Mehmet Ali Yalçınkaya; Ecir Uğur Küçüksille
Abstract
The widespread use of web applications and running on sensitive data has made them one of the most significant targets of cyber attackers. One of the most crucial security measures that can be taken is the detection and closure of vulnerabilities on web applications before attackers. In this study, a ...
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The widespread use of web applications and running on sensitive data has made them one of the most significant targets of cyber attackers. One of the most crucial security measures that can be taken is the detection and closure of vulnerabilities on web applications before attackers. In this study, a web application vulnerability scanner was developed based on dynamic analysis and artificial intelligence, which could test web applications using GET and POST methods and had test classes for 21 different vulnerability types. The developed vulnerability scanner was tested on a web application test laboratory, which was created within the scope of this study and had 262 different web applications. A data set was created from the results of the tests performed using the developed vulnerability scanner. In this study, as a first stage, web page classification was made using the mentioned data set. The highest success rate in the page classification process was determined by 95.39% using the Random Forest Algorithm. The second operation performed using the dataset was the association analysis between vulnerabilities. The proposed model saved the 21% time than the standard scanning model. The page classification process was also used in the crawling of the web application in this study.
Felisa Córdova; Claudia Durán; Fredi Palominos
Abstract
Port organizations have focused their efforts on physical or tangible assets, generating profitability and value. However, it is recognized that the greatest sustainable competitive advantage is the creation of knowledge using the intangible assets of the organization. The Balanced ScoreCard, ...
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Port organizations have focused their efforts on physical or tangible assets, generating profitability and value. However, it is recognized that the greatest sustainable competitive advantage is the creation of knowledge using the intangible assets of the organization. The Balanced ScoreCard, as a performance tool, has incorporated intangible assets such as intellectual, structural and social capital into management. In this way, the port community can count on new forms of managing innovation, strengthening organizational practices, and increasing collaborative work teams. In this study, the concepts from analysis of the cognitive SWOT are applied to diagnose the port activity and its community. In workshops with experts and from the vision, mission, cognitive SWOT and strategies, a cognitive strategic map considering strategic objectives and indicators is designed in the customer, processes, and learning and growth axis for the port and port community. Causal relationships between objectives, associated indicators and incidence factors are established in a forward way from learning and growth axis to customer axis. Then, the incidence matrix is developed and the direct and indirect effects between factors are analyzed, which allows recommending the future course of the port and its community.
Ahmed BaniMustafa
Abstract
This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public ...
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This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes which is known to deteriorate the performance of classifiers. It also influences its validity and generalizablity. The classification models in this study were built using five machine learning algorithms known as PLS-DA, MLP, SVM, C4.5 and ID3. This model is built after carrying out a number of intensive data preprocessing procedures to tackle the problem of imbalanced classes and improve the performance of the constructed classifiers.These procedures involves applying data transformation, normalization, standardization, re-sampling and data reduction procedures using a number of variables importance scorers. The best performance was achieved by building an MLP model that was trained and tested using five-fold cross-validation using datasets that were re-sampled using SMOTE method and then reduced using SVM variable importance scorer. This model was successful in classifying samples with excellent accuracy and also in identifying the potential disease biomarkers. The results confirm the validity of metabolomics data mining for diagnosis of cachexia. It also emphasizes the importance of data preprocessing procedures such as sampling and data reduction for improving data mining results, particularly when data suffers from the problem of imbalanced classes.