i-manager Publications

i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients

Most Cited

Volume 9 Issue 1 January - June 2022

Review Paper

Detection of DDoS Attack in Cloud Computing and its Prevention: A Systematic Review

Ali Raza*

Department of Computer Science, Bahria University Karachi Campus, Karachi City, Sindh, Pakistan.

Raza, A. (2022). Detection of DDoS Attack in Cloud Computing and its Prevention: A Systematic Review. i-manager’s Journal on Cloud Computing, 9(1), 1-8. https://doi.org/10.26634/jcc.9.1.18542

Abstract

Cloud computing is one of the latest and greatest environments for delivering Software as a Service (SaaS), Infrastructure as a Service (IaaS), and Platform as a Service (PaaS) in digital communications infrastructure. Cloud computing helps the user remotely access the required service as needed through the Internet. But this technological advancement, due to its remote availability in the cloud, leads to new attacks. One of the biggest threats to cloud infrastructure is Distributed Denial of Service (DDoS) flooding attacks. DDoS flooding attacks are clearly trying to exploit the availability of services for a legitimate user. An attacker gains access to a large number of computers (i.e., botnets) by exploiting the vulnerabilities, and then uses the botnets to initiate an organized attack with a large number of targets. This paper analyses the latest methods for detecting and preventing Distributed Denial of Service (DDoS) attacks. It also provided methods and technologies for preventing, detecting, and responding to DDoS flood attacks.

References

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[2]. Agrawal, N., & Tapaswi, S. (2021). An SDN-assisted defense mechanism for the shrew ddos attack in a cloud computing environment. Journal of Network and Systems Management, 29(2), 1-28. https://doi.org/10.1007/s10922-020-09580-7

[3]. Alhisnawi, M., & Ahmadi, M. (2020). Detecting and mitigating DDoS attack in named data networking. Journal of Network and Systems Management, 28(4), 1343-1365. https://doi.org/10.1007/s10922-020-09539-8

[4]. Alsaeedi, A., Bamasag, O., & Munshi, A. (2020). Real- Time DDoS flood attack monitoring and detection (rt- amd) model for cloud computing. In the 4th International Conference on Future Networks and Distributed Systems (ICFNDS), 1-5. https://doi.org/10.1145/3440749.3442606

[5]. Alzahrani, S., & Hong, L. (2018). Detection of distributed denial of service (DDoS) attacks using artificial intelligence on cloud. In 2018, IEEE World Congress on Services (SERVICES), 35-36. https://doi.org/10.1109/SERVICES.2018.00031

[6]. Chen, J., Yang, Y. T., Hu, K. K., Zheng, H. B., & Wang, Z. (2019). DAD-MCNN: DDoS attack detection via multi channel CNN. In Proceedings of the 2019 11th International Conference on Machine Learning and Computing, 484-488. https://doi.org/10.1145/3318299.3318329

[7]. Corrêa, J. H., Ciarelli, P. M., Ribeiro, M. R. N., & Villaça, R. S. (2021). Ml-based ddos detection and identification using native cloud telemetry macroscopic monitoring. Journal of Network and Systems Management, 29(2), 1-28. https://doi.org/10.1007/s10922-020-09578-1

[8]. Devi, B. K., & Subbulakshmi, T. (2017). DDoS attack detection and mitigation techniques in cloud computing environment. In 2017, International Conference on Intelligent Sustainable Systems (ICISS), 512-517. https://doi.org/10.1109/ISS1.2017.8389464

[9]. Elsayed, M. S., & Azer, M. A. (2018). Detection and countermeasures of ddos attacks in cloud computing. In 2018, Tenth International Conference on Ubiquitous and Future Networks (ICUFN), 708-713. https://doi.org/10.1109/ICUFN.2018.8436989

[10]. Hamdani, F. N., & Siddiqui, F. (2019). Detection of DDOS attacks in cloud computing environment. In 2019, International Conference on Intelligent Computing and Control Systems (ICCS), 83-87. https://doi.org/10.1109/ICCS45141.2019.9065429

[11]. He, Z., Zhang, T., & Lee, R. B. (2017). Machine learning based DDoS attack detection from source side in cloud. In 2017, IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud), 114-120. https://doi.org/10.1109/CSCloud.2017.58

[12]. Hezavehi, S. M., & Rahmani, R. (2020). An anomalybased framework for mitigating effects of DDoS attacks using a third party auditor in cloud computing environments. Cluster Computing, 23(4), 2609-2627. https://doi.org/10.1007/s10586-019-03031-y

[13]. Jiao, J., Ye, B., Zhao, Y., Stones, R. J., Wang, G., Liu, X., Wang, S., & Xie, G. (2017). Detecting TCP-based DDoS attacks in baidu cloud computing data centers. In 2017, IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 256-258. https://doi.org/10.1109/SRDS.2017.37

[14]. Lee, Y. J., Baik, N. K., Kim, C., & Yang, C. N. (2018). Study of detection method for spoofed IP against DDoS attacks. Personal and Ubiquitous Computing, 22(1), 35-44. https://doi.org/10.1007/s00779-017-1097-y

[15]. Madhupriya, G., Shalinie, S. M., & Rajeshwari, A. R. (2018). Detecting DDoS attack in cloud computing using local outlier factors. In 2018, 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 859-863. https://doi.org/10.1109/ICOEI.2018.8553920

[16]. Makkawi, A. M., & Yousif, A. (2021). Machine Learning for Cloud DDoS Attack Detection: A Systematic Review. In 2020, International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), 1-9. https://doi.org/10.1109/ICCCEEE49695.2021.9429678

[17]. Mishra, A., Gupta, B. B., Peraković, D., Peñalvo, F. J. G., & Hsu, C. H. (2021). Classification based machine learning for detection of DDoS attack in cloud computing. In 2021, IEEE International Conference on Consumer Electronics (ICCE), 1-4. https://doi.org/10.1109/ICCE50685.2021.9427665

[18]. Mondal, H. S., Hasan, M. T., Hossain, M. B., Rahaman, M. E., & Hasan, R. (2017). Enhancing secure cloud computing environment by Detecting DDoS attack using fuzzy logic. In 2017, 3rd International Conference on Electrical Information and Communication Technology (EICT), 1-4. https://doi.org/10.1109/EICT.2017.8275211

[19]. Narwal, P., Singh, S. N., & Kumar, D. (2017). Gametheory based detection and prevention of DoS attacks on networking node in open stack private cloud. In 2017, International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), 481-486. https://doi.org/10.1109/ICTUS.2017.8286057

[20]. Paharia, B., & Bhushan, K. (2018). DDoS Detection and Mitigation in cloud via FogFiter: a defence mechanism. In 2018, 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-7. https://doi.org/10.1109/ICCCNT.2018.8493704

[21]. Patil, R., Dudeja, H., Gawade, S., & Modi, C. (2018). Protocol specific multi-threaded network intrusion detection system (PM-NIDS) for DoS/DDoS attack detection in cloud. In 2018, 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 1-7. https://doi.org/10.1109/ICCCNT.2018.8494130

[22]. Potluri, S., Mangla, M., Satpathy, S., & Mohanty, S. N. (2020). Detection and prevention mechanisms for DDoS attack in cloud computing environment. In 2020, 11th International Conference On Computing, Communication and Networking Technologies (ICCCNT), 1-6. https://doi.org/10.1109/ICCCNT49239.2020.9225396

[23]. Rengaraju, P., Ramanan, V. R., & Lung, C. H. (2017). Detection and prevention of DoS attacks in softwaredefined cloud networks. In 2017, IEEE Conference on Dependable and Secure Computing, 217-223. https://doi.org/10.1109/DESEC.2017.8073810

[24]. Salemi, H., Rostami, H., Talatian-Azad, S., & Khosravi, M. R. (2021). LEAESN: Predicting DDoS attack in healthcare systems based on lyapunov exponent analysis and echo state neural networks. Multimedia Tools and Applications, 1-22. https://doi.org/10.1007/s11042-020-10179-y

[25]. Sambangi, S., & Gondi, L. (2020). A machine learning approach for DDoS (distributed denial of service) attack detection using multiple linear regression. In Proceedings, (63)1, (pp. 51). https://doi.org/10.3390/proceedings2020063051

[26]. Saxena, R., & Dey, S. (2020). DDoS attack prevention using collaborative approach for cloud computing. Cluster Computing, 23(2), 1329-1344. https://doi.org/10.1007/s10586-019-02994-2

[27]. Soliman, A. K., Salama, C., & Mohamed, H. K. (2018). Detecting DNS reflection amplification DDoS attack originating from the cloud. In 2018, 13th International Conference on Computer Engineering and Systems (ICCES), 145-150. https://doi.org/10.1109/ICCES.2018.8639414

[28]. Sophia, G. A., & Gandhi, M. (2017). Stealthy DDoS detecting mechanism for cloud resilience system. In 2017, International Conference on Information Communication and Embedded Systems (ICICES), 1-5. https://doi.org/10.1109/ICICES.2017.8070740

[29]. Tajane, V., & Sharma, D. (2018). Effective detection and prevention of DDoS in cloud computing environment. In 2018, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 1-5. https://doi.org/10.1109/ICCUBEA.2018.8697346

[30]. Vijayalakshmi, J., & Robin, C. R. (2019). An exponent based error detection mechanism against DXDOS attack for improving the security in cloud. Cluster Computing, 22(2), 3749-3758. https://doi.org/10.1007/s10586-018-2261-5

[31]. Wani, A. R., Rana, Q. P., Saxena, U., & Pandey, N. (2019). Analysis and detection of DDoS attacks on cloud computing environment using machine learning techniques. In 2019, Amity International Conference on Artificial Intelligence (AICAI), 870-875. https://doi.org/10.1109/AICAI.2019.8701238

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Review Paper

Awareness of Cloud Computing among the Prospective Teachers in the Modern World

D. R. Robert Joan*

Christian College of Education, Marthandam, Tamil Nadu ,India.

Joan, D. R. R. (2022). Awareness of Cloud Computing among the Prospective Teachers in the Modern World. i-manager’s Journal on Cloud Computing, 9(1), 9-16. https://doi.org/10.26634/jcc.9.1.18654

Abstract

Cloud computing plays a vital role in education, politics, religion, transportation, relationships, medical check-ups, and so on. In education, online courses are carried out with the help of cloud computing. In cloud computing, expert conversations and classes delivered through any electronic device are protected on the server. The user can collect any information from the server through any search engine. There are so many platforms for downloading information for users. Electronic devices are very popular in this digital world. Some companies or individuals are introducing many applications for mobile phones and computers. One of the most useful options is search engines. It is very common to collect existing information on a server and it is a good communicator between users and cloud computing systems. Prospective teachers are the future teachers who will shape future citizens. The aim of the paper is to raise awareness of cloud computing among prospective teachers. This paper used a survey method to determine cloud computing awareness among prospective teachers and includes 61 samples from the College of Education. It uses the Cloud Computing Awareness Checklist tool to collect data for the present study. Hypotheses related to family type, locality, father's profession, father's education, and parents' income were found to have no significant differences. Thus, the result showed that prospective teachers have little knowledge of cloud computing. The researcher concluded that background variables are not significant. The essential things are the funds provided and the desire to learn. This will develop awareness and finally, the educator and prospective teachers raise awareness among prospective teachers and students, respectively. Educators encourage future teachers to collect more information about cloud computing on its own or with the help of an external facilitator.

References

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[8]. Srivastava, P., & Khan, R. (2018). A review paper on cloud computing. International Journal of Advanced Research in Computer Science and Software Engineering, 8(6), 17-20. https://doi.org/10.23956/ijarcsse.v8i6.711

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Review Paper

Enhancing Replica Management in a Cloud Environment using Data Mining Based Dynamic Replication Algorithm

D. Rambabu*

Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.

Rambabu, D. (2022). Enhancing Replica Management in a Cloud Environment using Data Mining Based Dynamic Replication Algorithm. i-manager’s Journal on Cloud Computing, 9(1), 17-25. https://doi.org/10.26634/jcc.9.1.18566

Abstract

Cloud computing has recently gained a lot of popularity and attention from the research community. One of the many on-demand services that large-scale applications provide to cloud customers is storage, which accumulates more generated data and subsequently leads to the need for storage. Despite the fact that users can use the cloud to store and provide the type of storage that desire, it still takes a significant amount of time to store and retrieve data due to the large accumulation of data. Due to the need to improve data availability, response time, reliability, and migration costs, the current storage engine needs to be replicated across multiple sites. When copies are properly distributed, data replication speeds up execution. The biggest challenges in data replication are choosing which data to replicate, where to put it, how to manage replication, and how many replicas it needs. Therefore, various studies have been carried out on some data mining-based data replication systems to evaluate replication issues and manage cloud storage. In most cases, data replication in a data mining environment is done using data mining along with a replication algorithm and a data grid policy. In addition, this paper addresses replica management issues and proposes affordable data replication in the cloud that satisfies all Quality of Service (QoS) requirements.

References

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[11]. Mansouri, N., & Javidi, M. M. (2018). A new prefetching-aware data replication to decrease access latency in cloud environment. Journal of Systems and Software, 144, 197-215. https://doi.org/10.1016/j.jss.2018.05.027

[12]. Mansouri, N., Rafsanjani, M. K., &Javidi, M. M. (2017). DPRS: A dynamic popularity aware replication strategy with parallel download scheme in cloud environments. Simulation Modelling Practice and Theory, 77, 177-196. https://doi.org/10.1016/j.simpat.2017.06.001

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Review Paper

Elasticity in the Cloud Related to Database Autonomies and Scalability

Swapnil Raj* , Jay**

*Department of Computer Science Engineering, SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India.

** Chandigarh University, Punjab, India.

Raj, S. and Raghav, A. K. (2022). Elasticity in the Cloud Related to Database Autonomies and Scalability. i-manager’s Journal on Cloud Computing, 9(1), 26-31. https://doi.org/10.26634/jcc.9.1.18719

Abstract

Cloud computing has been a very popular paradigm for implementing online applications. Scalability, elasticity, cost of use, and large-scale economies are the main reasons for the effective and widespread acceptance of cloud computing. In this paper, we outline our work to inject the aforementioned "cloud capabilities" into a database system designed to support various applications deployed in the cloud: designing scalable databases using autonomies database and elasticity that enables lightweight resiliency using low-cost live database migrations and an intelligent and autonomous controller designed for system management without human intervention.

References

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[5]. Garg, S., Gupta, D. V., & Dwivedi, R. K. (2016, November). Enhanced active monitoring load balancing algorithm for virtual machines in cloud computing. In 2016, International Conference System Modeling & Advancement in Research Trends (SMART) (pp. 339-344). IEEE. https://doi.org/10.1109/SYSMART.2016.7894546

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[13]. Sharma, P., Berwal, Y. P. S., & Ghai, W. (2019a). Enhancement of plant disease detection framework using cloud computing and GPU computing. International Journal of Engineering and Advanced Technology (IJEAT), 9(1), 3139-3141. https://doi.org/10.35940/ijeat.A9541.109119

[14]. Sharma, P., Berwal, Y. S., & Ghai, W. (2019b). Krishimitr: A cloud computing and platform for disease detection in agriculture. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(12), 2967-2970. https://doi.org/10.35940/ijitee.K1955.1081219.

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Review Paper

Cloud Computing Rising in the Field of Big Data and Artificial Intelligence

Swapnil Raj*

Department of Computer Science Engineering, SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India.

Raj, S. (2022). Cloud Computing Rising in the Field of Big Data and Artificial Intelligence. i-manager’s Journal on Cloud Computing, 9(1), 32-37. https://doi.org/10.26634/jcc.9.1.18727

Abstract

Cloud Computing is a strong, large-scale and complex Computing technology. It lessens the need to maintain an expensive, specialized computer hardware area, as well as expensive software and software. Cloud computing has shown a significant increase in data quality or the production of large amounts of data. Big data processing is a difficult and time-consuming operation that necessitates the use of a large computer system in order to ensure performance. Knowledge creation and analysis are two intertwined activities and this paper investigates the rise of Big Data and Artificial Intelligence (AI) in Cloud Computing research. As data storage and mining methods advance, the preservation of expanding data quantities is characterized by a change in the core of structured results. This shift is reflected in the evolution of structured results. However, one major barrier is that this rate of growth surpasses the ability of data gathering systems and cloud infrastructure platforms to be upgraded. Workloads are really heavy and it is possible that certain cloud computing disputes will be created, which will include the description, characteristics, and categorization of huge data. In addition, analysis problems based on data integrity, heterogeneity and protection.

References

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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

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Volume 9 Issue 1 January - June 2022

Detection of DDoS Attack in Cloud Computing and its Prevention: A Systematic Review

Ali Raza*

Department of Computer Science, Bahria University Karachi Campus, Karachi City, Sindh, Pakistan.

Raza, A. (2022). Detection of DDoS Attack in Cloud Computing and its Prevention: A Systematic Review. i-manager’s Journal on Cloud Computing, 9(1), 1-8. https://doi.org/10.26634/jcc.9.1.18542

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Awareness of Cloud Computing among the Prospective Teachers in the Modern World

D. R. Robert Joan*

Christian College of Education, Marthandam, Tamil Nadu ,India.

Joan, D. R. R. (2022). Awareness of Cloud Computing among the Prospective Teachers in the Modern World. i-manager’s Journal on Cloud Computing, 9(1), 9-16. https://doi.org/10.26634/jcc.9.1.18654

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Enhancing Replica Management in a Cloud Environment using Data Mining Based Dynamic Replication Algorithm

D. Rambabu*

Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, Hyderabad, Telangana, India.

Rambabu, D. (2022). Enhancing Replica Management in a Cloud Environment using Data Mining Based Dynamic Replication Algorithm. i-manager’s Journal on Cloud Computing, 9(1), 17-25. https://doi.org/10.26634/jcc.9.1.18566

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Elasticity in the Cloud Related to Database Autonomies and Scalability

Swapnil Raj* , Jay**

*Department of Computer Science Engineering, SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India. ** Chandigarh University, Punjab, India.

Raj, S. and Raghav, A. K. (2022). Elasticity in the Cloud Related to Database Autonomies and Scalability. i-manager’s Journal on Cloud Computing, 9(1), 26-31. https://doi.org/10.26634/jcc.9.1.18719

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Cloud Computing Rising in the Field of Big Data and Artificial Intelligence

Swapnil Raj*

Department of Computer Science Engineering, SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India.

Raj, S. (2022). Cloud Computing Rising in the Field of Big Data and Artificial Intelligence. i-manager’s Journal on Cloud Computing, 9(1), 32-37. https://doi.org/10.26634/jcc.9.1.18727

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*Department of Computer Science Engineering, SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India.

** Chandigarh University, Punjab, India.