Skip to main content
SpringerLink
Log in

An Invasion Detection System in the Cloud That Use Secure Hashing Techniques

  • Conference paper
  • First Online:
Intelligent Data Engineering and Analytics (FICTA 2023)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 371))

  • 148 Accesses

Abstract

The same basic issue of inadequate protection affects all businesses, but current enemies are making major attempts to breach these barriers in order to participate in unlawful insider trading. They recognise a wide range of conduits for information theft. In the present day, intrusion into private information is more likely. While there are many safeguards against various attacks, hackers are always coming up with new ways to get past current barriers. As a result, an effort has been made in this essay to develop a unique strategy that would be particularly resistant to such an onslaught. The proposed plan calls for the use of a hash map-based intrusion detection system. The object is hashed and then saved as a shared key in this system. Nowadays, a major problem is the secure movement of data. Before uploading their files, users in the data-sharing system have the option to encrypt them using their own personal keys. In this paper, a secure and effective use of the approach is shown together with a security proof. Owners of data face several challenges when trying to make their data accessible through server or cloud storage. Numerous approaches may be used to deal with the problems. Multiple techniques are needed for the secure maintenance of a shared key that belongs to the owner of the data. This article has examined the idea of using a trusted authority to confirm the users of cloud data are who they claim to be. The key will be generated by the trusted authority using the SHA algorithm, and it will then be given to the user and the owner. After receiving an AES-encrypted file from the data owner, the certified authority system uses the MD-5 algorithm to obtain the hash value.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Medical Data in the Crosshairs: Why Is Healthcare an Ideal Target? 14 Aug 2021. Available at: https://www.trendmicro.com/vinfo/us/security/news/cyber-attacks/medical-data-in-the-crosshairs-why-is-healthcare-an-ideal-target. Accessed 15 May 2020

  2. Conaty-Buck, S.: Cybersecurity and healthcare records. Am. Nurse Today 12, 62–64 (2017)

    Google Scholar 

  3. eGOVERNMENT: Cloud Computing Initiatives, 22 April 2021. Available at: https://www.bahrain.bh/. Accessed 15 July 2021

  4. Moukhafi, M., El Yassini, K., Bri, S.: A novel hybrid GA and SVM with PSO feature selection for intrusion detection system. Int. J. Adv. Sci. Res. Eng. 4, 129–134 (2018)

    Google Scholar 

  5. Kuang, F., Xu, W., Zhang, S.: A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl. Soft Comput. J. 18, 178–184 (2014)

    Article  Google Scholar 

  6. Al-Yaseen, W.L., Othman, Z.A., Nazri, M.Z.A.: Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Syst. Appl. 67, 296–303 (2017)

    Article  Google Scholar 

  7. Feng, W., Zhang, Q., Hu, G., Huang, J.X.: Mining network data for intrusion detection through combining SVMs with ant colony networks. Future Gener. Comput. Syst. 37, 127–140 (2014)

    Article  Google Scholar 

  8. Ambusaidi, M.A., He, X., Nanda, P., Tan, Z.: Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Trans. Comput. 65, 2986–2998 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  9. Mustapha, B., Salah, E.H., Mohamed, I.: A two-stage classifier approach using RepTree algorithm for network intrusion detection. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 8, 389–394 (2017)

    Google Scholar 

  10. Tuan, A., McLernon, D., Mhamdi, L., Zaidi, S.A.R., Ghogho, M.: Intrusion detection in SDN-based networks: deep recurrent neural network approach. In: Advanced Sciences and Technologies for Security Applications, pp. 175–195. Springer, Berlin/Heidelberg, Germany (2019)

    Google Scholar 

  11. Nguyen, K.K., Hoang, D.T., Niyato, D., Wang, P., Nguyen, D., Dutkiewicz, E.: Cyberattack detection in mobile cloud computing: a deep learning approach. In: Proceedings of the IEEE Wireless Communications and Networking Conference, Barcelona, Spain, 15–18 April 2018

    Google Scholar 

  12. He, D., Qiao, Q., Gao, Y., Zheng, J., Chan, S., Li, J., Guizani, N.: Intrusion detection based on stacked autoencoder for connected healthcare systems. IEEE Netw. 33, 64–69 (2019)

    Article  Google Scholar 

  13. Mudzingwa, D., Agrawal, R.: A study of methodologies used in intrusion detection and prevention systems (IDPS). In: Proceedings of the IEEE, Southeastcon, Orlando, FL, USA, 15–18 Mar 2012

    Google Scholar 

  14. Aljawarneh, S., Aldwairi, M., Yassein, M.B.: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. J. Comput. Sci. 25, 152–160 (2018)

    Article  Google Scholar 

  15. Ludinard, R., Totel, É., Tronel, F., Nicomette, V., Kaâniche, M., Alata, É., Bachy, Y.: Detecting attacks against data in web applications. In: Proceedings of the 7th International Conference on Risks and Security of Internet and Systems (CRiSIS), Cork, Ireland, 10–12 Oct 2012

    Google Scholar 

  16. Li, X., Xue, Y., Malin, B.: Detecting anomalous user behaviors in workflow-driven web applications. In: Proceedings of the IEEE Symposium on Reliable Distributed Systems, Irvine, CA, USA, 8–11 Oct 2012, pp. 1–10

    Google Scholar 

  17. Le, M., Stavrou, A., Kang, B.B.: DoubleGuard: detecting intrusions in multitier web applications. IEEE Trans. Dependable Secur. Comput. 9, 512–525 (2012)

    Article  Google Scholar 

  18. Nascimento, G., Correia, M.: Anomaly-based intrusion detection in software as a service. In: Proceedings of the 2011 IEEE/IFIP 41st International Conference on Dependable Systems and Networks Workshops (DSN-W), Hong Kong, China, 27–30 June 2011, pp. 19–24

    Google Scholar 

  19. Ariu, D.: Host and Network Based Anomaly Detectors for HTTP Attacks. Ph.D. Thesis, University of Cagliari, Cagliari, Italy (2010)

    Google Scholar 

  20. Gimenez, C., Villaegas, A., Alvarez, G.: An anomaly-based approach for intrusion detection in web traffic. J. Inf. Assur. Secur. 5, 446–454 (2010)

    Google Scholar 

  21. Saraniya, G.: Securing the network using signature based IDS in network IDS. Shodhshauryam Int. Sci. Ref. Res. J. 2, 99–101 (2019)

    Google Scholar 

  22. Gao, W., Morris, T.: On cyber attacks and signature based intrusion detection for MODBUS based industrial control systems. J. Digit. Forens. Secur. Law 9, 37–56 (2014)

    Google Scholar 

  23. Uddin, M., Rehman, A.A., Uddin, N., Memon, J., Alsaqour, R., Kazi, S.: Signature-based multi-layer distributed intrusion detection system using mobile agents. Int. J. Netw. Secur. 15, 97–105 (2013)

    Google Scholar 

  24. Kumar, U., Gohil, B.N.: A survey on intrusion detection systems for cloud computing environment. Int. J. Comput. Appl. 109, 6–15 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Koneru Lakshmaiah Education Foundation, GreenFields, Vaddeswaram, AP, 522302, India

    Sridevi Sakhamuri, Sridevi Sakhamuri, Gopi Krishna Yanala, Varagani Durga Shyam Prasad, Ch. Bala Subrmanyam & Aika Pavan Kumar

Authors
  1. Sridevi Sakhamuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gopi Krishna Yanala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Varagani Durga Shyam Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ch. Bala Subrmanyam
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aika Pavan Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sridevi Sakhamuri .

Editor information

Editors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Technology (UNSIET), Veer Bahadur Singh Purvanchal University, Jaunpur, Uttar Pradesh, India

    Vikrant Bhateja

  2. Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Fiona Carroll

  3. Faculty of Engineering, University of Porto, Porto, Portugal

    João Manuel R. S. Tavares

  4. Cardiff Metropolitan University, Cardiff, Warwickshire, UK

    Sandeep Singh Sengar

  5. Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia

    Peter Peer

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Sakhamuri, S., Krishna Yanala, G., Durga Shyam Prasad, V., Bala Subrmanyam, C., Pavan Kumar, A. (2023). An Invasion Detection System in the Cloud That Use Secure Hashing Techniques. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_22

Download citation

Publish with us

Policies and ethics

Search

Navigation