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A Perspective for Intrusion Detection & Prevention in Cloud Environment


Affiliations
1 Research Scholar, Department of Computer Science, Punjabi University, Patiala, India
2 Assistant Professor, Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India
 

The cloud environment is used in all sectors that provide different services to the users. The assistance provided by the cloud environment in different sectors such as business, entertainment, government, education, IT industry, etc. The services rendered by both the public and private organizations considering scalable, on a payas-you-go basis, on-demand services, etc. Due to its dispersed nature and viability in all the sectors, makes the system inefficient which causes numerous attacks in the environment. These attacks affect the confidentiality, integrity, and availability of cloud resources. Some examples of attacks are Ransomware, man-in-the-middle attacks, Denial of service attacks, insider attacks, etc. Thus, Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) play a crucial role in the cloud environment by detecting and preventing the system from suspicious attacks. The objective of this paper is to provide information about attacks that affect the cloud environment. This paper also covers the different techniques of intrusion detection, intrusion prevention, and its hybrid approach.

Keywords

Cloud Computing, Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Intrusion Detection and Prevention System (IDPS)
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  • J. K. Samriya and N. Kumar, “A novel intrusion detection system using hybrid clustering optimization approach in cloud computing,” Mater. Today Proc., no. xxxx, 2020, doi: 10.1016/j.matpr.2020.09.614.

  • P. Singh and V. Ranga, “Attack and intrusion detection in cloud computing using an ensemble learning approach,” Int. J. Inf. Technol., 2021, doi: 10.1007/s41870-020-00583-w.

  • K. Pradeep Mohan Kumar, M. Saravanan, M. Thenmozhi, and K. Vijayakumar, “Intrusion detection system based on GA-fuzzy classifier for detecting malicious attacks,” Concurr. Comput. , vol. 33, no. 3, pp. 5–10, 2021, doi: 10.1002/cpe.5242.

  • S. R. K. Tummalapalli and A. S. N. Chakravarthy, “Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN,” Evol. Intell., no. 0123456789, 2020, doi: 10.1007/s12065-020-00410-y.

  • G. Zhao, C. Rong, M. G. Jaatun, and F. E. Sandnes, “Reference deployment models for eliminating user concerns on cloud security,” J. Supercomput., vol. 61, no. 2, pp. 337–352, 2012, doi: 10.1007/s11227-010-0460-9.

  • C. N. Modi, D. R. Patel, A. Patel, and M. Rajarajan, “Integrating Signature Apriori based Network Intrusion Detection System (NIDS) in Cloud Computing,” Procedia Technol., vol. 6, pp. 905–912, 2012, doi: 10.1016/j.protcy.2012.10.110.

  • C. Point and S. Technologies, “SECURITY,” pp. 1–21, 2020.

  • P. Sharma, J. Sengupta, and P. K. Suri, “WLIFCM and Artificial Neural Network Based Cloud Intrusion Detection System,” Int. J. Adv. Netw. Appl., vol. 10, no. 01, pp. 3698–3703, 2018, doi: 10.35444/ijana.2018.10014.

  • S. M. Alturfi, D. K. Muhsen, M. A. Mohammed, I. T. Aziz, and M. Aljshamee, “A Combination Techniques of Intrusion Prevention and Detection for Cloud Computing,” J. Phys. Conf. Ser., vol. 1804, no. 1, 2021, doi: 10.1088/1742- 6596/1804/1/012121.

  • C. Modi, D. Patel, B. Borisaniya, A. Patel, and M. Rajarajan, “A survey on security issues and solutions at different layers of Cloud computing,” J. Supercomput., vol. 63, no. 2, pp. 561–592, 2013, doi: 10.1007/s11227-012-0831-5.

  • A. Patil, A. Laturkar, S. V. Athawale, R. Takale, and P. Tathawade, “A multilevel system to mitigate DDOS, brute force and SQL injection attack for cloud security,” IEEE Int. Conf. Information, Commun. Instrum. Control. ICICIC 2017, vol. 2018-Janua, pp. 1–7, 2018, doi: 10.1109/ICOMICON.2017.8279028.

  • A. Gursaran, “U Sing G Enetic a Lgorithm,” vol. 3, no. 3, pp. 447–450, 2010.

  • C. Modi, D. Patel, B. Borisaniya, and H. Patel, “Journal of Network and Computer Applications A survey of intrusion detection techniques in Cloud,” J. Netw. Comput. Appl., vol. 36, no. 1, pp. 42–57, 2013, doi: 10.1016/j.jnca.2012.05.003.

  • K. W. I and V. Hou, “Detection Method of SQL injection Attack in Cloud,” pp. 487–493.

  • C. N. Modi and K. Acha, “Virtualization layer security challenges and intrusion detection/prevention systems in cloud computing: a comprehensive review,” J. Supercomput., vol. 73, no. 3, pp. 1192–1234, 2017, doi: 10.1007/s11227-016-1805-9.

  • S. Alam, M. Shuaib, and A. Samad, A Collaborative Study of Intrusion Detection and Prevention Techniques in Cloud Computing. Springer Singapore.

  • N. Das and T. Sarkar, “Survey on Host and Network Based Intrusion Detection System,” Int. J. Adv. Netw. Appl., vol. 6, no. 2, pp. 2266 –2269, 2014.

  • P. P. Chapke and R. R. Deshmukh, “Intrusion Detection System using fuzzy logic and data mining technique,” ACM Int. Conf. Proceeding Ser., vol. 06 -07 -Marc, 2015, doi: 10.1145/2743065.2743128.

  • K. Bakour, G. S. Daş, and H. M. Ünver, “An intrusion detection system based on a hybrid tabu - genetic algorithm,” 2nd Int. Conf. Comput. Sci. Eng. UBMK 2017, pp. 215 –220, 2017, doi: 10.1109/UBMK.2017.8093378.

  • H. Jin et al., “A VMM -based intrusion prevention system in cloud computing environment,” J. Supercomput., vol. 66, no. 3, pp. 1133 –1151, 2013, doi: 10.1007/s11227 -011 -0608 -2.

  • S. Das and M. J. Nene, “A survey on types of machine learning techniques in intrusion prevention systems,” Proc. 2017 Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2017, vol. 2018 -Janua, pp. 2296 –2299, 2018, doi: 10.1109/WiSPNET.2017.8300169.

  • A. Patel, M. Taghavi, K. Bakhtiyari, and J. Celestino Júnior, “An intrusion detection and prevention system in cloud computing: A systematic review,” J. Netw. Comput. Appl., vol. 36, no. 1, pp. 25 –41, 2013, doi: 10.1016/j.jnca.2012.08.007.


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  • A Perspective for Intrusion Detection & Prevention in Cloud Environment

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Authors

Vaneeta
Research Scholar, Department of Computer Science, Punjabi University, Patiala, India
Sangeeta Rani
Assistant Professor, Department of Computer Science, Mata Gujri College, Fatehgarh Sahib, India

Abstract


The cloud environment is used in all sectors that provide different services to the users. The assistance provided by the cloud environment in different sectors such as business, entertainment, government, education, IT industry, etc. The services rendered by both the public and private organizations considering scalable, on a payas-you-go basis, on-demand services, etc. Due to its dispersed nature and viability in all the sectors, makes the system inefficient which causes numerous attacks in the environment. These attacks affect the confidentiality, integrity, and availability of cloud resources. Some examples of attacks are Ransomware, man-in-the-middle attacks, Denial of service attacks, insider attacks, etc. Thus, Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) play a crucial role in the cloud environment by detecting and preventing the system from suspicious attacks. The objective of this paper is to provide information about attacks that affect the cloud environment. This paper also covers the different techniques of intrusion detection, intrusion prevention, and its hybrid approach.

Keywords


Cloud Computing, Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Intrusion Detection and Prevention System (IDPS)

References