D. Jung, Jiho Shin, Chaechang Lee, Kook-huei Kwon, Jung-Taek Seo
Recently 1, cyber attacks targeting Industrial Control Systems (ICS) have been increasing rapidly; accordingly, cyber security applications and security evaluations of ICS are becoming very important. Technical Assessment Methodology (TAM) is a method developed by the Electric Power Research Institute (EPRI) in the United States for assessing and applying security control methods for power plants. By applying TAM, limitations of cyber security application and security evaluation of existing nuclear facilities are able to address. In this study, a virtual test bed was composed for one of the safety systems of APR1400, and the TAM was applied and analyzed to derive two advantages and five features of TAM. Based on this, the rationale for using TAM for the application and assessment of security control methods in nuclear facilities was explained, including five considerations for the better application of TAM. Finally, we propose future work for applying TAM.
{"title":"A Study on the Application of Technical Assessment Methodology (TAM) for CyberSecurity in Nuclear Power Plant","authors":"D. Jung, Jiho Shin, Chaechang Lee, Kook-huei Kwon, Jung-Taek Seo","doi":"10.1145/3440943.3444741","DOIUrl":"https://doi.org/10.1145/3440943.3444741","url":null,"abstract":"Recently 1, cyber attacks targeting Industrial Control Systems (ICS) have been increasing rapidly; accordingly, cyber security applications and security evaluations of ICS are becoming very important. Technical Assessment Methodology (TAM) is a method developed by the Electric Power Research Institute (EPRI) in the United States for assessing and applying security control methods for power plants. By applying TAM, limitations of cyber security application and security evaluation of existing nuclear facilities are able to address. In this study, a virtual test bed was composed for one of the safety systems of APR1400, and the TAM was applied and analyzed to derive two advantages and five features of TAM. Based on this, the rationale for using TAM for the application and assessment of security control methods in nuclear facilities was explained, including five considerations for the better application of TAM. Finally, we propose future work for applying TAM.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131853995","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}
Different hyperparameter settings for a deep neural network (DNN) algorithm will come up with different prediction results. One of the most important things is thus in selecting a set of suitable hyperparameters for a DNN so as to increase its accuracy. This can be regarded as a hyperparameter optimization problem for DNN or DNN-based algorithms. Compared with manual, grid search, or random search for parameter settings, metaheuristic algorithms are able to find better hyperparameters for DNNs. To improve the accuracy of a prediction model based on DNN, an improved version of multi-swarm particle swarm optimization (MSPSO) is presented in this paper. Moreover, data provided by Taipei Rapid Transit Corporation will be used to evaluate the performance of the proposed algorithm in predicting the number of passengers for the Taipei metro station. The simulation results show that the proposed algorithm can be used to find better hyperparameters for DNN. This means that the proposed algorithm can provide a more accurate result than other machine learning algorithms, DNN, and PSO with DNN in terms of the prediction accuracy.
{"title":"An Effective Multi-Swarm Algorithm for Optimizing Hyperparameters of DNN","authors":"Zhi-Yan Fang, Zhe Xiao, Chun-Wei Tsai","doi":"10.1145/3440943.3444722","DOIUrl":"https://doi.org/10.1145/3440943.3444722","url":null,"abstract":"Different hyperparameter settings for a deep neural network (DNN) algorithm will come up with different prediction results. One of the most important things is thus in selecting a set of suitable hyperparameters for a DNN so as to increase its accuracy. This can be regarded as a hyperparameter optimization problem for DNN or DNN-based algorithms. Compared with manual, grid search, or random search for parameter settings, metaheuristic algorithms are able to find better hyperparameters for DNNs. To improve the accuracy of a prediction model based on DNN, an improved version of multi-swarm particle swarm optimization (MSPSO) is presented in this paper. Moreover, data provided by Taipei Rapid Transit Corporation will be used to evaluate the performance of the proposed algorithm in predicting the number of passengers for the Taipei metro station. The simulation results show that the proposed algorithm can be used to find better hyperparameters for DNN. This means that the proposed algorithm can provide a more accurate result than other machine learning algorithms, DNN, and PSO with DNN in terms of the prediction accuracy.","PeriodicalId":310247,"journal":{"name":"Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125156166","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}
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