DNNLSTM Algorithms for Comparative Study on Machine Learning Approach for Malicious Study
Keywords:
E-commerce systems, Security, Machine learning, Malicious CodeAbstract
E-commerce systems, utilizing the Internet for various functions such as banking, shopping, and sales, offer numerous advantages to customers. However, security remains a challenge, as the numerous advantages of these systems come with the potential for cyber threats. Therefore, it is crucial to carefully consider the security measures in these systems. Online transactions in the e-commerce system are increasing rapidly, posing a significant risk to consumers' personal information. To ensure the Internet safety, it is crucial to address vulnerabilities in the system's components, which can be accidental or malicious, at entry and exit points. The number of threats in the system was well managed through the usage of machine learning techniques, ensuring efficient detection and prevention. E-commerce systems require robust security measures to ensure their functionality and safety, a task that researchers working in this field are well-equipped to tackle. This paper focuses on analysing security in e-commerce systems using machine learning.
Downloads
References
Akhtar, M.S.; Feng, T. Detection of sleep paralysis by using IoT based device and its relationship between sleep paralysis and sleep quality. EAI Endorsed Trans. Internet Things 2022, 8, e4.
Wang, J., Deng, P., Fan, Y., Jaw, L., Liu, Y.: Virus detection using data mining techniques. In: Proceedings of IEEE International Conference on Data Mining (2003).
Chen, X., Andersen, J., Mao, Z., Bailey, M., Nazario, J.: Towards an understanding of anti-virtualization and anti-debugging behavior in modern malware. In: DSN (2008).
P. Laskov, C. Gehl, S. Krüger et al., “Incremental support vector learning: analysis, implementation and applications,” Journal of Machine Learning Research, vol. 7, no. 3, p. 2006, 2006.
M. Alazab, “Profiling and classifying the behavior of malicious codes,” Journal of Systems & Software, vol. 100, pp. 91–102, 2014.
G. Liang, J. Pang, Z. Shan, R. Yang, and Y. Chen, “Automatic benchmark generation framework for malware detection,” Security and Communication Networks, vol. 2018, Article ID 4947695, 8 pages, 2018.
D. Xue, J. Li, T. Lv, W. Wu, and J. Wang, “Malware classification using probability scoring and machine learning,” IEEE Access, vol. 7, pp. 91641–91656, 2019.
Zhao, K.; Zhang, D.; Su, X.; Li, W. Fest: A feature extraction and selection tool for android malware detection. In Proceedings of the 2015 IEEE Symposium on Computers and Communication (ISCC), Larnaca, Cyprus, 6–9 July 2015; pp. 714–720.
D. Xue, J. Li, W. Wu et al., “Homology analysis of malware based on ensemble learning and multifeatures,” PLoS One, vol. 14, no. 8, Article ID e0211373, 2019.
M. Ristin, M. Guillaumin, J. Gall, and L. Van Gool, “Incremental learning of random forests for large-scale image classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 3, pp. 490–503, 2016.
Nikam, U.V.; Deshmuh, V.M. Performance evaluation of machine learning classifiers in malware detection. In Proceedings of the 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), Ballari, India, 23–24 April 2022; pp. 1–5.
Sharma, S.; Krishna, C.R.; Sahay, S.K. Detection of advanced by machine learning techniques. In Proceedings of the SoCTA 2017, Jhansi, India, 22–24 December 2017.
Chandrakala, D.; Sait, A.; Kiruthika, J.; Nivetha, R. Detection and classification of malware. In Proceedings of the 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), Coimbatore, India, 8–9 October 2021; pp. 1–3.
Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P.G., Alvarez, G.: PUMA: permission usage to detect Malware in Android. In: Advances in Intelligent Systems and Computing (AISC) (2012)
Akhtar, M.S.; Feng, T. IOTA based anomaly detection machine learning in mobile sensing. EAI Endorsed Trans. Create. Tech. 2022, 9, 172814.
Sethi, K.; Kumar, R.; Sethi, L.; Bera, P.; Patra, P.K. A novel machine learning based malware detection and classification framework. In Proceedings of the 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Oxford, UK, 3–4 June 2019; pp. 1–13.
Sethi, K.; Kumar, R.; Sethi, L.; Bera, P.; Patra, P.K. A novel machine learning based malware detection and classification framework. In Proceedings of the 2019 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), Oxford, UK, 3–4 June 2019; pp. 1–13.
F. Behgounia, B. J. I. J. o. C. S. Zohuri, and I. Security, “Machine Learning Driven An E-Commerce,” International Journal of Computer Science and Information Security (IJCSIS), vol. 18, no. 10, 2020.
M. Zhang, L. Lin, and Z. J. C. C. Chen, “Lightweight security scheme for data management in E-commerce platform using dynamic data management using blockchain model,” Lightweight security scheme for data management in E-commerce platform using dynamic data management using blockchain model." Cluster Computing (2021): 1-15., pp. 1-15, 2021.
N. L. Bhatia, V. K. Shukla, R. Punhani et al., "Growing Aspects of Cyber Security in E-Commerce." pp. 1-6.
M. Li, L. Zhu, Z. Zhang et al., “Anonymous and verifiable reputation system for E-commerce platforms based on blockchain,” IEEE Transactions on Network and Service Management, vol. 18, no. 4, pp. 4434-4449, 2021.
M. J. Girsang, R. Hendayani, and Y. Ganesan, "Can Information Security, Privacy and Satisfaction Influence The E-Commerce Consumer Trust?." pp. 1-7.
Z. Zhu, Y. Bai, W. Dai et al., “Quality of e-commerce agricultural products and the safety of the ecological environment of the origin based on 5G Internet of Things technology,” Environmental Technology & Innovation, 22, 101462., vol. 22, pp. 101462, 2021.
L. T. T. J. J. o. R. Tran, and C. Services, “Managing the effectiveness of e-commerce platforms in a pandemic,” Journal of Retailing and Consumer Services 58 (2021): 102287, vol. 58, pp. 102287, 2021.
A. R. Khan, M. Kashif, R. H. Jhaveri et al., “Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions,” vol. 2022, 2022.
Y. Otoum, D. Liu, and A. J. T. o. E. T. T. Nayak, “DL‐IDS: a deep learning–based intrusion detection framework for securing IoT,” vol. 33, no. 3, pp. e3803, 2022.
L. Sama, H. Wang, and P. Watters, "Enhancing System Security by Intrusion Detection Using Deep Learning." pp. 169-176.
M. Imran, N. Haider, M. Shoaib et al., “An intelligent and efficient network intrusion detection system using deep learning,” vol. 99, pp. 107764, 2022.
I. Ullah, and Q. H. J. I. A. Mahmoud, “Design and development of a deep learning-based model for anomaly detection in IoT networks,” vol. 9, pp. 103906-103926, 2021.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
