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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Using Bootstrap Aggregating Equipped with SVM and T-SNE Classifiers for Cryptography
verfasst von : Neeraja Narayanswamy, Siddhaling Urolagin
Erschienen in: Advances in Machine Learning and Computational Intelligence
Verlag: Springer Singapore
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Abstract
With the application of machine learning in almost every field comes the need for systems secure enough to protect enormous amounts of data from leaking. There has also been a rise in new methods of attacks that use machine learning to attack the system. Security sometimes comes at the cost of accuracy and historically, there always has been a trade-off between artificial intelligence and data security. Thus, a system has to be created that can not only use artificial intelligence and machine learning in new and improved ways but also protect the data it works on. This paper attempts to reverse engineer one such attack in order to create software that can double as an intrusion detection system. The approach that is taken is a combination of two separate methods combined with an ensemble classifier, rather than use each approach separately thereby giving the system enough time to recognize the attack.