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In Mobile Ad-hoc Network(MANET) there are so many routing protocols are commenced. But no one will fulfill all security requirements. The crucial issue in MANET is Security due to the openness, infrastructure less and variable topology of MANET. In the broad dispersion of MANET, so many routing protocols are incorporated initially with the assumption that security must be retrofitted. Thus the routing protocols are more prone to various attacks with the risk of destruction of data. In this paper, we have discussed various components which will be required or helpful for designing of secure routing protocol in MANET.

MANET, security, attacks, secure routing protocols, components

[1] Hu Y., A.Perrig., “A Survey of Secure Wireless Ad Hoc Routing”, IEEE Security & Privacy, 2004.
[2] H. Yang, H. Luo, “Security in Mobile Ad Hoc Networks: Challenges and Solution”, IEEE Wireless Communications, 2004.
[3] V.Bhargavi, M.Seetha, S.Viswanadharaju, “ A Trust Based Secure Routing Scheme for MANETS”, IEEE, pp-565-570, 2016.
[4] J.L. Tornos, J.J.Piles and J. L. Salazar,“ ADSR: Authenticated DSR”, 2011 6th International Conference on Risks and Security of Internet and Systems (CRiSIS), IEEE,pp-1-8, 2011
[5] M.Pandya,A.K.Shrivastava.,“Improvising Performance with Security of AODV Routing Protocol for MANETs”, IJCA, 78 , p1-1-7,2003.
[6] K. V. Arya, S. S. Rajput ,“ Securing AODV Routing Protocol in MANET using NMAC with HBKS Technique”, 2014 International Conference on Signal Processing and Integrated Networks (SPIN), IEEE,pp-281-285, 2014
[7] A. Patwardhan, J. Parker, A. Joshi, M. Iorga and T. Karygiannis, “Secure Routing and Intrusion Detection in Ad Hoc Networks,” in Proc. of the 3rd IEEE – International conference on Pervasive Computing and Communications, March, pp. 191-199,2005.
[8] S. Soni, A. Parihar, “ Effective Intrusion Detection Scheme in Mobile Ad-Hoc Networks”, IJCA (0975 – 8887), Volume 135 – No.5, February 2016
[9] J. Kim, G. Tsudik,“SRDP: Secure route discovery for dynamic source routing in MANETs”, elsevier,Ad Hoc Networks 7, pp- 1097–1109,2009
[10] A.Rajaram ,S.Palaniswami, “ A High Certificate Authority Scheme for Authentication in Mobile Ad hoc Networks”, IJCSI, Vol. 7, Issue 4, No 5,pp-37-45, July 2010
[11] Anitha, J. Akilandeswari, “ Secured Message Transmission in Mobile AD HOC Networks through Identification and Removal of Byzantine Failures”, InterJRI Computer Science and Networking, Vol. 2, Issue 1,pp-14-18, August 2010
[12] N. Raza1, M. U. Aftab1, M. Q. Akbar2, O. Ashraf3, M. Irfan4, “ Mobile Ad-Hoc Networks Applications and Its Challenges”, Communications and Network, 8,pp- 131- 136,August 2016 .
[13] A Ambhaikar, D Mitra,R.Deshmukh, “ Performance of MANET Routing Protocol for Improving Scalability ”, Published in IJAEA,

S.S. Zalte, V.R. Ghorpade, "Components for Designing of Secure Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.313-316, 2017.

Whether we realize it or not, machine learning touches our daily lives in many ways. When you upload a picture on social media, for example, you might be prompted to tag other people in the photo. That’s called image recognition, a machine learning capability by which the computer learns to identify facial features. Other examples include number and voice recognition applications. From an intrusion detection perspective, analysts can apply machine learning, data mining and pattern recognition algorithms to distinguish between normal and malicious traffic. One way that a computer can learn is by examples. With the advances in information technology (IT) criminals are using cyberspace to commit numerous cyber crimes. Cyber infrastructures are highly vulnerable to intrusions and other threats. Physical devices and human intervention are not sufficient for monitoring and protection of these infrastructures; hence, there is a need for more sophisticated cyber defense systems that need to be flexible, adaptable and robust, and able to detect a wide variety of threats and make intelligent real-time decisions. Numerous bio-inspired computing methods of Machine Learning have been increasingly playing an important role in cyber crime detection and prevention. The purpose of this study is to present advances made so far in the field of applying ML techniques for combating cyber crimes, to demonstrate how these techniques can be an effective tool for detection and prevention of cyber attacks, as well as to give the scope for future work.

Intrusion Detection; Machine Learning

[1] S. Singh and S. Silakari, "A Survey of Cyber Attack Detection Systems", IJCSNS International Journal of Computer Science and Network Security, vol. 9, no. 5, 2009 [Online].Available:http://paper.ijcsns.org/07_book/200905/20090501.pdf. [Accessed: 08- Feb- 2016]
[2] S. Simmons, D. Edwards, N. Wilde, J. Just and M. Satyanarayana, "Preventing Unauthorized Islanding: Cyber-Threat Analysis", 2006 IEEE/SMC International Conference on System of Systems Engineering, pp. 5, 24-26 [Online]. Available:http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=165229&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D1652294. [Accessed: 11- Feb- 2016]
[3] I. Ionita and L. Ionita, "An agent-based approach for building an intrusion detection system",RoEduNet International Conference 12th Edition: Networking in Education and Research, pp. 1-6, 26-28, 2013 [Online]. Available: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6714184. [Accessed: 11- Feb- 2016]
[4] S. Dilek, H. Çakır and M. Aydin, “APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES TO COMBATING CYBER CRIMES: A REVIEW", International Journal of Artificial Intelligence & Applications (IJAIA), vol. 6, no. 1, 2015 [Online]. Available: http://arxiv.org/ftp/arxiv/papers/1502/1502.03552.pdf. [Accessed: 13- Feb- 2016]
[5] A. Cerli and D. Ramamoorthy, "Intrusion Detection System by Combining Fuzzy Logic with Genetic Algorithm", Global Journal of Pure and Applied Mathematics (GJPAM), vol. 11, no. 1, 2015 [Online]. Available: http://ripublication.com/gjpamspl/gjpamv11n1spl_20.pdf. [Accessed: 09- Feb- 2016]
[6] F. Rosenblatt. The Perceptron -- a perceiving and recognizing automaton. Report 85- 460-1, Cornell Aeronautical Laboratory, 1957.
[7] G. Klein, A. Ojamaa, P. Grigorenko, M. Jahnke, E. Tyugu. Enhancing Response Selection in Impact Estimation Approaches. Military Communications and Information Systems Conference (MCC), Wroclaw, Poland, 2010.
[8] http://en.wikipedia.org/wiki/Expert_system. Expert System. Wikipedia.
[9] J. Kivimaa, A. Ojamaa, E. Tyugu. Graded Security Expert System. Lecture Notes in Computer Science, v. 5508. Springer, 2009, 279-286.
[10] D. Anderson, T. Frivold, A. Valdes. Next- generation intrusion detection expert system (NIDES). Technical Report SRI-CSL-95-07, SRI International, Computer Science Lab (1995).
[11] TF. Lunt, R. Jagannathan. A Prototype Real-Time Intrusion-Detection Expert System. Proc. IEEE Symposium on Security and Privacy, 1988, p. 59.
[12] L. Rui, L. Wanbo, (2010) “Intrusion Response Model based on AIS”, International Forum on Information Technology and Applications (IFITA), Vol. 1, pp. 86 – 90.
[13] U. Kaster, B. Kuhiber. Information and Knowledge Management in C2 Systems – The Gap Between Theory and Practice is not all that big. In: M.- Amanovicz. Comcepts and Implementations for Innovative Military Communications and Information Technologies. Military University of Technology Publisher, Warsaw, 2010, pp. 98 – 107.
[14] J. Kaster. Combined Knowledge Management and Workflow Management in C2 Systems – a user centered approach. Fraunhofer Institute for Communication, Information Processing and Ergonomics. Report ID # 197, 2009.
[15] http://singinst.org/overview/whatisthesingularity/
[17] R. Kurtzwell. The Singularity is Near. Viking Adult. 2005.
[18]http://www.ted.com/webcast/archive/event/ibmwatson
[19] J. Kivimaa, A. Ojamaa, E. Tyugu. Pareto-Optimal Situation Analysis for Selection of Security Measures. Proc. MilCom, 2008.

Namita Parati, Pratyush Anand, "Machine Learning in Cyber Defence," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.317-322, 2017.

Clustering is the task of dividing the data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. As Big Data is referring to terabytes and petabytes of data and clustering algorithms are come with high computational costs, the question is how to cope with this problem and how to deploy clustering techniques to big data and get the results in a reasonable time. This paper focuses on the traditional partition based clustering algorithms such as KMeans, K Medoids, PAM, CLARA and CLARANS and its advantages and disadvantages.

K-Means, PAM, CLARA, CLARANS

[1]. T Saha, K Dhas “ Inregration and Interelation of Bigdata With Cloud Computing: A Review “ International Journal of Computer Sciences and Engineering Vol.5(11), Nov 2017, E-ISSN: 2347-2693
[2]. Unnati R. Raval et al, International Journal of Computer Science and Mobile Computing, Vol.5 Issue.5, May- 2016, pg. 191-203
[3]. Shalini S Singh, N C Chauhan,” K-means v/s Kmedoids: A Comparative Study”, National Conference on Recent Trends in Engineering & Technology, May 2011.
[4]. C. Zhang, and Z. Fang, An improved k-means clustering algorithm, Journal of Information & Computational Science, 10(1), 2013, 193-199.
[5]. 5.Fahad, N. Alshatri, Z. Tari, A. Alamri, I. Khalil A. Zomaya, S. Foufou, and A. BourasA Survey of Clustering Algorithms for Big Data:Taxonomy& Empirical Analysis,Accepted for IEEE transaction on emerging topics in computing 2014.
[6]. Gopi Gandhi, RohitSrivastava ,”Review Paper: A Comparative Study on Partitioning Techniques of Clustering Algorithms “- International Journal of Computer Applications (0975 – 8887) Volume 87 – No.9, February 2014
[7]. AzharRauf, Sheeba, SaeedMahfooz, Shah Khusro and HumaJaved“ “Enhanced K-Mean Clustering Algorithm to Reduce Number of Iterations and Time Complexity “Middle-East Journal of Scientific Research 12 (7): 959-963, 2012
[8]. Ali SeyedShirkhorshidi, SaeedAghabozorgi, Teh Ying Wah and TututHerawan, “Big Data Clustering: A Review”, Research Gate, Jun, (2014)

E. Mahima Jane and E. George Dharma Prakash Raj, "Survey on Partition based Clustering Algorithms in Big Data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.323-325, 2017.

The drastic amount of data is transferred into public cloud at every moment and securing data while transferring into cloud has become a tradition and in this paper we present scope of improvement for using optimized symmetric key algorithm for cloud environment. After careful investigation AES, 3DES, RC6, Twofish, Blowfish algorithms, In this paper, we proposed an approach to select optimized algorithm for deciding the use of efficient algorithm in cloud depending upon the outcomes of investigations carried. The future work can be carried out in the direction of replacement of conventional key expansion routine with the genetic concept routine in the identified algorithm.

Conventional Key Expansion, AES, Blowfish, Twofish, Genetic Operation

[1] Ashok Sharma, Ramjeevan Singh Thakur, Shaliesh Jaloree, “Investigation of Efficient Cryptic Algorithm for image files Encryption in Cloud”, International Journal of Scientific Research in Computer Science and Engineering,Vol.4(5), pp.5-11, 2016 ISSN: 2320-7639.
[2] Ashok Sharma, Ramjeevan Singh Thakur, Shaliesh Jaloree, “Investigation of Efficient Cryptic Algorithm for Video files Encryption in Cloud”, International Journal of Scientific Research in Computer Science and Engineering,Vol.4(6) pp.8-14, 2016 ISSN: 2320-7639.
[3] Yaseen I , Sahasrabuddhe H., “A Genetic Algorithm for the Cryptanalysis of Chor Rivest Knapsack Public Key Crypto System (PKC)” in proceeding of third International Conference on Computational Intelligence and Multimedia Application, September 23-26, New Delhi 1999.
[4] Mathews R., “The use of Genetic Algorithm in Cryptanalysis”, Cryptologia, Vol.17(4), 1993.
[5] Spillman R, “The Use a Genetic Algorithm in the Cryptanalysis of Simple Substitution Ciphers” Cryptologia, Vol.17(1), 1993
[6] Bagnall A, “The Application of Genetic Algorithm Cryptanalysis”, Master Degree Thesis in School of Information Systems, University of East Anglia 1996
[7] McKeon G. and Rayward-Smith V., “The Cryptanalysis of a Three Rotor Machine Using Genetic Algorithm” available at http://citeseer.nj.nec.com/162166.html
[8] http://dip.sun.ac.za/~vuuren/papers/genetic.ps.
[9] Rashed Abdali, “Using Modified Genetic Algorithm to Replace AES Key Expansion Algorithms”, Journal of Applied Mathematical Sciences, Vol.7 (144), pp.7161-7171, 2013.

Ashok Sharma, "Identifying High Performing Symmetric Key Algorithms in Transferring Data into Cloud," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.326-328, 2017.

Distributed computing gives an assortment of administrations to our current specialized regions. It is helpful for the two customers and organizations to utilize applications without access their own records. Security is an unavoidable component of our cloud administration. So we ought to make sure that our specialist organization can give the security to our information. Yet, various prominent hacking cases will prompt various sorts of safety issues on cloud. It for the most part happens in multi clients distributed computing regions Cloud security is significant in each field of clients. Everybody needs to give their data completely safe. For this reason, here we can utilize property-based encryption that is a kind of open key encryption. Characteristic based encryption permits information proprietors and clients to encode and decode in light of the individual ascribes. So we propose a audit scheme which gives a sort of security insurance on clients and keep from unapproved access from programmers.

Encryption, CP-ABE, Collusion Attack

[1] K. Yang and X. Jia, “Expressive, efficient and revocable data access control for multi-authority cloud storage,” IEEE Transactions on Parallel and Distributed Systems, Vol.25, Issue.no.7,pp. 1733-1744 2013.
[2] K. Xue and P. Hong, “A dynamic secure group sharing framework in public cloud computing,” IEEE Transactions on Cloud Computing, Vol. 2, Issue 4, pp. 459–470, 2014.
[3] ]. J. Han, W. Susilo, Y. Mu, J. Zhou, and M. H. A. Au, “Improving privacy and security in decentralized cipher text policy attribute-based encryption,” IEEE Transactions on Information Forensics and Security, Vol. 10, Issue. 3, pp. 665–678, 2015
[4] K. Yang, X. Jia, and K. Ren, “Secure and verifiable policy update outsourcing for big data access control in the cloud,” IEEE Transactions on Parallel &Distributed Systems, Vol. 26, Issue 12, pp. 3461–3470, 2015.
[5] W. Li, K. Xue, Y. Xue, and J. Hong, “TMACS:A robust and verifiable threshold multi-authority access control system in public cloud storage,” IEEE Transactions on Parallel & Distributed Systems, Vol. 27, Issue. 5, pp. 1484– 1496, 2016
[6] Kai Fan1*, Qiong Tian1, Junxiong Wang1, Hui Li1, Yintang Yang2, “Privacy Protection Based Access Control Scheme in Cloud-Based Services” Vol.14,Issue.1,pp.61-71

Nimmymol Manuel, Simy Mary Kurian, Neena Joseph, Neema George, "Privacy Protection on Cloud Computing with Auditing Scheme," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.329-333, 2017.

Scheduling the class intervals using traditional methods like using on line spreadsheets is complicated and time consuming. The complexity will increase with more than one concern, pupil and teachers because the requirements become extra complex. It is tough to manage topics with multiple instructors or forming student businesses and assigning instructors. In addition, priority of topics or instructors, class hours are not taken into consideration for scheduling. Class scheduling using genetic algorithm, has been advanced to time table class rooms thinking about various resources and parameters. The proposed algorithm accepts diverse parameters like priority values for teachers and topics or elegance hours and give the satisfactory solution. Our new device makes the school room scheduling less difficult and also reduce the time required for scheduling.

Scheduling, ComplexityIOT, fuzzylogic, genetic algorithm.

[1]. R. Lewis and J. Thompson, “Analysing the effects of solution space connectivity with an effective metaheuristic for the course timetabling problem,” European. Journal of Operation Research, vol. 240, no. 3, pp. 637–648, 2014.
[2]. A. El Amraoui, M.-A. Manier, A. El Moudni, and M. Benrejeb, “A genetic algorithm approach for a single hoist scheduling problem with time windows constraints,” Engineering Application of Artificial Intelligence, vol. 26, no. 7, pp. 1761–1771, 2013.
[3]. Rohit. P.S, S.M., “A Probability Based Object-Oriented Expert System for Generating Time-Table” International Journal of Research in Computer Applications & Information Technology, 2013. Volume 1( Issue 1): p. pp. 52-58
[4]. Mohammad, K.H., J, Problem-solving capacities of spiritual intelligence for artificial intelligence. Social and Behavioral Sciences, 2012: p. 170 – 175.
[5]. Isaai, M.T., Kanani, A., Tootoonchi, M., & Afzali, H. R. , Intelligent timetable evaluation using fuzzy AHP Expert Systems with Applications,, 2011. 38(4): p. 3718-3723.
[6]. David E Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning, 1st ed. Addison-Wesley Publishing Company, Inc, 1989, Ch. 1, pp. 1-2..

Vinodh P Vijayan, Neena Joseph, Neema George, Simy Mary Kurian, "Scheduling Time Slots for Class Conduction Using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.334-338, 2017.

Scheduling is the process of allocating cloud resources to several users in accordance with a predetermined schedule. Proper parallel operations planning is necessary to achieve good performance in scattered conditions. Work scheduling must take a number of limits and objectives into account in order to create meaningful schedules in the cloud environment. The difficulty of task mapping given the resources at hand is categorised as an NP-hard problem. Cloud computing`s Quality of Service (QoS) problem has to be overcome before it can be deemed successful. Resource allocation is crucial when it comes to tasks with performance Optimization restrictions. The only way to effectively accomplish crucial objectives in cloud computing including high performance, high profit, high utilization, scalability, provision efficiency, and economy is by using an effective task scheduling system. This article suggests a framework based on the Grey Wolf Optimization, Particle Swarm Optimization, and Flower Pollination Algorithms for efficient job scheduling in a cloud computing environment. Job scheduling is done by Grey Wolf Optimization to shorten execution times and lower costs.

[1] M. Joundy, S. Sarhan, S. Elmougy, "Task Scheduling Algorithms In Cloud Computing: A Comparative Study," International Journal of Intelligent Computing and Information Science, VOL.15, NO. 4 OCTOBER 2015
[2] Sung Ho Jang et. al., The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing, International Journal of Control and Automation 5(4):157-162 Dec 2012.
[3] Hu Xu-Huai et. al., "An IPSO Algorithm for Grid Task Scheduling Based on Satisfaction Rate," IHMSC `09. International Conference on Intelligent Human-Machine Systems and Cybernetics, 2009.
[4] S. S. Kim, J. H. Byeon, H. Liu, A. Abraham, and S. McLoone, “Optimal job scheduling in grid computing using efficient binary artificial bee colony optimization,” Soft Computing, vol. 17, no. 5, pp. 867–882, 2013.
[5] https://scholarworks.wmich.edu/cgi/viewcontent.cgi?article = 1661&context=masters_theses
[6] N. J. Kansal and I. Chana, “Artificial bee colony based energy-aware resource utilization technique for cloud computing,” Concurrency and Computation: Practice and Experience, vol. 27, no. 5, pp. 1207–1225, 2015.
[7] X. Chen and D. Long, "Task scheduling of cloud computing using integrated particle swarm algorithm and ant colony algorithm," Clust. Comput., pp. 1-9, 2017.
[8] Jasti, V., Zamani, A., Arumugam, K., Naved, M., Pallathadka, H., & Sammy, F. et al. (2022). Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis. Security And Communication Networks, 2022, 1-7. doi: 10.1155/2022/1918379
[9] Mondal, B., Dasgupta, K., Dutta, P.: Load balancing in cloud computing using stochastic hill climbing-a soft computing approach. Procedia Technol. 4, 783–789 (2012)
[10] Abdi, S., Motamedi, S.A., Sharifian, S.: Task scheduling using modified PSO algorithm in cloud computing environment. In: International Conference on Machine Learning, Electrical and Mechanical Engineering (ICMLEME’2014) Jan. 8–9, Dubai (UAE) (2014)
[11] I. Damakoa, et. Al. i, “Efficient and scalable ACO-based task scheduling for green cloud computing environment,” in 2017 IEEE International Conference on Smart Cloud (SmartCloud), pp. 66–71, New York, NY, USA, 2017.
[12] A. Alireza, “PSO with adaptive mutation and inertia weight and its application in parameter estimation of dynamic systems,” Acta Automatica Sinica, vol. 37, no. 5, pp. 541–549, 2011.
[13] Chitra, Madhusudhanan, Sakthidharan & Saravanan 2014, ‘Local minima jump PSO for workflow scheduling in cloud computing environments’, In Advances in computer science and its applications, vol. 19, no. 3, pp. 222-234.
[14] Kamalam Balasubramani , Karnan Marcus, "A Study on Flower Pollination Algorithm and Its Applications",International Journal of Application or Innovation in Engineering & Management, Vol. 3, Issue11, Nov. 2014.

Arpit Agrawal , "Task Scheduling for Multi-Objective Optimization in a Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.339-343, 2017.