Vulnerabilities and attacks on the blockchain software engineering landscape

Research Article
Open access

Vulnerabilities and attacks on the blockchain software engineering landscape

Maheshwari V. 1 , Prasanna M. 2*
  • 1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.    
  • 2 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.    
  • *corresponding author prasanna.m@vit.ac.in
Published on 14 June 2023 | https://doi.org/10.54254/2755-2721/6/20230851
ACE Vol.6
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-59-1
ISBN (Online): 978-1-915371-60-7

Abstract

Blockchain is also known as Distributed Ledger Technology (DLT) and real transparencies of the history of digital assets by decentralization and encryption. It guarantees that the user's data never be erased, making it impossible to alter or falsify. Some people know that the "Blockchain revolution" can be compared with the internet and the web in their early days. As a result, all software development around blockchain is growing incredibly. Most software engineers are interested in blockchain technologies as they rush to develop unregulated software. Although some research has been performed on blockchain security and privacy concerns, a thorough analysis state of blockchain security is lacking. This article explores current problems and new principles for blockchain-oriented software engineering (BOSE) and discusses the need for new software engineering practices in the blockchain industry. Also, examine the solutions to improve blockchain protection, which might have been used to develop different blockchain applications, and suggest a few potential directions for moving research into this area.

Keywords:

Blockchain-oriented software engineering (BOSE), Distributed Ledger Technology (DLT), Decentralization, Security

V.,M.;M.,P. (2023). Vulnerabilities and attacks on the blockchain software engineering landscape. Applied and Computational Engineering,6,422-427.
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References

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[11]. Rajalaxmi, R. R., Narasimha Prasad, L. V., Janakiramaiah, B., Pavankumar, C. S., Neelima, N., & Sathishkumar, V. E. (2022). Optimizing Hyperparameters and Performance Analysis of LSTM Model in Detecting Fake News on Social media. Transactions on Asian and Low-Resource Language Information Processing.

[12]. Shanthi, N., VE, S., Upendra Babu, K., Karthikeyan, P., Rajendran, S., & Allayear, S. M. (2022). Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques. Computational Intelligence & Neuroscience.

[13]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

[14]. Karrothu, A., Anilkumar, C., & Sathishkumar, V. E. (2022). An Escrow-Free and Authenticated Group Key Management in Internet of Things. In Smart Intelligent Computing and Applications, Volume 2 (pp. 505-512). Springer, Singapore.

[15]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

[16]. Zhang, M., Wang, X., Sathishkumar, V. E., & Sivakumar, V. (2021). Machine learning techniques based on security management in smart cities using robots. Work, 68(3), 891-902.


Cite this article

V.,M.;M.,P. (2023). Vulnerabilities and attacks on the blockchain software engineering landscape. Applied and Computational Engineering,6,422-427.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN:978-1-915371-59-1(Print) / 978-1-915371-60-7(Online)
Editor:Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.6
ISSN:2755-2721(Print) / 2755-273X(Online)

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References

[1]. Sathishkumar, V. E., Park, J., & Cho, Y. (2020). Using data mining techniques for bike sharing demand prediction in metropolitan city. Computer Communications, 153, 353-366.

[2]. Senthilkumar, K., & Easwaramoorthy, S. (2017, November). A Survey on Cyber Security awareness among college students in Tamil Nadu. In IOP Conference Series: Materials Science and Engineering (Vol. 263, No. 4, p. 042043). IOP Publishing.

[3]. VE, S., & Cho, Y. (2020). A rule-based model for Seoul Bike sharing demand prediction using weather data. European Journal of Remote Sensing, 53(sup1), 166-183.

[4]. Krishnamoorthy, N., Prasad, L. N., Kumar, C. P., Subedi, B., Abraha, H. B., & Sathishkumar, V. E. (2021). Rice leaf diseases prediction using deep neural networks with transfer learning. Environmental Research, 198, 111275.

[5]. VE, S., Shin, C., & Cho, Y. (2021). Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city. Building Research & Information, 49(1), 127-143.

[6]. VE, S., Park, J., & Cho, Y. (2020). Seoul bike trip duration prediction using data mining techniques. IET Intelligent Transport Systems, 14(11), 1465-1474.

[7]. Easwaramoorthy, S., Sophia, F., & Prathik, A. (2016, February). Biometric Authentication using finger nails. In 2016 international conference on emerging trends in engineering, technology and science (ICETETS) (pp. 1-6). IEEE.

[8]. Easwaramoorthy, S., Thamburasa, S., Samy, G., Bhushan, S. B., & Aravind, K. (2016, April). Digital forensic evidence collection of cloud storage data for investigation. In 2016 International Conference on Recent Trends in Information Technology (ICRTIT) (pp. 1-6). IEEE.

[9]. VE, S., & Cho, Y. (2020). Season wise bike sharing demand analysis using random forest algorithm. Computational Intelligence.

[10]. Subedi, B., Sathishkumar, V. E., Maheshwari, V., Kumar, M. S., Jayagopal, P., & Allayear, S. M. (2022). Feature learning-based generative adversarial network data augmentation for class-based few-shot learning. Mathematical Problems in Engineering, 2022.

[11]. Rajalaxmi, R. R., Narasimha Prasad, L. V., Janakiramaiah, B., Pavankumar, C. S., Neelima, N., & Sathishkumar, V. E. (2022). Optimizing Hyperparameters and Performance Analysis of LSTM Model in Detecting Fake News on Social media. Transactions on Asian and Low-Resource Language Information Processing.

[12]. Shanthi, N., VE, S., Upendra Babu, K., Karthikeyan, P., Rajendran, S., & Allayear, S. M. (2022). Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques. Computational Intelligence & Neuroscience.

[13]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E. (2022). Visiting Indian Hospitals Before, During and After COVID. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.

[14]. Karrothu, A., Anilkumar, C., & Sathishkumar, V. E. (2022). An Escrow-Free and Authenticated Group Key Management in Internet of Things. In Smart Intelligent Computing and Applications, Volume 2 (pp. 505-512). Springer, Singapore.

[15]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

[16]. Zhang, M., Wang, X., Sathishkumar, V. E., & Sivakumar, V. (2021). Machine learning techniques based on security management in smart cities using robots. Work, 68(3), 891-902.