An Integrated Framework for Data Security Using Advanced Machine Learning Classification and Best Practices
Abstract
In the current interconnected digital environment, data security has become a paramount concern, as cyberattacks and data breaches are increasing in frequency and complexity. Both organizations and people face challenges in safeguarding sensitive information, requiring resilient security systems that can adjust to various threats. This paper presents a comprehensive approach to data security, focusing on integrating advanced classification techniques and best practices to secure data proactively. This study uses and analyzes advanced classification algorithms like decision trees, support vector machines (SVM), and neural networks to determine how well they work to find, sort, and keep sensitive data safe across various security needs. The results indicate substantial improvements in classification accuracy, with the optimal model attaining an accuracy rate of 98.83%. The other models, including decision tress and SVM provide 89% and 92% accuracy, respectively. This highlights the dependability and resilience of these methods in detecting possible security concerns across various datasets. In addition to these classification results, we comprehensively analyze industry best practices in data security, encompassing encryption technologies, dynamic access control, and continuous monitoring to mitigate vulnerabilities and improve threat detection. Integrating sophisticated classification methodologies with these optimal practices provides a comprehensive security framework that enhances data protection and mitigates risk. This study offers significant insights for practitioners and organizations aiming to implement a more systematic and efficient data security approach, enhancing academic and practical discussions in this domain. This work seeks to strengthen the effectiveness of data security practices by introducing a novel method that integrates high-accuracy categorization with proactive security protocols.
Full Text:
PDFReferences
C. Thapa and S. Camtepe, "Precision health data: Requirements, challenges and existing techniques for data security and privacy," Computers in Biology and Medicine, vol. 129, pp. 104130, 2021.
Ö. Aslan, S. S. Aktuğ, M. Ozkan-Okay, A. A. Yilmaz, and E. Akin, "A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions," Electronics, vol. 12, no. 6, pp. 1333, 2023.
D. Dasgupta, Z. Akhtar, and S. Sen, "Machine learning in cybersecurity: a comprehensive survey," The Journal of Defense Modeling and Simulation, vol. 19, no. 1, pp. 57--106, 2022.
I. H. Sarker, "Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions," SN Computer Science, vol. 2, no. 6, pp. 420, 2021.
W. Ahmad, A. Rasool, A. R. Javed, T. Baker, and Z. Jalil, "Cyber security in IoT-based cloud computing: A comprehensive survey," Electronics, vol. 11, no. 1, pp. 16, 2021.
W. S. Admass, Y. Y. Munaye, and A. A. Diro, "Cyber security: State of the art, challenges and future directions," Cyber Security and Applications, vol. 2, pp. 100031, 2024.
Z. Zhang, H. Al Hamadi, E. Damiani, C. Y. Yeun, and F. Taher, "Explainable artificial intelligence applications in cyber security: State-of-the-art in research," IEEE Access, vol. 10, pp. 93104--93139, 2022.
A. B. Ige, E. Kupa, and O. Ilori, "Analyzing defense strategies against cyber risks in the energy sector: Enhancing the security of renewable energy sources," International Journal of Science and Research Archive, vol. 12, no. 1, pp. 2978--2995, 2024.
A. B. Ige, E. Kupa, and O. Ilori, "Best practices in cybersecurity for green building management systems: Protecting sustainable infrastructure from cyber threats," International Journal of Science and Research Archive, vol. 12, no. 1, pp. 2960--2977, 2024.
R. Kaur, D. Gabrijelčič, and T. Klobučar, "Artificial intelligence for cybersecurity: Literature review and future research directions," Information Fusion, vol. 97, pp. 101804, 2023.
Z. Yang, X. Liu, T. Li, D. Wu, J. Wang, Y. Zhao, and H. Han, "A systematic literature review of methods and datasets for anomaly-based network intrusion detection," Computers & Security, vol. 116, pp. 102675, 2022.
A. Fatani, A. Dahou, M. A. Al-Qaness, S. Lu, and M. A. Elaziz, "Advanced feature extraction and selection approach using deep learning and Aquila optimizer for IoT intrusion detection system," Sensors, vol. 22, no. 1, pp. 140, 2021.
X. Sun, F. R. Yu, and P. Zhang, "A survey on cyber-security of connected and autonomous vehicles (CAVs)," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 6240--6259, 2021.
S. Nifakos, K. Chandramouli, C. K. Nikolaou, P. Papachristou, S. Koch, E. Panaousis, and S. Bonacina, "Influence of human factors on cyber security within healthcare organisations: A systematic review," Sensors, vol. 21, no. 15, pp. 5119, 2021.
A. U. R. Butt, T. Mahmood, T. Saba, S. O. Bahaj, F. S. Alamri, M. W. Iqbal, and A. R. Khan, "An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment," IEEE Access, 2023.
M. I. Khan, A. Imran, A. H. Butt, A. U. R. Butt, et al., "Activity Detection of Elderly People Using Smartphone Accelerometer and Machine Learning Methods," International Journal of Innovations in Science & Technology, vol. 3, no. 4, pp. 186--197, 2021.
M. Ghiasi, T. Niknam, Z. Wang, M. Mehrandezh, M. Dehghani, and N. Ghadimi, "A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future," Electric Power Systems Research, vol. 215, pp. 108975, 2023.
D. Chen, P. Wawrzynski, and Z. Lv, "Cyber security in smart cities: a review of deep learning-based applications and case studies," Sustainable Cities and Society, vol. 66, pp. 102655, 2021.
M. A. Ferrag, O. Friha, D. Hamouda, L. Maglaras, and H. Janicke, "Edge-IIoTset: A new comprehensive realistic cyber security dataset of IoT and IIoT applications for centralized and federated learning," IEEE Access, vol. 10, pp. 40281--40306, 2022.
Z. Zhang, H. Ning, F. Shi, F. Farha, Y. Xu, J. Xu, F. Zhang, and K.-K. R. Choo, "Artificial intelligence in cyber security: research advances, challenges, and opportunities," Artificial Intelligence Review, pp. 1--25, 2022.
A. Khraisat and A. Alazab, "A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges," Cybersecurity, vol. 4, pp. 1--27, 2021.
T. O. Oladoyinbo, O. O. Adebiyi, J. C. Ugonnia, O. O. Olaniyi, and O. J. Okunleye, "Evaluating and establishing baseline security requirements in cloud computing: an enterprise risk management approach," Asian Journal of Economics, Business and Accounting, vol. 23, no. 21, pp. 222--231, 2023.
R. Vallabhaneni, S. E. V. S. Pillai, S. A. Vaddadi, S. R. Addula, and B. Ananthan, "Secured web application based on CapsuleNet and OWASP in the cloud," Indonesian Journal of Electrical Engineering and Computer Science, vol. 35, no. 3, pp. 1924--1932, 2024.
M. K. Hasan, A. K. M. A. Habib, Z. Shukur, F. Ibrahim, S. Islam, and M. A. Razzaque, "Review on cyber-physical and cyber-security system in smart grid: Standards, protocols, constraints, and recommendations," Journal of Network and Computer Applications, vol. 209, pp. 103540, 2023.
I. H. Sarker, M. H. Furhad, and R. Nowrozy, "AI-driven cybersecurity: an overview, security intelligence modeling and research directions," SN Computer Science, vol. 2, no. 3, pp. 173, 2021.
M. A. Ferrag, O. Friha, L. Maglaras, H. Janicke, and L. Shu, "Federated deep learning for cyber security in the internet of things: Concepts, applications, and experimental analysis," IEEE Access, vol. 9, pp. 138509--138542, 2021.
DOI: https://doi.org/10.31449/inf.v49i12.7838
This work is licensed under a Creative Commons Attribution 3.0 License.