Open Access
Research Article
Recognize corrupted data packeted while transferring data through ensemble machine learning techniques
*Satyajeet SharmaCorresponding authorsharma.satyajeet24@gmail.comDepartment of Computer Science and Engineering JECRC UniversityDepartment of Computer Science & Engineering JECRC UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Bhavna Sharmabhavna.sharma@jecrcu.edu.inDepartment of Computer Science and Engineering JECRC UniversityDepartment of Computer Science and Engineering JECRC University Jaipur Rajasthan IndiaJaipur, Rajasthan, 303905, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 03 Feb 2023
- Published Online:
- 11 Oct 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1420
- Pages:
- 1459–1469
Abstract
In today’s world, every technology is moving towards cloud storage which makes file transfer protocols a cornerstone for any platform to run smoothly. Therefore, identifying damaged files is a crucial responsibility in the area of data management and integrity. In this study, we suggest an AdaBoost-based machine learning technique for identifying damaged files. AdaBoost is an ensemble method that combines many weak classifiers into one powerful classifier. In our method, we train weak classifiers called decision stumps using a dataset that includes both damaged and healthy files. The final prediction was decided by a weighted majority vote of all the weak classifiers. We evaluated our method on a dataset generated by collecting metadata information of files and passed it to the algorithms. We used the AdaBoost approach as a base algorithm for comparison along with more established techniques like Naive Bayes, Logistic Regression, and Linear Discriminant Analysis. The results show that the AdaBoost algorithm is effective in detecting corrupted files, and it performs better than other traditional methods. Additionally, our method is computationally efficient and can be easily integrated into existing data management systems. It is expected to have a positive impact on data integrity and management in various fields such as digital forensics, cloud computing, and storage systems.
Keywords
Subject Classifications
Primary 68T07Secondary 68M25
Acknowledgements
DG 4977
References
[1] Anshu Parashar and Kuljot Singh Saggu “Machine learning based framework for network intrusion detection system using stacking ensemble technique” Indian Journal of Engineering & Materials Sciences, Vol. 29, August 2022, pp. 509-518, (2023).[2] Tehseen Mazhar and Hafiz Muhammad Irfan “Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods” Future Internet 2023, 15, 83. (2023).[3] Nikos Mitro and Katerina Argyri “AI-Enabled SmartWristband Providing Real-Time Vital Signs and Stress Monitoring” Sensors 2023, 23, 2821. (2023).[4] Sharma, Gajanand”Data management framework for IoT edge-cloud architecture for resource-constrained IoT application.” Journal of Discrete Mathematical Sciences and Cryptography 25.4 (2022): 1093-1103.(2022).[5] Sharma, Gajanand “Self-healing topology for DDoS attack identification & discovery protocol in software-defined networks.” Journal of Discrete Mathematical Sciences and Cryptography 24.8 (2021).[6] Sweta Bhattacharya and Rajeswari C “A Hybrid Approach To Evaluate Stock Returns Using Data Mining Techniques” International Journal Of Scientific and Technology Research Volume 9, Issue 01, Issn 2277-8616 , January 2020. [7] A. Ann Romalt and R. Mathusoothana S. Kumar “An Analysis on Feature Selection Methods, Clustering And Classification Used In Heart Disease Prediction – A Machine Learning Approach” Journal of Critical (2020).[8] Naveen and Kishore G V.Rajesh “Forecast of Diabetes Using Machine Learning Classification Algorithms” International Journal of Scientific and Technology Research,ISSN 2277-8616, Volume 9, Issue 01, January 2020.[9] Zhijian Liu and DiWu et.al (2019) “Accuracy investigations andmodel examination ofmachine learning adoptedin building energyconsumption forecast” Energy Exploration and Exploitation, Vol. 37(4) (2019). [10] Bindhia K. furthermore, Francis “Anticipating Academic Performance of Students Using a Hybrid Data Mining Approach” Journal of Medical Systems (2019). [11] Sharma, Gajanand, et. al. (2019) “Video tempering detection assessment in full reference mode using difference matrices.” Journal of Discrete Mathematical Sciences and Cryptography 22.4 (2019): 645-659.[12] Dr. Amit Sharma “An Approach of Data Mining on Cloud Using Secure Monitoring Process” Journal of The Gujarat Research Society ISSN: 0374-8588 volume 21 Issue 16, December 2019. [13] Dr. Amit Sharma “An Approach of Data Protection over Cloud Storage through Effective IDS Mining And Monitoring” Journal of The Gujarat Research Society ISSN: 0374-8588 volume 21 Issue 16, December 2019.[14] Sharma, Gajanand”Analysis & Design of Visual Cryptography Using Moving Image.” International Journal of Information and communication Technology Research (ISSN: 2223–4985) Volume.3. (2018).
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