Abstract
Security of information systems and their connecting networks has become a primary focus given that pervasive cyber-attacks against information systems are geometrically increasing. Intrusion Detection and Prevention Systems (IDPS) effectively secure the data, storage devices and the systems holding them. We will build system consist of five steps: (a) description the orders that required to archives the event by five fuzzy concepts as input and three fuzzy concepts as output, then save it in temporal bank of orders, (b) Pre-processing that order by convert from the description to numerical values and compute the Membership function for that values. (c) applied the association data mining techniques on these database after compute the correlation among their features, this lead to generation thirty two rules but not all this rules is salsify the confidence measures (i.e., we take only the rules that satisfy the purity 100%) (d) Building the Confusion matrix for all the samples using in training processing (e) Testing the Pragmatic Miner to Risk Analysis (PMRA) model and verification from the accuracy of their results by press new samples to model not used in training stage then compute the values of error and accuracy measures, in addition of correct. The existing systems employing firewalls and encryptions for data protection are getting outdated. IDPS provides a much improved detection system that can prevent the intrusions to attack the system. However, as effective as it is in preventing intrusions, which can disrupt the retrieval of desired information as the system sometimes perceives it as an attack. The base aim of this work is to determine a way to risk analysis of IDPS to an acceptable level while detecting the intrusions and maintaining effective security of a system. Experimental results clearly show the superficiality of the proposed model against the conventional IDPS system.
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Al-Janabi, S. (2017). Pragmatic Miner to Risk Analysis for Intrusion Detection (PMRA-ID). In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_23
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DOI: https://doi.org/10.1007/978-981-10-7242-0_23
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