Abstract
Cloud computing is rapidly growing nowadays and stands a creative computing paradigm which provides Internet facilities to meet users’ storage needs. Various interconnected technologies exist in Cloud. Each one has some weaknesses which raise many concerns about privacy and security. One among Cloud computing's main security issues is to safeguard against network encroachments affecting the security, availability, and legitimacy of Cloud services and resources provided. Hidden Markov model is a state-based transition model by which the transition of states is been used to estimate using the distributions of probabilities from another state to previous and next another state. This model can also be used here on this paper for both the identification and prevention of intrusion. Except in the cloud setting where security issues and confidentiality are main concerns, identification and prevention of attack is done. Compared to some other current intrusion detection methodology the recommended methodology applied here is successful. This model also increases the real positive rate of intrusion detection systems and reduces the false positive rate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ismail MN, Aborujilah A, Musa S, Shahzad A (2013) Detecting flooding based DoS attack in cloud computing environment using covariance matrix approach
John EK, Thaseen S (2012) Efficient defense system for IP spoofing in networks. Comput Sci In Technol (CS & IT), pp 185–193
Wang Q, Wang C, Ren K, Lou W, Li J (2011) Enabling public auditability and data dynamics for storage security in cloud computing. IEEE Trans Parallel Distrib Syst 22(5):847–859
Khosronejad M, Sharififar E, Torshizi HA, Jalali M (2013) Developing a hybrid method of Hidden Markov Models and C5.0 as a Intrusion Detection System. Int J Database Theory Appl. https://doi.org/10.14257/ijdta.2013.6.5.15. Corpus ID: 32936020
Sultana A, Hamou-Lhadj A, Couture M (2012) An improved Hidden Markov Model for anomaly detection using frequent common patterns. In: 2012 IEEE international conference on communications (ICC). https://doi.org/10.1109/ICC.2012.6364527
Barsoum A, Hasan A (2012) Enabling dynamic data and indirect mutual trust for cloud computing storage systems. https://doi.org/10.1109/TPDS.2012.337. 1045-9219/12/$31.00 © 2012 IEEE
Ali S, Siegel HJ, Maheswaran M, Hensgen D, Ali S (2000) Representing task and machine heterogeneities for heterogeneous computing systems. Tamkang J Sci Eng 3(3):195–207
Azimzadeh F, Biabani F (2017) Multi-objective job scheduling algorithm in cloud computing based on reliability and time. In: 2017 third international conference on web research (ICWR). IEEE, pp 96–101
Luo, L, Li H, Qiu X, Tang Y (2016) A resource optimization algorithm of cloud data center based on correlated model of reliability, performance and energy. In: 2016 IEEE international conference on software quality, reliability and security companion (QRS-C), Vienna, pp 416–417
Madni SHH, Shafie ALM, Abdulhamid SM (2017) Optimal resource scheduling for IaaS cloud computing using Cuckoo search algorithm. Sains Humanika 9(1–3)
Mastelic T, Oleksiak A, Claussen H, Brandic I, Pierson J-M, Vasilakos AV (2015) Cloud computing: survey on energy efficiency. ACM Comput Surv 47(2):33
Shuja J, Gani A, Shamshirband S, Ahmad RW, Bilal K (2016) Sustainable cloud datacenters: a survey of enabling techniques and technologies. Renew Sustain Energy Rev 62:195–214
Singh S, Chana I (2016) A survey on resource scheduling in cloud computing: issues and challenges. J. Grid Comput 14(2):217–264
Youn C-H, Chen M, Dazzi P (2017) Cloud broker and cloudlet for workflow scheduling. KAIST Research Series book series. KAISTRS Springer, pp 2214–2541
Zhang S, Chatha KS (2007) Approximation algorithm for the temperature aware scheduling problem. In: Proceedings of international conference on computer aided design, pp 281–288
Zhou A, Wang S, Zheng Z, Hsu CH, Lyu MR, Yang F (2016) On cloud service reliability enhancement with optimal resource usage. IEEE Trans Cloud Comput 4(4):452–466
Zhang Y, Hong B, Zhang M et al (2013) ECAD: cloud anomalies detection from an evolutionary view. International Conference on Cloud computing and Big Data (CloudCom-asia). IEEE, pp 328–334
Boutros T, Liang M (2011) Detection and diagnosis of bearing and cutting tool faults using hidden Markov models. Mech Syst Signal Process 25(6):2102–2124
Lopes Dalmazo B, Vilela JP, Curado M (2013) Predicting traffic in the Cloud: a statistical approach. 2013 Third international conference on cloud and green computing (CGC). IEEE, pp 121–126
Tan Y, Nguyen H, Shen Z et al (2012) Prepare: detective performance anomaly prevention for virtualized Cloud systems. In: 2012 IEEE 32nd international conference on distributed computing systems (ICDCS). IEEE, 285–294
Koch R, Golling M, Rodosek GD (2014) Behavior based intrusion detection in encrypted environments. Commun Mag 52(7):124–131
Zhao D, Traore I, Sayed B, Lu W, Saad S, Ghorbani A, Garant D (2013) Botnet detection based on traffic behavior analysis and flow intervals. Comput Secur 39:2–16
Zheng X, Martin P, Brohman K, Xu LD (2014) CLOUDQUAL: a quality model for cloud services. IEEE Trans Ind Informat 10(2):15271536
Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv 47(1):3
Xing T, Huang D, Xu L, Chung CJ, Khatkar P (2013) Snortflow: a openflow-based intrusion prevention system in cloud environment. In: Research and Educational Experiment Workshop (GREE), 2013 Second GENI, 2013, pp 89–92
Oktay U, Sahingoz OK (2013) Attack types and intrusion detection systems in cloud computing. In: 2013 6th International information security & cryptology conference, pp 71–76
Zhang X, Meng F, Chen P, Xu J (2016) Taskinsight: a fine-grained performance anomaly detection and problem locating system. In: Proceedings of the 2016 IEEE 9th international conference on cloud computing (CLOUD). San Francisco, CA
Matsuki T, Matsuoka N (2016) A resource contention analysis framework for diagnosis of application performance anomalies in consolidated cloud environments. In: Proceedings of the 7th ACM/SPEC on international conference on performance engineering (ICPE), Delft, The Netherlands
Calheiros RN, Ramamohanarao K, Buyya R, Leckie C, Versteeg S (2017) On the effectiveness of isolation-based anomaly detection in cloud data centers. Concurrency Computat Pract Exper 29(18):e4169
Tan Y, Nguyen H, Shen Z, Gu X, Venkatramani C, Rajan D (2012) Prepare: predictive performance anomaly prevention for virtualized cloud systems. In: Proceedings of the 32nd IEEE international conference on distributed computing systems, Macau, China
Cetinski K, Juric MB (2015) AME-WPC: advanced model for efficient workload prediction in the cloud. J Netw Comput Appl 55:191–201
Ibidunmoye O, Hernández-Rodriguez F, Elmroth E (2015) Performance anomaly detection and bottleneck identification. ACM Comput Surv 48(1):4:1–4:35
Sharma B, Jayachandran P, Verma A, Das CR (2013) CloudPD: Problem determination and diagnosis in shared dynamic clouds. In: Proceedings of the 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN’13). IEEE, pp 1–12
Smith D, Guan Q, Fu S (2010) An anomaly detection framework for autonomic management of compute cloud systems. In: Proceedings of the IEEE 34th annual computer software and applications conference workshops (COMPSACW’10). IEEE, pp 376–381
Tan Y, Adviser-Gu XH (2012) Online performance anomaly prediction and prevention for complex distributed systems. North Carolina State University
Wang T, Zhang W, Wei J, Zhong H (2012) Workload-aware online anomaly detection in enterprise applications with local outlier factor. In: Proceedings of the IEEE 36th annual computer software and applications conference (COMPSAC’12). IEEE, pp 25–34
Kumar DR, Krishna TA, Wahi A (2018) Health monitoring framework for in time recognition of pulmonary embolism using Internet of Things. J Comput Theor Nanosci 15(5):1598–1602. https://doi.org/10.1166/jctn.2018.7347
Krishnasamy L, Dhanaraj RK, Ganesh Gopal D, Reddy Gadekallu T, Aboudaif MK, Abouel Nasr E (2020) A Heuristic angular clustering framework for secured statistical data aggregation in sensor networks. Sensors 20(17), 4937. https://doi.org/10.3390/s20174937
Dhiviya S, Malathy S, Kumar DR (2018) Internet of Things (IoT) elements, trends and applications. J Comput Theor Nanosci 15(5):1639–1643. https://doi.org/10.1166/jctn.2018.7354
Rajesh Kumar D, Shanmugam A (2017) A hyper heuristic localization based cloned node detection technique using GSA based simulated annealing in sensor networks. In: Cognitive computing for big data systems over IoT, pp 307–335. Springer International Publishing. https://doi.org/10.1007/978-3-319-70688-7_13
Prasanth T, Gunasekaran M, Kumar DR (2018) Big data Applications on Health Care. 2018 4th International conference on computing communication and automation (ICCCA). 2018 4th International conference on computing communication and automation (ICCCA), Dec 2018. https://doi.org/10.1109/ccaa.2018.8777586
Lin YD, Lu CN, Lai YC, Peng WH, Lin PC (2019) Application classification using packet size distribution and port association. Journal of Network and Computer Applications 32(5):1023–1030
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Arvindhan, M., Rajesh Kumar, D. (2022). Analysis of Load Balancing Detection Methods Using Hidden Markov Model for Secured Cloud Computing Environment. In: Deepak, B.B.V.L., Parhi, D., Biswal, B., Jena, P.C. (eds) Applications of Computational Methods in Manufacturing and Product Design. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-0296-3_53
Download citation
DOI: https://doi.org/10.1007/978-981-19-0296-3_53
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0295-6
Online ISBN: 978-981-19-0296-3
eBook Packages: EngineeringEngineering (R0)