Distributed Computing and Artificial Intelligence, 13th by Sigeru Omatu, Ali Semalat, Grzegorz Bocewicz, Pawel Sitek,

By Sigeru Omatu, Ali Semalat, Grzegorz Bocewicz, Pawel Sitek, Izabela E. Nielsen, Julián A. García García, Javier Bajo

The thirteenth overseas Symposium on disbursed Computing and synthetic Intelligence 2016 (DCAI 2016) is a discussion board to give functions of leading edge options for learning and fixing complicated difficulties. The trade of principles among scientists and technicians from either the tutorial and business quarter is key to facilitate the advance of platforms which may meet the ever-increasing calls for of today’s society. the current version brings jointly previous adventure, present paintings and promising destiny developments linked to dispensed computing, synthetic intelligence and their program with a purpose to offer effective suggestions to genuine difficulties.

This symposium is geared up through the college of Sevilla (Spain), Osaka Institute of know-how (Japan), and the Universiti Teknologi Malaysia (Malaysia)

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Below are presented some proposed techniques in intrusion detection systems (IDS): Distributed Denial of Service (DDoS) Attacks Detection 35 Rules: Responsible for analyzing the traffic that goes through the IDS, classifying the frame as normal or as intrusion. This technique uses a database of knowledge where a set of rules is applied to compare traffic patterns with the parameterized rules in the database [2]. As proposed by [4] on a system based intrusion detection agents with a rules engine based on XML, it can be that the argued model was presented as a distributed intrusion detection system, which consists of three intelligent agents that have specific functions and exchange information via XML sure how SSL and a pointto-point and IAP.

The Section 3 shows the proposed model of the development and application of machine learning prototype. In section 4 some results are evident and in the end, finally in Section 5 the conclusions are presented. 2 Context and Recent Literature Since 1998 and 1999, DARPA has collected and distributed the first standard dataset for evaluation of intrusion detection systems for computer networks. The first formal, repeatable and statistically significant evaluations of intrusion detection systems are coordinated.

The application of techniques with a SVM supervised model has large advantages over rule-based techniques, since the generation of the model is based on a statistical model that changes its behavior according to the input parameters defined and based on a training rule that requires human interaction; in the prototype evaluation it was found that the correct classification rate of normal or abnormal requests in the training phase is directly related to standardization and proper selection of the input parameters, allowing the output variables are generated with minimum percentage of misclassification, generating confidence in the generated model and the detection of these behaviors.

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