Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier For Secured Big Data Communication
Main Article Content
Abstract
The application of big data analytics and related technologies like the Internet of Things (IoT) facilitates user intentions and behaviors as well as operational decision-making. Security is the major concern in the application of big data analytics to protect the system and secure the information as well as the data being handled. Conventional security techniques have become inefficient in terms of processing and identifying network threats in a reasonable amount of time. To deal with this problem, a unique Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier based Secured Data Communication (CSDECGBAC-SDC) model with improved accuracy and lower time complexity is introduced. The CSDECGBAC-SDC model's core functions for enhancing security include user registration, data collection, and data communication. During the registration process, the user's information is initially registered. Following that, the CSDECGBAC-SDC model collects data from the enrolled user. The Chi-Square Detective Ensembled Cardinal Gradient Bootstrap Aggregating Classifier is used in the CSDECGBAC-SDC Model to accomplish user authentication for anyone who want to access the data. For detecting the authorized user, the ensemble technique uses a group of weak learners as a Tversky Indexive Chi-square automatic interaction detection decision tree. The weak learner results are combined. Finally, cardinal voting is applied to find the majority vote in data classification by using the gradient ascent function. This in turn helps to improve secured data communication. Experimental evaluation is carried out on factors such as classification accuracy, error rate, and classification time with respect to a number of users. The results indicate that the CSDECGBAC-SDC model effectively improves the classification accuracy with minimum error rate and classification time than the conventional approaches.
Article Details
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References
Swapna, S. L., and V. Saravanan. “Survival Analysis on Secured Data Communication in Cloud.” International Journal of Computer Applications, vol. 183, no. 46, Foundation of Computer Science, Jan. 2022, pp. 31–35. Crossref, https://doi.org/10.5120/ijca2022921864.
Xie, Hui, et al. “HBRSS: Providing High-secure Data Communication and Manipulation in Insecure Cloud Environments.” Computer Communications, vol. 174, Elsevier BV, June 2021, pp. 1–12. Crossref, https://doi.org/10.1016/j.comcom.2021.03.018.
Alsahlani, Ahmed Yaser Fahad, and Alexandru Popa. “LMAAS-IoT: Lightweight Multi-factor Authentication and Authorization Scheme for Real-time Data Access in IoT Cloud-based Environment.” Journal of Network and Computer Applications, vol. 192, Elsevier BV, Oct. 2021, p. 103177. Crossref, https://doi.org/10.1016/j.jnca.2021.103177.
Amanullah, Mohamed Ahzam, et al. “Deep Learning and Big Data Technologies for IoT Security.” Computer Communications, vol. 151, Elsevier BV, Feb. 2020, pp. 495–517. Crossref, https://doi.org/10.1016/j.comcom.2020.01.016.
Rosado, David G., et al. “MARISMA-BiDa Pattern: Integrated Risk Analysis for Big Data.” Computers & Security, vol. 102, Elsevier BV, Mar. 2021, p. 102155. Crossref, https://doi.org/10.1016/j.cose.2020.102155.
Liang, Wuchao, et al. “Information Security Monitoring and Management Method Based on Big Data in the Internet of Things Environment.” IEEE Access, vol. 9, Institute of Electrical and Electronics Engineers (IEEE), 2021, pp. 39798–812. Crossref, https://doi.org/10.1109/access.2021.3064350.
El Alaoui, Imane, and Youssef Gahi. “Network Security Strategies in Big Data Context.” Procedia Computer Science, vol. 175, Elsevier BV, 2020, pp. 730–36. Crossref, https://doi.org/10.1016/j.procs.2020.07.108.
Narayanan, Uma, et al. “A Novel System Architecture for Secure Authentication and Data Sharing in Cloud Enabled Big Data Environment.” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 6, Elsevier BV, June 2022, pp. 3121–35. Crossref, https://doi.org/10.1016/j.jksuci.2020.05.005.
Razaque, Abdul, et al. “Big Data Handling Approach for Unauthorized Cloud Computing Access.” Electronics, vol. 11, no. 1, MDPI AG, Jan. 2022, p. 137. Crossref, https://doi.org/10.3390/electronics11010137.
Sharma, Rohit, and Rajeev Arya. “Secure Transmission Technique for Data in IoT Edge Computing Infrastructure.” Complex & Intelligent Systems, vol. 8, no. 5, Springer Science and Business Media LLC, Nov. 2021, pp. 3817–32. Crossref, https://doi.org/10.1007/s40747-021-00576-7.
Fang, Dongfeng, et al. “A Flexible and Efficient Authentication and Secure Data Transmission Scheme for IoT Applications.” IEEE Internet of Things Journal, vol. 7, no. 4, Institute of Electrical and Electronics Engineers (IEEE), Apr. 2020, pp. 3474–84. Crossref, https://doi.org/10.1109/jiot.2020.2970974.
Deebak, B. D., and Fadi AL-Turjman. “Lightweight Authentication for IoT/Cloud-based Forensics in Intelligent Data Computing.” Future Generation Computer Systems, vol. 116, Elsevier BV, Mar. 2021, pp. 406–25. Crossref, https://doi.org/10.1016/j.future.2020.11.010.
Zhang, Qingyang, et al. “A Trusted and Collaborative Framework for Deep Learning in IoT.” Computer Networks, vol. 193, Elsevier BV, July 2021, p. 108055. Crossref, https://doi.org/10.1016/j.comnet.2021.108055.
Wazid, Mohammad, et al. “LAM-CIoT: Lightweight Authentication Mechanism in Cloud-based IoT Environment.” Journal of Network and Computer Applications, vol. 150, Elsevier BV, Jan. 2020, p. 102496. Crossref, https://doi.org/10.1016/j.jnca.2019.102496.
Son, Seunghwan, et al. “A Secure, Lightweight, and Anonymous User Authentication Protocol for IoT Environments.” Sustainability, vol. 13, no. 16, MDPI AG, Aug. 2021, p. 9241. Crossref, https://doi.org/10.3390/su13169241.
Saqib, Manasha, et al. “A Lightweight Three Factor Authentication Framework for IoT Based Critical Applications.” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 9, Elsevier BV, Oct. 2022, pp. 6925–37. Crossref, https://doi.org/10.1016/j.jksuci.2021.07.023.
El Zouka, Hesham A., and Mustafa M. Hosni. “Secure IoT Communications for Smart Healthcare Monitoring System.” Internet of Things, vol. 13, Elsevier BV, Mar. 2021, p. 100036. Crossref, https://doi.org/10.1016/j.iot.2019.01.003.
Singh, Ashish, and Kakali Chatterjee. “Securing Smart Healthcare System With Edge Computing.” Computers & Security, vol. 108, Elsevier BV, Sept. 2021, p. 102353. Crossref, https://doi.org/10.1016/j.cose.2021.102353.
Al Sibahee, Mustafa A., et al. “Lightweight Secure Message Delivery for E2E S2S Communication in the IoT-Cloud System.” IEEE Access, vol. 8, Institute of Electrical and Electronics Engineers (IEEE), 2020, pp. 218331–47. Crossref, https://doi.org/10.1109/access.2020.3041809.
Yadav, Kusum, et al. “A Secure Data Transmission and Efficient Data Balancing Approach for 5G‐based IoT Data Using UUDIS‐ECC and LSRHS‐CNN Algorithms.” IET Communications, vol. 16, no. 5, Institution of Engineering and Technology (IET), Jan. 2022, pp. 571–83. Crossref, https://doi.org/10.1049/cmu2.12336.
Ostad-Sharif, Arezou, et al. “Three Party Secure Data Transmission in IoT Networks Through Design of a Lightweight Authenticated Key Agreement Scheme.” Future Generation Computer Systems, vol. 100, Elsevier BV, Nov. 2019, pp. 882–92. Crossref, https://doi.org/10.1016/j.future.2019.04.019.
Mondal, Sanjoy, et al. “Energy Efficient and Secure Healthcare Data Transmission in the Internet of Medical Things Network.” Microsystem Technologies, Springer Science and Business Media LLC, Nov. 2022. Crossref, https://doi.org/10.1007/s00542-022-05398-2.
Jan, Aiman, et al. “Secure Data Transmission in IoTs Based on CLoG Edge Detection.” Future Generation Computer Systems, vol. 121, Elsevier BV, Aug. 2021, pp. 59–73. Crossref, https://doi.org/10.1016/j.future.2021.03.005
Rani, S. Sheeba, et al. “Optimal Users Based Secure Data Transmission on the Internet of Healthcare Things (IoHT) With Lightweight Block Ciphers.” Multimedia Tools and Applications, vol. 79, no. 47–48, Springer Science and Business Media LLC, May 2019, pp. 35405–24. Crossref, https://doi.org/10.1007/s11042-019-07760-5.
Pampapathi, B. M., et al. “Data Distribution and Secure Data Transmission Using IANFIS and MECC in IoT.” Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 3, Springer Science and Business Media LLC, Jan. 2021, pp. 1471–84. Crossref, https://doi.org/10.1007/s12652-020-02792-4.
R. John Martin. “IoMT Supported COVID Care – Technologies and Challenges.” International Journal of Engineering and Management Research, vol. 12, no. 1, Vandana Publications, Feb. 2022, pp. 125–31. Crossref, https://doi.org/10.31033/ijemr.12.1.16.