Dynamic Adjustment of the TLS Protocol Based on Naive Bayes
DOI:
https://doi.org/10.61173/qqw1q225Keywords:
TLS protocol, naive bayes, self-adjustment, cipher suite parametersAbstract
The adaptive adjustment of the TLS (Transport Layer Security) protocol aims to address various security threats and performance requirements in the current network environment. It is crucial for the reliability of online transactions and data transmissions. This research will adopt a comprehensive research approach, integrating network traffic analysis, naïve bayes machine learning algorithms and simulation experiments. It is expected to develop a mechanism that can dynamically adjust the cipher suite parameters of the TLS protocol according to the real-time network situation, so as to strengthen the security of data transmission and improve the efficiency of network communications.
References
[1] Asadzadeh Kaljahi, Maryam, Ali Payandeh, and Mohammad Bagher Ghaznavi-Ghoushchi. “TSSL: improving SSL/TLS protocol by trust model.” Security and Communication Networks 8.9 (2015): 1659-1671.
[2] Zhou, Jiuxing, et al. “Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive Survey.” Electronics 13.20 (2024): 4000.
[3] Diemert, Denis, and Tibor Jager. “On the tight security of TLS 1.3: Theoretically sound cryptographic parameters for realworld deployments.” Journal of Cryptology 34.3 (2021): 30.
[4] Fletcher-Lloyd, Nan, et al. “A Markov Chain Model for Dean&Francis Kaikun Li Identifying Changes in Daily Activity Patterns of People Living with Dementia.” IEEE Internet of Things Journal (2023).
[5] Matsuda, Koji, et al. “Benchmark for Personalized Federated Learning.” IEEE Open Journal of the Computer Society (2023).
[6] Li, Xinyi, et al. “: Towards Collaborative and Cross-Domain Wi-Fi Sensing: A Case Study for Human Activity Recognition.” IEEE Transactions on Mobile Computing 23.2 (2023): 1674- 1688.
[7] Pajila, PJ Beslin, et al. “A comprehensive survey on naive bayes algorithm: Advantages, limitations and applications.” 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE, 2023.
[8] Kohavi, Ron. “Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid.” Kdd. Vol. 96. 1996.
[9] Lachiche, Nicolas, and Peter A. Flach. “Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves.” Proceedings of the 20th international conference on machine learning (ICML-03). 2023.
[10] Ontivero-Ortega, Marlis, et al. “Fast Gaussian Naïve Bayes for searchlight classification analysis.” Neuroimage 163 (2017): 471-479.
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