Skip to main content
SpringerLink
Log in

Deep nonlinear regression least squares polynomial fit to detect malicious attack on IoT devices

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The explosion of IoT gadgets which be able to more effortlessly conceded than PCs has prompted an expansion in the existence of IoT-dependent botnet attacks. So as to alleviate this newfangled danger there remains a necessity to grow innovative techniques designed for identifying attacks propelled from conceded IoT gadgets in addition to distinguish among hour as well as millisecond elongated IoT-dependent attacks. Now we suggest and experimentally estimate a Deep Nonlinear Regression Least Squares Polynomial Fit to recognize peculiar system traffic originating as of conceded IoT gadgets. On the way to estimate our strategy, we contaminated 9 business IoT gadgets in our lab through 2 of the most generally acknowledged IoT-dependent botnets, Mirai and BASHLITE. Our estimated outcomes showed our suggested strategy's capacity to precisely and rapidly recognize the attacks as they were being propelled from the conceded IoT gadgets which remained a piece of a botnet. The tests show a truly accuracy 98.75%.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Arabo A, Pranggono B (2013) Mobile malware and smart device security: Trends, challenges and solutions. In 2013 19th International Conference on Control Systems and Computer Science pp 526–531

  • Bianco LJ (2016) The inherent weaknesses in industrial control systems devices; hacking and defending SCADA systems (Doctoral dissertation, Utica College)

  • Chen Y (2019) Comment on the work of Zhang et al. (2017, Journal of Inequalities and Applications). J Inequal Appl 2019:186. https://doi.org/10.1186/s13660-019-2142-3

    Article  MathSciNet  Google Scholar 

  • D’Angelo G, Palmieri F, Rampone S (2019) Detecting unfair recommendations in trust-based pervasive environments. Inf Sci 486:31–51

    Article  Google Scholar 

  • D’Angelo G, Ficco M, Palmieri F (2020) Malware detection in mobile environments based on Autoencoders and API-images. J Parallel and Distrib Comput 137:26–33

    Article  Google Scholar 

  • HaddadPajouh H, Dehghantanha A, Khayami R, Choo KKR (2018) A deep Recurrent neural network based approach for internet of things malware threat hunting. Future Gener Comput Syst 85:88–96

    Article  Google Scholar 

  • Hinton GE, Zemel RS (1994) Autoencoders, minimum description length, and helmholtz free energy. In: Cowan JD, Tesauro G, Alspector J (eds) Advances in neural information processing systems 6. Morgan Kaufmann, San Mateo, CA

    Google Scholar 

  • Macías-Escrivá FD, Haber R, Del Toro R, Hernandez V (2013) Self-adaptive systems: a survey of current approaches, research challenges and applications. Expert Syst Appl 40(18):7267–7279

    Article  Google Scholar 

  • Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning. Nature 518:529–533. https://doi.org/10.1038/nature14236

    Article  Google Scholar 

  • Moh M, Raju R (2018) Machine learning techniques for security of Internet of Things (IoT) and fog computing systems. In 2018 International Conference on High Performance Computing and Simulation (HPCS) pp 709–715.

  • Nguyen A (2016) A plan to prevent cyber criminals from sucessfully attacking the internet of things from the supply chain (Doctoral dissertation, Utica College).

  • Su J, Vasconcellos V, Prasad S, Daniele S, Feng Y, Sakurai K (2018) Lightweight classification of IoT malware based on image recognition, pp 664–669. https://doi.org/10.1109/COMPSAC.2018.10315

  • Vinod P, Zemmari A, Conti M (2019) A machine learning based approach to detect malicious android apps using discriminant system calls. Future Gener Comput Syst 94:333–350

    Article  Google Scholar 

  • Wei D, Qiu X (2018) Status-based detection of malicious code in internet of things (IoT) devices, pp 1–7. https://doi.org/10.1109/CNS.2018.8433183

  • Yang C, Xu Z, Gu G, Yegneswaran V, Porras P, Droidminer A (2014) Automated mining and characterization of fine-grained malicious behaviors in android applications, In European Symposium on Research in Computer Security pp 163–182.

  • Zolotukhin M, Hamalainen T (2018) On artificial intelligent malware tolerant networking for IoT, pp 1–6. https://doi.org/10.1109/NFV-SDN.2018.8725767

Download references

Author information

Authors and Affiliations

  1. Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, 641014, India

    E. Arul

Authors
  1. E. Arul
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to E. Arul.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arul, E. Deep nonlinear regression least squares polynomial fit to detect malicious attack on IoT devices. J Ambient Intell Human Comput 12, 769–779 (2021). https://doi.org/10.1007/s12652-020-02075-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-02075-y

Keywords

Search

Navigation