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Showing 3 results for Nazari
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
Mr Mohamad Jari, Miss Fariba Nazari,
Volume 8, Issue 2 (2-2020)
Abstract
The purpose of this study is to identify and prioritize the effective technical and technical stress factors of information security by IT experts identified in Aghajari oil and gas Exploitation Company. The statistical population of the study consisted of 100 ICT managers and experts in Aghajari Oil and Gas Co. which directly related to the security of information in the company, 80 of them were selected as samples. In this research, the first questionnaire was designed with the aim of identifying the factors and half-openness. The second questionnaire was designed with the aim of screening the identified factors as closed and based on Likert's five-choice spectrum. Finally, a third questionnaire was designed with the aim of determining the weights and rank of each one of the factors and in a pair comparison. The necessary analysis was carried out through the software SSS, Excel, ExpressChevis and MATLAB. The results of the research led to the identification of two main factors (occupational stressors and technical stressful factors influencing information security by IT experts in Aghajari oil and gas Exploitation Company) and 14 sub factors and then their rank were determined.
Ali Nazari, Babak Sadeghiyan,
Volume 11, Issue 2 (3-2023)
Abstract
Under the coverage of legitimate commerce, criminals money-launder their illicit incomes through the payment gateways provided by Payment Service Providers (PSP). In order to do money-laundering forensics in transactions of PSP companies, a new method was proposed by Hojati et al which is done through detecting deviations from class behavior based on peer group analysis (PGA) method. Our experiments showed that using the proposed method for money laundering detection leads to a false positive rate of about 13%. In this paper, we improved the proposed method and reduced the false positive rate to less than 1%. To achieve this, we analyzed the amount of financial transactions of sellers along with the number of visitors to their websites in PGA. Based on the number of visitors, we estimated the volume of transactions for each seller. If the volume of sales was much higher than expected, we considered it abnormal. We achieved a higher detection accuracy by using a restricted Boltzmann machine to separate out-of-class transactions. We also reduced rate of false negative alarms by the help of CBR method. Our proposed system detects money laundering online using a four-week sliding window. The experimental results confirmed the detection accuracy of 99% for our proposed system.
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