Abstract
This paper aims to present a hypothetic theory of intelligent security system. In society the threat of cyber-attacks is getting louder and the use of computers, criminal activity has also changed from physical to cybernetic intrusion. There had been many cyber security solutions used to counteract these attacks, however we highlight the importance of self-protected systems in defense and in a correct analysis of cyber attacks. The internet is vulnerable to cyber-attacks as well as the information found in data systems and through a form of recognition and extraction of relevant information, we can represent data as shared data and integrated to intelligent system. What was used us a static firewall is now intended to be dynamic and self-critical. By techniques of data analysis, statistics, machine learning, data mining, the cybersecurity and privacy challenges are within our reach. This paper examines data mining techniques in order to predict pathways of Internet security and which considerations are involved in the theoretical solutions presented for the privacy systems such as the e-Learning environments.
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Notes
- 1.
A decision tree is a tree data structure consisting of decision nodes and leaves. A leaf species is a class value. A decision node species a test over one of the attributes, which is called the attribute selected at the node. For each possible outcome of the test, a child node is present. In particular, the test on a discrete attribute A has h possible outcomes A = d1, …, A = dh, where d1;:::dh are the known values for attribute A. The test on a continuous attribute has two possible outcomes, A t and A > t, where t is a value determined at the node, and called the threshold [8].
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Manuel, J., Cordeiro, R., Silva, C. (2018). Between Data Mining and Predictive Analytics Techniques to Cybersecurity Protection on eLearning Environments. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds) Applied Computational Intelligence and Mathematical Methods. CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol 662. Springer, Cham. https://doi.org/10.1007/978-3-319-67621-0_17
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DOI: https://doi.org/10.1007/978-3-319-67621-0_17
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