Oni Omoyemi Abimbola, Akinyemi Bodunde Odunola, A. Temitope, G. Aderounmu, Kamagaté Beman Hamidja
Most of the existing solutions in cybersecurity analysis has been centered on identifying threats and vulnerabilities, and also providing suitable defense mechanisms to improve the robustness of the cyberspace network. These solutions lack effective capabilities to countermeasure the effect of risks and perform long-term prediction. In this paper, an improved risk assessment model for cyberspace security that will effectively predict and mitigate the consequences of risk was developed. Real-time vulnerabilities of a selected network were scanned and analysed and the ease of vulnerability exploitability was assessed. A Risk Assessment Model was formulated using the synergy of Absorbing Markov Chain and Markov Reward Model. The model was utilized to analyse cybersecurity state of the selected network. The proposed model was simulated using R- Statistical Package, and its performance was evaluated by benchmarking with an existing model, using Reliability and Availability as metrics. The result showed that the proposed model has higher reliability and availability over the existing model. This implied that there is a significant improvement in the assessment of security situations in a cyberspace network.
{"title":"An Improved Stochastic Model for Cybersecurity Risk Assessment","authors":"Oni Omoyemi Abimbola, Akinyemi Bodunde Odunola, A. Temitope, G. Aderounmu, Kamagaté Beman Hamidja","doi":"10.5539/cis.v12n4p96","DOIUrl":"https://doi.org/10.5539/cis.v12n4p96","url":null,"abstract":"Most of the existing solutions in cybersecurity analysis has been centered on identifying threats and vulnerabilities, and also providing suitable defense mechanisms to improve the robustness of the cyberspace network. These solutions lack effective capabilities to countermeasure the effect of risks and perform long-term prediction. In this paper, an improved risk assessment model for cyberspace security that will effectively predict and mitigate the consequences of risk was developed. Real-time vulnerabilities of a selected network were scanned and analysed and the ease of vulnerability exploitability was assessed. A Risk Assessment Model was formulated using the synergy of Absorbing Markov Chain and Markov Reward Model. The model was utilized to analyse cybersecurity state of the selected network. The proposed model was simulated using R- Statistical Package, and its performance was evaluated by benchmarking with an existing model, using Reliability and Availability as metrics. The result showed that the proposed model has higher reliability and availability over the existing model. This implied that there is a significant improvement in the assessment of security situations in a cyberspace network.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"20 1","pages":"96-110"},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88216287","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}
Adil Iguider, K. Bousselam, Oussama Elissati, Mouhcine Chami, A. En-Nouaary
The codesign is a robust methodology, used in modern embedded systems with the objective of achieving the functional specifications and meeting the non-functional requirements. The most interesting step in the codesing is the process of Hardware/Software Partitioning. The aim is to decide which functionalities of the system should be implemented in hardware ($HW$) or in software ($SW$). In this article, a new heuristic algorithm is proposed to simultaneously optimize the hardware area (cost) and the execution time (performance) of a multiprocessor system. The proposed algorithm is inspired from game theory and especially from the GO game. The system is modeled using the DAG graph (Data Acyclic Graph), and two players (HW player and SW player) play in turn and choose a block (functionality) from the graph (system). The HW player has the goal of optimizing the global HW area while the SW player has the objective of minimizing the global execution time. After the game termination, and based on the 0-1 Knapsack algorithm, a step of refinement is used to meet the constraint on the total hardware area or on the overall execution time if a constraint is pre-defined. Experimental results show that the proposed algorithm gives better solutions compared to the Simulated Annealing algorithm and the Genetic Algorithm.
{"title":"GO Game Inspired Algorithm for Hardware Software Partitioning in Multiprocessor Embedded Systems","authors":"Adil Iguider, K. Bousselam, Oussama Elissati, Mouhcine Chami, A. En-Nouaary","doi":"10.5539/cis.v12n4p111","DOIUrl":"https://doi.org/10.5539/cis.v12n4p111","url":null,"abstract":"The codesign is a robust methodology, used in modern embedded systems with the objective of achieving the functional specifications and meeting the non-functional requirements. The most interesting step in the codesing is the process of Hardware/Software Partitioning. The aim is to decide which functionalities of the system should be implemented in hardware ($HW$) or in software ($SW$). In this article, a new heuristic algorithm is proposed to simultaneously optimize the hardware area (cost) and the execution time (performance) of a multiprocessor system. The proposed algorithm is inspired from game theory and especially from the GO game. The system is modeled using the DAG graph (Data Acyclic Graph), and two players (HW player and SW player) play in turn and choose a block (functionality) from the graph (system). The HW player has the goal of optimizing the global HW area while the SW player has the objective of minimizing the global execution time. After the game termination, and based on the 0-1 Knapsack algorithm, a step of refinement is used to meet the constraint on the total hardware area or on the overall execution time if a constraint is pre-defined. Experimental results show that the proposed algorithm gives better solutions compared to the Simulated Annealing algorithm and the Genetic Algorithm.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"24 1","pages":"111-122"},"PeriodicalIF":0.0,"publicationDate":"2019-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90227390","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}
With the increased accessibility of research information, the demands on research information systems (RIS) that are expected to automatically generate and process knowledge are increasing. Furthermore, the quality of the RIS data entries of the individual sources of information causes problems. If the data is structured in RIS, users can read and filter out their information and knowledge needs without any problems. This technique, which nevertheless allows text databases and text sources to be analyzed and knowledge extracted from unknown texts, is referred to as text mining or text data mining based on the principles of data mining. Text mining allows automatically classifying large heterogeneous sources of research information and assigning them to specific topics. Research information has always played a major role in higher education and academic institutions, although they were usually available in unstructured form in RIS and grow faster than structured data. This can be a waste of time searching for RIS staff in universities and can lead to bad decision-making. For this reason, the present paper proposes a new approach to obtaining structured research information from heterogeneous information systems. It is a subset of an approach to the semantic integration of unstructured data using the example of a RIS. The purpose of this paper is to investigate text and data mining methods in the context of RIS and to develop an improvement quality model as an aid to RIS using universities and academic institutions to enrich unstructured research information.
{"title":"A Text and Data Analytics Approach to Enrich the Quality of Unstructured Research Information","authors":"Otmane Azeroual","doi":"10.5539/cis.v12n4p84","DOIUrl":"https://doi.org/10.5539/cis.v12n4p84","url":null,"abstract":"With the increased accessibility of research information, the demands on research information systems (RIS) that are expected to automatically generate and process knowledge are increasing. Furthermore, the quality of the RIS data entries of the individual sources of information causes problems. If the data is structured in RIS, users can read and filter out their information and knowledge needs without any problems. This technique, which nevertheless allows text databases and text sources to be analyzed and knowledge extracted from unknown texts, is referred to as text mining or text data mining based on the principles of data mining. Text mining allows automatically classifying large heterogeneous sources of research information and assigning them to specific topics. Research information has always played a major role in higher education and academic institutions, although they were usually available in unstructured form in RIS and grow faster than structured data. This can be a waste of time searching for RIS staff in universities and can lead to bad decision-making. For this reason, the present paper proposes a new approach to obtaining structured research information from heterogeneous information systems. It is a subset of an approach to the semantic integration of unstructured data using the example of a RIS. The purpose of this paper is to investigate text and data mining methods in the context of RIS and to develop an improvement quality model as an aid to RIS using universities and academic institutions to enrich unstructured research information.","PeriodicalId":14676,"journal":{"name":"J. Chem. Inf. Comput. Sci.","volume":"7 1","pages":"84-95"},"PeriodicalIF":0.0,"publicationDate":"2019-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82366069","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：