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Showing 3 results for Support Vector Machine
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Volume 3, Issue 1 (9-2014)
Abstract
In recent years, electronic payments has grown rapidly among internet activities so nowadays has attracted many customers due to its speed, efficiency, cost reduction and ease of access. Credit cards can be considered as one of the most widely used tools for electronic payments transactions . Purpose of this research is the identification and extraction feature of fraudulent transaction followed by correct classification of them into two categories of legal and fraud, using support vector machine algorithm and cross-validation. The results of ths method to show improvement in fraud detection so that false negative reduction has 77%, cost 88% and detection rate increased by 11%.
Narges Salehpour, Mohammad Nazari Farokhi, Ebrahim Nazari Farokhi,
Volume 3, Issue 2 (3-2015)
Abstract
Abstract One of the most important issues in securing computer networks is an Intrusion Detection System. Intrusion detection systems are searching for malicious behavior, deviation normal patterns and attacks on computer networks are discovered. This system recognizes the type of traffic allowed for unauthorized traffic. Since the today's data mining techniques to intrusion detection in computer networks are used. In this research is provided, a method for designing an intrusion detection system based on machine learning. One of the features of neural networks and machine learning systems, training is based on the training data. In this research is used for detecting the intrusion of machine learning to learn the features of the theory of Rough property that has a higher correlation coefficient is used. To train and evaluate has used the proposed approach the KDD CUP 99 dataset. This study, the accuracy of our method compares with feature-based learning algorithm, neural network self and decision tree. The simulation results show that the proposed system has high accuracy and speed of detection based on rough theory is right
Seyed Omid Azarkasb, Seyed Hossein Khasteh, Saeed Sedighian Kashi,
Volume 11, Issue 1 (9-2022)
Abstract
Fog is a cloud that closes to the ground. The components of fog and cloud complement each other. These components provide mutually beneficial interdependent services for communication, processing, control, and storage across the network. Attacking the fog nodes are as important as attacking the cloud. Since the fog node has more limited resources, it is more targeted by intruders. In addition, fog nodes are more attractive to attackers because they have less computing power and are located closer to the attacker than the cloud. But the key point is that access to limited resources makes it easier to save the fog node because the fog does not have the complexities of the cloud, and it is easy to run an intrusion detection system on it. In this article, focusing on the resource limitation in the fog node, we will invent a method to save the fog node. In the proposed method, the support vector machines (SVMs) technique is used. Among the advantages of using the support vector machine, we can mention not being trapped in local optima, solving the over fitting problem, and ease of working with high-dimensional data. Based on the research, support vector machine is the most widely used machine learning method for Internet of Things security articles in the literature. In this article, in order to conduct tests, according to published global statistics, the most important category of web attacks, i.e. SQL injection attacks, is considered. The average detection accuracy is obtained and the results of the evaluations indicate the acceptable efficiency of the proposed method.
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