Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00037
H. Lei, M. Quweider, Liyu Zhang, Fitratullah Khan
Surveys are commonly used as an important data collection tool for empirical research in many applications such as social sciences, marketing and pedagogy. Survey data is becoming one of the major data sources in the era of big data. Conventional statistic tools are utilized to perform survey data analysis. Methods in data mining can extend the capabilities of statistics to explore and discover possible nuggets in massive data. While data mining on general databases has been intensive studied, very few has been done on survey data. Considering the specialities of survey data, this paper describes strategies in mining survey data using computational methods. A novel method for data preparation and dependent pattern mining is presented. Experiments on a real survey dataset were conducted to evaluate the strategies. Results on finding meaningful patterns are reported and discussed.
{"title":"Mining Survey Data","authors":"H. Lei, M. Quweider, Liyu Zhang, Fitratullah Khan","doi":"10.1109/ICDIS.2019.00037","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00037","url":null,"abstract":"Surveys are commonly used as an important data collection tool for empirical research in many applications such as social sciences, marketing and pedagogy. Survey data is becoming one of the major data sources in the era of big data. Conventional statistic tools are utilized to perform survey data analysis. Methods in data mining can extend the capabilities of statistics to explore and discover possible nuggets in massive data. While data mining on general databases has been intensive studied, very few has been done on survey data. Considering the specialities of survey data, this paper describes strategies in mining survey data using computational methods. A novel method for data preparation and dependent pattern mining is presented. Experiments on a real survey dataset were conducted to evaluate the strategies. Results on finding meaningful patterns are reported and discussed.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130162855","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00036
Yongzhong He, Liping Hu, Ruimei Gao
Tor (The second generation Onion Router) is the most popular anonymous communication network. In order to protect Tor user from traffic analysis attack, many obfuscation techniques are adopted and Obfs4 is one of the states of art techniques used in Tor. It is very hard to detect the Tor traffic camouflaged under Obfs4, especially in the real world when there is a large volume of various traffic, because of random padding and randomization of time sequence. In this paper, we propose a novel scheme for Obfs4 traffic detection based on two-level filtering. We sequentially utilize coarse-grained fast filtering and fine-grained accurate identification to achieve high-precision, real-time recognition of Obfs4 traffic. In the coarse-grained filtering phase, we use the randomness detection algorithm to detect the randomness of the handshake packet payload in the communication and use the timing sequence characteristics of the packet in the handshake process to remove other interference traffic. In the fine-grained identification phase, we analyze its statistical feature on a large number of Obfs4 traffic and use the classification algorithms to identify the Obfs4 traffic. We train and test with different classifiers. The experiments show that the accuracy for identifying Obfs4 is above 99% when using the SVM (Support Vector Machine) algorithm, which indicates that Obfs4 cannot effectively counteract traffic analysis attacks in practical applications.
{"title":"Detection of Tor Traffic Hiding Under Obfs4 Protocol Based on Two-Level Filtering","authors":"Yongzhong He, Liping Hu, Ruimei Gao","doi":"10.1109/ICDIS.2019.00036","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00036","url":null,"abstract":"Tor (The second generation Onion Router) is the most popular anonymous communication network. In order to protect Tor user from traffic analysis attack, many obfuscation techniques are adopted and Obfs4 is one of the states of art techniques used in Tor. It is very hard to detect the Tor traffic camouflaged under Obfs4, especially in the real world when there is a large volume of various traffic, because of random padding and randomization of time sequence. In this paper, we propose a novel scheme for Obfs4 traffic detection based on two-level filtering. We sequentially utilize coarse-grained fast filtering and fine-grained accurate identification to achieve high-precision, real-time recognition of Obfs4 traffic. In the coarse-grained filtering phase, we use the randomness detection algorithm to detect the randomness of the handshake packet payload in the communication and use the timing sequence characteristics of the packet in the handshake process to remove other interference traffic. In the fine-grained identification phase, we analyze its statistical feature on a large number of Obfs4 traffic and use the classification algorithms to identify the Obfs4 traffic. We train and test with different classifiers. The experiments show that the accuracy for identifying Obfs4 is above 99% when using the SVM (Support Vector Machine) algorithm, which indicates that Obfs4 cannot effectively counteract traffic analysis attacks in practical applications.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129055263","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00011
Zachary Hill, J. Hale, M. Papa, P. Hawrylak
Increasing integration of physical components into digital systems has introduced new attack vectors that traditional intrusion detection systems (IDSs) are incapable of protecting with previously developed methods. Physical components can be targeted to change the behavior of the system without modifying the digital network, leading to unsafe or undesirable system states without causing unusual network activity. Anomaly-based detection methods can be adapted to monitor the system's physical behavior to mitigate these attacks. This paper presents such a method utilizing the Bro IDS with a simulation model of the physical system. The state of the model is compared to the state information of the system being transmitted on the network, allowing attacks to be detected by observing inconsistencies between the model and the system.
{"title":"Using Bro with a Simulation Model to Detect Cyber-Physical Attacks in a Nuclear Reactor","authors":"Zachary Hill, J. Hale, M. Papa, P. Hawrylak","doi":"10.1109/ICDIS.2019.00011","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00011","url":null,"abstract":"Increasing integration of physical components into digital systems has introduced new attack vectors that traditional intrusion detection systems (IDSs) are incapable of protecting with previously developed methods. Physical components can be targeted to change the behavior of the system without modifying the digital network, leading to unsafe or undesirable system states without causing unusual network activity. Anomaly-based detection methods can be adapted to monitor the system's physical behavior to mitigate these attacks. This paper presents such a method utilizing the Bro IDS with a simulation model of the physical system. The state of the model is compared to the state information of the system being transmitted on the network, allowing attacks to be detected by observing inconsistencies between the model and the system.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117304976","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}
Pub Date : 2019-06-01DOI: 10.1109/icdis.2019.00003
{"title":"[Copyright notice]","authors":"","doi":"10.1109/icdis.2019.00003","DOIUrl":"https://doi.org/10.1109/icdis.2019.00003","url":null,"abstract":"","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124931076","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00043
J. Bassey, Xiangfang Li, Lijun Qian
Complex numbers are used to represent data in many practical applications such as in telecommunications, image processing, and speech recognition. In this work, we examine the efficiency of complex-valued neural networks and compare that with their real-valued counterpart. Specifically, we examine the performance of neural network with Multi Layer Multi-Valued Neuron (MLMVN) for classification on several benchmark datasets such as Iris and MNIST datasets. It is shown that in applications where complex numbers occur naturally, complex-valued neural networks such as MLMVN network could offer advantages such as more efficient embedding and processing of information over their real-valued counterparts. It is also observed that complex-valued neural networks have a tendency of overfitting especially in applications involving large datasets. Potential solution to the overfitting problem has been discussed.
{"title":"An Experimental Study of Multi-Layer Multi-Valued Neural Network","authors":"J. Bassey, Xiangfang Li, Lijun Qian","doi":"10.1109/ICDIS.2019.00043","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00043","url":null,"abstract":"Complex numbers are used to represent data in many practical applications such as in telecommunications, image processing, and speech recognition. In this work, we examine the efficiency of complex-valued neural networks and compare that with their real-valued counterpart. Specifically, we examine the performance of neural network with Multi Layer Multi-Valued Neuron (MLMVN) for classification on several benchmark datasets such as Iris and MNIST datasets. It is shown that in applications where complex numbers occur naturally, complex-valued neural networks such as MLMVN network could offer advantages such as more efficient embedding and processing of information over their real-valued counterparts. It is also observed that complex-valued neural networks have a tendency of overfitting especially in applications involving large datasets. Potential solution to the overfitting problem has been discussed.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117040197","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00035
Pinchao Liu, Adnan Maruf, F. Yusuf, Labiba Jahan, Hailu Xu, Boyuan Guan, Liting Hu, S. S. Iyengar
With the advancement of ever-growing online services, distributed Big Data storage i.e. Hadoop, Dryad gained much more attention than ever and the fundamental requirements like fault tolerance and data availability become the concern for these platforms. Data replication policies in Big Data applications are shifting towards dynamic approaches based on the popularity of files. Formulation of dynamic replication factor paved the way of solving the issues generated by existing data contention in hotspots and ensuring timely data availability. But from the empirical observations, it can be deduced that popularity of files is temporal rather than perpetual in nature and, after a certain period, content's popularity ceases most of the time which introduces the I/O bottleneck of updating replication in the disk. To handle such temporal skewed popularity of contents, we propose a dynamic data replication toolset using the power of in-memory processing by integrating MemCached server into Hadoop for getting improved performance. We compare the proposed algorithm with the traditional infrastructure and vanilla memory algorithm, as the evidence from the experimental results, the proposed design performs better i.e throughput and execution period.
{"title":"Towards Adaptive Replication for Hot/Cold Blocks in HDFS using MemCached","authors":"Pinchao Liu, Adnan Maruf, F. Yusuf, Labiba Jahan, Hailu Xu, Boyuan Guan, Liting Hu, S. S. Iyengar","doi":"10.1109/ICDIS.2019.00035","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00035","url":null,"abstract":"With the advancement of ever-growing online services, distributed Big Data storage i.e. Hadoop, Dryad gained much more attention than ever and the fundamental requirements like fault tolerance and data availability become the concern for these platforms. Data replication policies in Big Data applications are shifting towards dynamic approaches based on the popularity of files. Formulation of dynamic replication factor paved the way of solving the issues generated by existing data contention in hotspots and ensuring timely data availability. But from the empirical observations, it can be deduced that popularity of files is temporal rather than perpetual in nature and, after a certain period, content's popularity ceases most of the time which introduces the I/O bottleneck of updating replication in the disk. To handle such temporal skewed popularity of contents, we propose a dynamic data replication toolset using the power of in-memory processing by integrating MemCached server into Hadoop for getting improved performance. We compare the proposed algorithm with the traditional infrastructure and vanilla memory algorithm, as the evidence from the experimental results, the proposed design performs better i.e throughput and execution period.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126171275","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00031
M. Al-Ramahi, Ali Ahmed
Lodging sharing economy services have exponentially grown in the last decade. The users of this new economic model are also facing many challenges and difficulties which are less commonly known to the research community. In this research, we have used unique data collection and an unsupervised machine learning method to uncover the needs and concerns of the users of this new economic model. We focused on the lodging company, Airbnb, to use as our test case. Similar approaches can also be applied on other sharing economies companies. The results reported current lodging sharing services lacks regulations for disputes. Findings also revealed safety concerns of the users. Overall, this research contributes with practical managerial implications and guidelines for future research while implementing a new data collection methodology.
{"title":"Identifying Users' Concerns in Lodging Sharing Economy Using Unsupervised Machine Learning Approach","authors":"M. Al-Ramahi, Ali Ahmed","doi":"10.1109/ICDIS.2019.00031","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00031","url":null,"abstract":"Lodging sharing economy services have exponentially grown in the last decade. The users of this new economic model are also facing many challenges and difficulties which are less commonly known to the research community. In this research, we have used unique data collection and an unsupervised machine learning method to uncover the needs and concerns of the users of this new economic model. We focused on the lodging company, Airbnb, to use as our test case. Similar approaches can also be applied on other sharing economies companies. The results reported current lodging sharing services lacks regulations for disputes. Findings also revealed safety concerns of the users. Overall, this research contributes with practical managerial implications and guidelines for future research while implementing a new data collection methodology.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132043864","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00010
Cong Pu, Bryan N. Groves
With increasingly popular computing devices endowed with sensing and communicating capabilities, low power and lossy networks (LLNs) are rapidly emerging as an important part of ubiquitous computing and communication infrastructure. In order to support the vision of Internet-of-Things (IoT) and its applications, a novel routing protocol for low power and lossy networks, also referred to as RPL, has been proposed to provide efficient and reliable communication and enable the integration of resource-constrained devices into the Internet. However, due to the shared wireless medium, the lack of physical protection, and instinctive resource constraints, RPL-based LLNs are undeniably vulnerable to various Denial-of-Service (DoS) attacks. In this paper, we propose a misbehavior-aware detection scheme, called MAD, against energy depletion attack in RPL-based LLNs, where a malicious node intentionally generates and sends a large number of packets to legitimate node to excessively consume the energy resource of intermediate nodes located along the forwarding path, and finally makes the resource-constrained network suffer from denial of service. In the MAD, each node maintains a count of the number of received packets from its child node within a specific time window, and then compares the count with a dynamically calculated threshold to detect potential energy depletion attack. We conduct extensive simulation experiments for performance evaluation and comparison with the original RPL with and without adversary, respectively. The simulation results show that the proposed scheme is a viable approach against energy depletion attack in RPL-based LLNs.
{"title":"Energy Depletion Attack in Low Power and Lossy Networks: Analysis and Defenses","authors":"Cong Pu, Bryan N. Groves","doi":"10.1109/ICDIS.2019.00010","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00010","url":null,"abstract":"With increasingly popular computing devices endowed with sensing and communicating capabilities, low power and lossy networks (LLNs) are rapidly emerging as an important part of ubiquitous computing and communication infrastructure. In order to support the vision of Internet-of-Things (IoT) and its applications, a novel routing protocol for low power and lossy networks, also referred to as RPL, has been proposed to provide efficient and reliable communication and enable the integration of resource-constrained devices into the Internet. However, due to the shared wireless medium, the lack of physical protection, and instinctive resource constraints, RPL-based LLNs are undeniably vulnerable to various Denial-of-Service (DoS) attacks. In this paper, we propose a misbehavior-aware detection scheme, called MAD, against energy depletion attack in RPL-based LLNs, where a malicious node intentionally generates and sends a large number of packets to legitimate node to excessively consume the energy resource of intermediate nodes located along the forwarding path, and finally makes the resource-constrained network suffer from denial of service. In the MAD, each node maintains a count of the number of received packets from its child node within a specific time window, and then compares the count with a dynamically calculated threshold to detect potential energy depletion attack. We conduct extensive simulation experiments for performance evaluation and comparison with the original RPL with and without adversary, respectively. The simulation results show that the proposed scheme is a viable approach against energy depletion attack in RPL-based LLNs.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531689","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00029
S. Elliott
This research analyzes a seemingly malicious behavior known as a block withholding (BWH) attack between pools of cryptocurrency miners in Bitcoin-like systems featuring blockchain distributed databases. This work updates and builds on a seminal paper, The Miner's Dilemma, which studied a simplified scenario and showed that a BWH attack can be rational behavior that is profitable for the attacker. The new research presented here provides an in-depth profit analysis of a more complex and realistic BWH attack scenario, which includes mutual attacks between multiple, non-uniform Bitcoin mining pools. As a result of mathematical analysis and MATLAB modeling, this paper illustrates the Nash equilibrium conditions of a system of independent mining pools with varied mining rates and computes the equilibrium rates of mutual BWH attack. The analysis method quantifies the additional profit the largest pools extract from the system at the expense of the smaller pools. The results indicate that while the presence of BWH is a net negative for smaller pools, they must participate in BWH to maximize their remaining profits, and the results quantify the attack rates the smaller pools must maintain. Also, the smallest pools maximize profit by not attacking at all-that is, retaliation is not a rational move for them.
{"title":"Nash Equilibrium of Multiple, Non-Uniform Bitcoin Block Withholding Attackers","authors":"S. Elliott","doi":"10.1109/ICDIS.2019.00029","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00029","url":null,"abstract":"This research analyzes a seemingly malicious behavior known as a block withholding (BWH) attack between pools of cryptocurrency miners in Bitcoin-like systems featuring blockchain distributed databases. This work updates and builds on a seminal paper, The Miner's Dilemma, which studied a simplified scenario and showed that a BWH attack can be rational behavior that is profitable for the attacker. The new research presented here provides an in-depth profit analysis of a more complex and realistic BWH attack scenario, which includes mutual attacks between multiple, non-uniform Bitcoin mining pools. As a result of mathematical analysis and MATLAB modeling, this paper illustrates the Nash equilibrium conditions of a system of independent mining pools with varied mining rates and computes the equilibrium rates of mutual BWH attack. The analysis method quantifies the additional profit the largest pools extract from the system at the expense of the smaller pools. The results indicate that while the presence of BWH is a net negative for smaller pools, they must participate in BWH to maximize their remaining profits, and the results quantify the attack rates the smaller pools must maintain. Also, the smallest pools maximize profit by not attacking at all-that is, retaliation is not a rational move for them.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"153 6S 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115981583","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}
Pub Date : 2019-06-01DOI: 10.1109/ICDIS.2019.00040
Jonathan West, J. Hale, M. Papa, P. Hawrylak
Within the nuclear reactor domain, many of the assets which were once analog are being phased out and replaced with digital assets. The ability to automatically identify which of these digital assets are also critical assets as defined by the Nuclear Regulatory Commission (NRC) is an important step in building an effective cybersecurity program for the nuclear power domain. This paper presents an approach to automatically identify these critical digital assets. Three variations of this approach are presented in this paper. The runtime of these three implementations is obtained to demonstrate how each scales as network model sizes for nuclear reactors increase.
{"title":"Automatic Identification of Critical Digital Assets","authors":"Jonathan West, J. Hale, M. Papa, P. Hawrylak","doi":"10.1109/ICDIS.2019.00040","DOIUrl":"https://doi.org/10.1109/ICDIS.2019.00040","url":null,"abstract":"Within the nuclear reactor domain, many of the assets which were once analog are being phased out and replaced with digital assets. The ability to automatically identify which of these digital assets are also critical assets as defined by the Nuclear Regulatory Commission (NRC) is an important step in building an effective cybersecurity program for the nuclear power domain. This paper presents an approach to automatically identify these critical digital assets. Three variations of this approach are presented in this paper. The runtime of these three implementations is obtained to demonstrate how each scales as network model sizes for nuclear reactors increase.","PeriodicalId":181673,"journal":{"name":"2019 2nd International Conference on Data Intelligence and Security (ICDIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131113905","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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