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Iraqi Journal for Computer Science and Mathematics最新文献

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Optimization Simulation programming for NoC NoC优化仿真编程
Iraqi Journal for Computer Science and Mathematics Pub Date : 2020-01-30 DOI: 10.52866/ijcsm.2019.01.01.003
D. Yaseen
{"title":"Optimization Simulation programming for NoC","authors":"D. Yaseen","doi":"10.52866/ijcsm.2019.01.01.003","DOIUrl":"https://doi.org/10.52866/ijcsm.2019.01.01.003","url":null,"abstract":"The article presents the concept of networks-on-chip (NoCs) as a promising alternative to communication subsystem for multiprocessor systems with bus architecture. The networks simulator developed as important software tool to estimate NoC performance parameters. The results of approbation of the developed simulator are reliance of the number of hops on the NoC dimension for mesh and torus topologies, as well as the dependences of communication links workload on the frequency, with which IP blocks generate messages. Its possibilities are considered and the accepted results are given.","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121316503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rao-SVM Machine Learning Algorithm for Intrusion DetectionSystem 入侵检测系统的Rao-SVM机器学习算法
Iraqi Journal for Computer Science and Mathematics Pub Date : 2020-01-30 DOI: 10.52866/ijcsm.2019.01.01.004
Shamis N. Abd, Mohammad Alsajri, Hind Ra'ad Ibraheem
{"title":"Rao-SVM Machine Learning Algorithm for Intrusion Detection\u0000System","authors":"Shamis N. Abd, Mohammad Alsajri, Hind Ra'ad Ibraheem","doi":"10.52866/ijcsm.2019.01.01.004","DOIUrl":"https://doi.org/10.52866/ijcsm.2019.01.01.004","url":null,"abstract":"Most of the intrusion detection systems are developed based on optimization algorithms as a result\u0000of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the\u0000performance of the human-based methods in terms of their training time and classification accuracy. This article\u0000presents the development of an improved intrusion detection method for binary classification. In the proposed IDS,\u0000Rao Optimization Algorithm, Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic\u0000Regression (LR) (feature selection and weighting) were combined with NTLBO algorithm with supervised ML\u0000techniques (for feature subset selection (FSS). Being that feature subset selection is considered a multi-objective\u0000optimization problem, this study proposed the Rao-SVM as an FSS mechanism; its algorithm-specific and parameterless concept was also explored. The prominent intrusion machine-learning dataset, UNSW-NB15, was used for the\u0000experiments and the results showed that Rao-SVM reached 92.5% accuracy on the UNSW-NB15 dataset","PeriodicalId":158721,"journal":{"name":"Iraqi Journal for Computer Science and Mathematics","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134078169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
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