An Efficient Water Marking and Intrusion Detection System Framework for Insider Attack Detection in Cloud Based E-Healthcare Data Management
Keywords:
Cloud Computing, Resource allocation, Task Scheduling, Improved Cuckoo Search Algorithm (ICSA), Deep Reinforcement Learning (DRL), Watermarking Techniques, Intrusion Detection System (IDS), E-Health Care ServicesAbstract
The health care industries in recent years have been facing numerous challenges towards the implementation of electronic health records in preserving the privacy of patients and keeping intact their information. The impact of e-Healthcare system has resulted in inappropriate investigation of patient’s health records. This in turn has affected the cost effectiveness and data consumption. Therefore, the liability towards these two factors is greatly affected. Hence, lack of an appropriate system for detection brings about data breaches. The concept of enabling eHealth systems on the cloud environment will provide a practical solution to the existing problem failing which health records are prone to attacks and intrusion. Any such attacks consequently incur misrepresentation of data which may prove to be endangering for the patients. While the recent researches have failed to throw light on identifying a malignant insider, the current research proposes a novel method which is inbuilt of options for identifying a destructive insider.The effective resource allocation module and watermarking methods used in conjunction with Intrusion Detection System (IDS) for Cloud-based Healthcare System are the main topics of this article. Regarding data storage and malware attack detection, the suggested solution is thought to be more effective and functional. This work uses the Improved Cuckoo Search Algorithm (ICSA) and Convolutional Neural Network for task scheduling and resource allocation (CNN). The spatial domain is reliant on the watermark's hindrance on the pictures' least significant bits (LSB) when a later watermarking approach is utilised. Finally, an intrusion assault detection framework is used for identification in order to determine the attackers of information notification in the e-Health care system. In order to defend against data modification attacks and to identify intruders, watermarking is crucial. Thus, this investigational assessment shows the proposed research which delivers efficient storage of health data in a highly secured environment.
Downloads
References
D. Ergu, G. Kou, Y. Peng, Y. Shi, Y. Shi, The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment, The Journal of Supercomputing, 64(3): 835-848, 2013, doi: 10.1007/s11227-011-0625-1.
P. S. Pillai, S. Rao, Resource allocation in cloud computing using the uncertainty principle of game theory, IEEE Systems Journal, 10(2): 637-648, 2014, doi: 10.1109/JSYST.2014.2314861.
S. Vakilinia, B. Heidarpour, M. Cheriet, Energy efficient resource allocation in cloud computing environments, IEEE Access, 4 (12): 8544-8557, 2016, doi: 10.1109/ACCESS.2016.2633558.
P. Mishra, E. S. Pilli, V. Varadharajan, U. Tupakula, (2017). Intrusion detection techniques in cloud environment: A survey, Journal of Network and Computer Applications, 77(1): 18-47, 2017, doi: https://doi.org/10.1016/j.jnca.2016.10.015.
D. Zissis, D. Lekkas, Addressing cloud computing security issues, Future Generation computer systems, 28(3): 583-592, 2012, doi: https://doi.org/10.1016/j.future.2010.12.006.
K. Popović, Ž. Hocenski, Cloud computing security issues and challenges, In The 33rd International Convention Mipro, Opatija, Croatia, pp. 344-349, 2010, doi: https://ieeexplore.ieee.org/abstract/document/553331.
M. Almorsy, J. Grundy, I. Müller, An analysis of the cloud computing security problem, Published in in Proceedings of the APSEC 2010 Cloud Workshop, Sydney, Australia, pp. 1-6, 2016, doi: https://doi.org/10.48550/arXiv.1609.01107.
C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, M. Rajarajan, A survey of intrusion detection techniques in cloud, Journal of Network and Computer Applications, 36(1): 42-57, 2013, doi: https://doi.org/10.1016/j.jnca.2012.05.003.
R. Arora, A. Parashar, CCI transforming, Secure user data in cloud computing using encryption algorithms, International Journal of Engineering Research and Applications, 3(4): 1922-1926, 2013, doi: 10.4236/jis.2015.61002.
D. Y. Chang, M. Benantar, J. Y. C. Chang, V. Venkataramappa, U.S. Patent No. 8,769,622. Washington, DC: U.S. Patent and Trademark Office, 2014.
M. Almorsy, J. Grundy, A. S. Ibrahim, Collaboration-based cloud computing security management framework, In 2011 IEEE 4th International Conference on Cloud Computing, Washington, DC, USA, pp. 364-371, 2011, doi: 10.1109/CLOUD.2011.9.
T. Gunasekhar, K. T. Rao, V. K. Reddy, B. T. Rao, Mitigation of insider attacks through multi-cloud, International Journal of Electrical & Computer Engineering, 5(1): 136-141, 2015, doi: https://core.ac.uk/reader/334419680.
Z. M. Yusop, J. Abawajy, Analysis of insiders attack mitigation strategies, Procedia-Social and Behavioral Sciences, 129(5): 581-591, 2014, doi: https://doi.org/10.1016/j.sbspro.2014.03.716.
A. Duncan, S. Creese, M. Goldsmith, A combined attack-tree and kill-chain approach to designing attack-detection strategies for malicious insiders in cloud computing, In 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Oxford, UK, pp. 1-9, 2019, doi: 10.1109/CyberSecPODS.2019.8885401.
N. C. S. Iyengar, A. Banerjee, G. Ganapathy, A fuzzy logic based defense mechanism against distributed denial of service attack in cloud computing environment, International Journal of Communication Networks and Information Security, 6(3), 233, 2014, doi: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=01be7ab80b1ab81d9ba07d5e1ab96ac95bd885de.
C. C. Lo, C. C. Huang, J. Ku, A cooperative intrusion detection system framework for cloud computing networks, In 2010 39th International Conference on Parallel Processing Workshops, San Diego, CA, USA, pp. 280-284, 2010, doi: 10.1109/ICPPW.2010.46.
A. V. Dastjerdi, K. A. Bakar, S. G. H. Tabatabaei, Distributed intrusion detection in clouds using mobile agents, In 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences, Sliema, Malta, pp. 175-180, 2009, doi: 10.1109/ADVCOMP.2009.34.
Z. Chiba, N. Abghour, K. Moussaid, M. Rida, A cooperative and hybrid network intrusion detection framework in cloud computing based on snort and optimized back propagation neural network, Procedia Computer Science, 83(1): 1200-1206, 2016, doi: https://doi.org/10.1016/j.procs.2016.04.249.
S. Roschke, F. Cheng, C. Meinel, An extensible and virtualization-compatible IDS management architecture, In 2009 Fifth International Conference on Information Assurance and Security, Xi'an, China, pp. 130-134, 2009, doi: 10.1109/IAS.2009.151.
C. N. Modi, D. Patel, A novel hybrid-network intrusion detection system (H-NIDS) in cloud computing, In 2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), Singapore, pp. 23-30, 2013, doi: 10.1109/CICYBS.2013.6597201.
U. Oktay, O. K. Sahingoz, Proxy network intrusion detection system for cloud computing, In 2013 The International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), Konya, Turkey, pp. 98-104, 2013, doi: 10.1109/TAEECE.2013.6557203.
A. Patel, Q. Qassim, Z. Shukor, J. Nogueira, J. Júnior, C. Wills, P. Federal, Autonomic agent-based self-managed intrusion detection and prevention system, In Proceedings of the South African Information Security Multi-Conference (SAISMC 2010), Port Elizabeth, South Africa, pp. 223-234, 2011.
F. M. Okikiola, A. M. Mustapha, A. F. Akinsola, M. A. Sokunbi, A new framework for detecting insider attacks in cloud-based e-health care system, International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS), Ayobo, Nigeria, pp. 1-6, 2020, doi: 10.1109/ICMCECS47690.2020.240889.
C. Chakraborty, A. Kishor, Real-time cloud-based patient-centric monitoring using computational health systems, IEEE Transactions on Computational Social Systems, 9(6): 1613-1623, 2022, doi: 10.1109/TCSS.2022.3170375.
A. Kishor, C. Chakarbarty, Task offloading in fog computing for using smart ant colony optimization, Wireless Personal Communications, pp. 1-22, 2021, doi: https://link.springer.com/article/10.1007/s11277-021-08714-7.
A. Kishor, C. Chakraborty, W. Jeberson, Reinforcement learning for medical information processing over heterogeneous networks, Multimedia Tools and Applications, 80(16): 23983-24004, , doi: https://link.springer.com/article/10.1007/s11042-021-10840-0.
A. H. Gandomi, X. S. Yang, A. H. Alavi, Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems, Engineering with Computers, 29(1): 17-35, 2013, doi: https://link.springer.com/article/10.1007/s00366-011-0241-y.
M. P. K. Shelke, M. S. Sontakke, A. D. Gawande, Intrusion detection system for cloud computing, International Journal of Scientific & Technology Research, 1(4): 67-71, 2012, doi: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=d60f1d825f98e342cc90f5cc0498f9d544706e19.
B. Brik, N. Lagraa, H. Cherroun, A. Lakas, Token-based clustered data gathering protocol (TCDGP) in vehicular networks, In 2013 9th International Wireless Communications and Mobile Computing Conference (IWCMC), Sardinia, Italy, pp. 1070-1074, 2013, doi: 10.1109/IWCMC.2013.6583705.
B. Brik, N. Lagraa, M. B. Yagoubi, A. Lakas, An efficient and robust clustered data gathering protocol (CDGP) for vehicular networks, In Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications, New York, NY, USA, pp. 69-74, 69–74, 2012, doi: https://doi.org/10.1145/2386958.2386969.
Hemanand, D., Reddy, G. ., Babu, S. S. ., Balmuri, K. R. ., Chitra, T., & Gopalakrishnan, S. (2022). An Intelligent Intrusion Detection and Classification System using CSGO-LSVM Model for Wireless Sensor Networks (WSNs). International Journal of Intelligent Systems and Applications in Engineering, 10(3), 285–293. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2167
S. Gopalakrishnan and P. M. Kumar, "An Improved velocity Energy-efficient and Link-aware Cluster-Tree based data collection scheme for mobile networks," 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India, 2016, pp. 1-10, doi: 10.1109/ICACCS.2016.7586372.
Russo, L., Kamińska, K., Christensen, M., Martínez, L., & Costa, A. Machine Learning for Real-Time Decision Support in Engineering Operations. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/117
Ricci, A., Jankowski, M., Pedersen, A., Sánchez, F., & Oliveira, F. Predicting Engineering Student Success using Machine Learning Algorithms. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/118
Rossi, G., Nowak, K., Nielsen, M., García, A., & Silva, J. Enhancing Collaborative Learning in Engineering Education with Machine Learning. Kuwait Journal of Machine Learning, 1(2). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/119
Agrawal, S. A., Umbarkar, A. M., Sherie, N. P., Dharme, A. M., & Dhabliya, D. (2021).Statistical study of mechanical properties for corn fiber with reinforced of polypropylene fiber matrix composite. Materials Today: Proceedings, doi:10.1016/j.matpr.2020.12.1072
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
