp. 1-7, 2013.
[5] M. Wazid, A. K. Das, "An Efficient Hybrid Anomaly Detection Scheme Using K-Means Clustering for Wireless Sensor Networks", Wireless Personal Communications, vol. 90, Issue 4, pp. 1971-2000, 2016.
[6] A. Saeed, A. Ahmadinia, A. Javed, H. Larijani, "Random Neural Network based Intelligent Intrusion Detection for Wireless Sensor Networks", In proceedings of International Conference on Computational Science, vol. 80, pp. 2372-2376, 2016.
[7] S.Shamshirband, A. Amini, N. B. Anuar, L. M. Kiah, T. Y. Wah , S. Furnell, "D-FICCA: A Density-based Fuzzy Imperialist Competitive Clustering Algorithm for Intrusion Detection in Wireless Sensor Networks", Measurement, vol. 55, pp. 212-226, 2014.
[8] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques’, International Journal of Scientific Research In Network Security and communication , vol. 5,no. 6,pp 5-8,2017.
[9] I.Butun, Salvatore D. Morgera, R. Sankar, "A Survey of Intrusion Detection Systems in Wireless Sensor Networks", In proceedings of IEEE International Conference on Modeling, Simulation and Applied Optimization, pp. 1-6, 2015.
[10] Y. Wang, X. Wang, B. Xie, D. Wang , Dharma P. Agrawal, "Intrusion Detection in Homogeneous and Heterogeneous Wireless Sensor Networks", IEEE Transactions on Mobile computing, vol. 7, no. 6, pp. 698-710, 2008.
[11] A. Abduvaliyev, Al-Sakib Khan Pathan, J. Zhou, R. Roman, Wai-Choong Wong, "On the Vital Areas of Intrusion Detection Systems in Wireless Sensor Networks", IEEE Communications Surveys & Tutorials, vol. 15, no. 3, pp. 1223-1237, 2013.
[12] Sandhya G , A. Julian, "Intrusion Detection in Wireless Sensor Network Using Genetic K-Means Algorithm", In proceedings of IEEE International Conference on Advanced Communication Control and Computing Teclmologies, pp. 1-4, 2014.
[13] G. Han, J. Jiang, W. Shen, L. Shu, J. Rodrigues, "IDSEP: a novel intrusion detection scheme based on energy prediction in cluster-based wireless sensor networks", IET Information Security, vol. 7, no. 2, pp. 97-105, 2013.
[14] H. M. Salmon, C. M. d. Farias, P. Loureiro, L. Pirmez, "Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques", International Journal of Wireless Information Networks, vol. 20, Issue 1, pp. 39-66, 2013.
[15] S. Shamshirband, N.B. Anuar, M.L.M. Kiah, A. Patel, "An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique", Engineering Applications of Artificial Intelligence, vol. 26, no. 9, pp. 2105-2127, 2013.
[16] S. Rajasegarar, C. Leckie, M. Palaniswami, "Hyperspherical cluster based distributed anomaly detection in wireless sensor networks", Journal of Parallel and Distributed Computing, vol. 74, no. 1, pp. 1833-1847, 2014.
[17] M. Elleuch, O. Hasan, S. Tahar, M. Abid, "Formal probabilistic analysis of detection properties in wireless sensor networks", Formal Aspects of Computing, vol. 27, no. 1, pp. 79-102, 2015.
[18] S. Mirjalili, A. Lewis, "The Whale Optimization Algorithm", Advances in Engineering Software Vol.95, pp. 51–67, 2016.
[19] S. Shamshirband, N. B. Anuar, L. M. Kiah, "Co-FAIS: Cooperative fuzzy artificial immune system for detecting intrusion in wireless sensor networks", Journal of Network and Computer Applications, vol. 42, pp. 102-117, 2014.
[20] M. Riecker, S. Biedermann, R. E. Bansarkhani, M. Hollick, "Lightweight energy consumption-based intrusion detection system for wireless sensor networks", International Journal of Information Security, vol. 14, Issue 2, pp.155-167, 2015.
[21] Guorui Li, Jingsha He,Yingfang Fu, "Group-based intrusion detection system in wireless sensor networks", Computer Communications, vol. 31, no. 18, pp. 4324-4332, 2008.
[22] M. Moshtaghi, C. Leckie, S. Karunaseker , S. Rajasegarar, "An adaptive elliptical anomaly detection model for wireless sensor networks", Computer Networks, vol. 64, pp. 195-207, 2014.
[23] 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. No. 6,pp 1-7,2016.
[24] W. R. Heinzelman, A. Chandrakasan, H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks,” In Proceedings of the 33rd Hawaii International Conference on System Sciences – 2000,IEEE,pp. 1-10,2000.
[25] KDD Cup 1999. Available on: http://kdd.ics. uci.edu/databases /kddcup99/kddcup99.html, October 2007.

Rakesh Sharma, Vijay Anant Athavale, "ID-WNFS: Intrusion Detection Using Whale Neuro-Fuzzy System In Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.60-71, 2018.

In cloud environment data can be stored and shared between other resources. Any symmetric or asymmetric algorithm is used for secure data in cloud. We need to store the keys in database .The main objective of our work is to improve the protection by encrypting the information with the key generated by QR. It proposes a novel algorithm for generating key using QR image features. It is a reliable and flexible method of key generation for information security. Existing technique for key generation using feature extraction have problem such as high extraction time and high searching time .Existing method need to store more images and stored image may be damaged,. In cloud environment, searching process for authentication causes high network bandwidth, congestion and delay. This paper suggests QR code, a two dimensional code can be used for authentication. Key is extracted from the user unique QR code which is used to achieve the fast retrieval of data from the server. Lost image can be recovered using QR error correction technique. Particularly it is very useful to access mobile applications and there is no need to store more images in server. Additionally extracted key is used as prime factors p and q of the modulus n in RSA for encryption process which tries to secure from mathematical attack. This novel key is applied with asymmetric algorithm.

Mobile Cloud computing, Feature Extraction, GLCM , Spectral Cluster Algorithm, QR Code, RSA

[1] Yao-Jen Chang,Wende Zhang, and Tsuhan Chen “Biometrics-based cryptographic key generation” .In the Proceedings of the 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763)
[2] David PintorMaestre "QRP: An improved secure authentication method using QR codes" UniversitatOberta de Catalunya 08018, Barcelona, Spaindpintor@uoc. edu June 8, 2012.
[3] Thiyagarajan M, Dinesh Kumar K”Qr code authentication for product using cloud computing”Journal Of Global Research InComputer Science,Volume 3, No. 2, February 2012
[4] Suraj Kumar Sahu: " Encryption in QR Code Using Stegnography”International Journal of Engineering Research and Applications,Vol. 3, Issue 4, Jul-Aug 2013, pp.1738-1741
[5] Dong sik-oh, Bong han-kim and Jae- Kwang Lee : "A Study on Authentication System using QR code for Mobile cloud computing Environment".Springer,Hennam University Daejeon,Korea “Future Information Technology” pp500-507
[6] GaurangPanchal a , DebasisSamanta a , “A Novel Approach to Fingerprint Biometric-Based Cryptographic Key Generation and its Applications to Storage Security“ Elsevier “Computer and electrical engineering “Volume 69, July 2018, Pages 461-4782018

[7] Tawfiq S. Barhoom,Zakaria M. Abusilmiyeh”A Novel Cryptography Method Based on Image for KeyGeneration“ Palestinian International Conference on Information and Communication Technology 2013 pages 71-76
[8] Mohammed Tajuddin C. Nandini “Cryptographic Key Generation using Retina Biometric Parameter” International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013
[9] P Selvarani , N Malarvizhi“Secure data in cloud with multimodal key generation” International Journal of Engineering &Technology, Volume 7 (1.7) 2018 Special Issue 7
[10] SonalSharma“Modified RSA Public Key Cryptosystem Using Short Range Natural Number Algorithm” ijarcsse Volume 2, Issue 8, August 2012 ISSN: 2277 128X
[11] Peter Kieseberg, Manuel Leithner, Martin Mulazzani, Lindsay Munroe, SebastianSchrittwieser, MayankSinha, EdgarWeippl: "QR-Code Security". SBA Research, 2010
[12] G. Ateniese, R. Burns, R. Curtmola, J. Herring, O. Khan, L. Kissner, Z. Peterson, and D. Song. “Remote data checking using provable data possession”.ACM Trans. Info.& System Security , May 2011.
[13] C. Wang, Q. Wang, K. Ren and W. Lou, &ldquo,”Privacy-Preserving Public Auditing for Storage Security in Cloud Computing”rdquo, Proc. IEEE INFOCOM `,10, Mar. 2010.
[14] Sandha, M.GanagaDurga, “ Study on Data Security Mechanism in Cloud Computing” 2014 ,IEEE digital Library
[15] E. Mary Shyla , M.Punithavalli “Hybrid Facial Color Component Feature Identification Using Bayesian Classifier” International Journal of Scientific Research in Computer Science and Engineering Vol-1, Issue-3 E-ISSN: 2320-7639
[16] A. Shakin Banu1 , P.Vasuki 2 , S. Mohamed Mansoor Roomi3 , A. Yusuf Khan4,” SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement” International Journal of Scientific Research in Network Security and Communication (ISSN: 2321-3256) Vol.6 , Issue.3 , pp.13-17, Jun-2018

Sandha, M. Ganaga Durga, "Effective Feature Extraction Method in Novel Key Generation Using QR Code to secure the data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.72-76, 2018.

Presenting cutting edge research- A mobile Adhoc network is a collection of wireless devices in which nodes interact with each other in a tentative topology without using pre-defined infrastructure. Almost all networks are protected using multilayer firewalls and encryption methods, but many of them are not so effective. Therefore we proposed a Robust Design of Intrusion Detection System in Wireless Mobile Adhoc Network (RDIDS-WMAN) to detect anomalies for multi-hop networks. The purpose of this paper is to design a detail architecture of Analysis Process that operates on simple rules including four different phases namely Event management, Authentication, Duplicate request generation and Message alert monitoring phase to detect the malicious node. This paper also conducted the simulation of proposed RDIDS-WMAN with a combination of AODV protocol to show its effectiveness and resists the attacks. Meanwhile, the performance of network, such as Packet Delivery Ratio, Average End-to-End delay, Throughput and Packet Drop Ratio is tolerable according to the NS-2.35 simulation results. Our solution also uses an authentication process with hash method.

MANET, AODV, RDIDS-WMAN, Multi-hop network, Authentication, Intusion

[1]. J. Srilakshmi1, S.S.S.N. Usha Devi N2, “Secure and Efficient Multipath Routing Using Overlay Nodes”, International Journal of Scientific Research. Computer Science and Engineering, Vol.6, Issue 5, pp.16-19, October 2018.
[2]. Lubdha M. Bendale, Roshani. L. Jain, Gayatri D. Patil, “Study of Various Routing Protocols in Mobile Ad-Hoc Networks”, International Journal of Scientific Research in Network Security and Communication”, Vol. 6, Sp.issue.01, pp.1-5, January 2018.
[3]. Rutvij H. Jhaveri, Sankita J. Patel and Devesh C. Jinwala “DOS Attacks in Mobile Ad-hoc Networks: A Survey”, In the Proceedings of the 2012 Second International Conference on Advanced Computing & Communication Technologies, India, Pages 535-541, 2012.
[4]. Swaijit Kaushal, Reena Aggarwal,“ A study of different types of attacks in MANET and performance analysis of AODV protocol against wormhole attack”, International Journal of Advanced Research in Computer Engineering & Technology, Vol. 4 Issue 2, February 2015.
[5]. Naveen Bilandi and Harsh K Verma, “Comparative Analysis of Reactive, Proactive and Hybrid Routing Protocols in MANET”, International Journal of Electronics and Computer Science Engineering Vol. 1, Issue 3, pp.1660-1667,
[6]. Y. Huang and W. Lee, “A Cooperative Intrusion Detection System for Ad Hoc Networks,” Proceedings of the 1st ACM Workshop on Security of Ad Hoc and Sensor Networks, USA, Pages 135-147, 2003.
[7] D. Djenouri and N. Badache, “Struggling against selfishness and black hole attacks in MANET”, Wireless Communication. Mobile Computing, vol. 6, pp. 689–704, 2008.
[8] Morigere Subramanya Bhat, Shwetha .D, Manjunath .D and DevarajuJ.T,”Scenario Based Study of on demand Reactive Routing Protocol for IEEE-802.11 and 802.15.4 Standards”, Vol. 1(2), 128-135, November 2011.
[9] Ms. Nidhi Sha rma and Mr. A. Sharma, “The Black-hole node attack in MANET”, Proceedings of 2012 Second International Conference on Advanced Computing & Communication Technologies, pp. 546-548, 2012.
[10] S. D. Manikantan, C. Yu and A. Trisha, “Channel-aware detection of gray hole attacks in wireless mesh networks”, IEEE global telecommunications conference, pp. 1–6, December 2009.

Syed Muqtar Ahmed, Syed Abdul Sattar, "Robust Design of Intrusion Detection System in Wireless Mobile Adhoc Network (RDIDS-WMAN)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.77-82, 2018.