With ever growing cyber threats on critical infrastructures, need of deploying the security measures to protect these should be of utmost priority. However, without knowing about the assets in the network and what nee...
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Brain-Computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to...
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For developing and using various features provided by the internet by all the sections of the society, it is important to make the technology accessible irrespective of the language. So, the translation between the la...
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Demand-side flexibility is crucial to balancing supply and demand, as renewable energy sources are increasingly integrated into the energy mix, and heating and transport systems are becoming more and more electrified....
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The next level of the smart grid contains essential components like advanced Interface Energy Meters (IEMs), which play a major role in SAMAST (Scheduling, Accounting, Metering and Settlement of Transactions in Electr...
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Effective risk management and compliance adherence are paramount for the success of financial institutions and organizations. However, they often face significant challenges due to fraudulent activities, with ATM frau...
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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
Effective risk management and compliance adherence are paramount for the success of financial institutions and organizations. However, they often face significant challenges due to fraudulent activities, with ATM fraud, among others, emerging as a prevalent issue in today’s banking landscape. We proposed a novel profiling-based one-class classification (OCC) method to solve this problem. Then training phase of our approach employs the K-Means clustering algorithm to cluster non-fraudulent transactions exhibiting similar characteristics and patterns. A rule is generated from each cluster, thereby in a rule set comprising K rules, each consisting of conditions based on the lower and upper bounds on all features. This rule set is employed to identify fraudulent transactions presented in the test phase because ours is an OCC method. One distinctive feature of our approach is its interpretability and explainability, which is crucial for understanding the model's predictions. Overall, our proposed approach demonstrates the best performance vis-à-vis that of various state-of-the-art OCC methods in terms of classification rate. Additionally, we provide sensitivity analysis by varying the number of conditions violated across the K rules.
Gaining the empathy and trust of customers is paramount in the financial domain. However, the recurring occurrence of fraudulent activities undermines both of these factors. ATM fraud is a prevalent issue faced in tod...
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ISBN:
(数字)9798350354836
ISBN:
(纸本)9798350354843
Gaining the empathy and trust of customers is paramount in the financial domain. However, the recurring occurrence of fraudulent activities undermines both of these factors. ATM fraud is a prevalent issue faced in today's banking landscape. The critical challenges in fraud datasets are highly imbalanced datasets, evolving fraud patterns, and lack of explainability. In this study, we handled these techniques on an ATM transaction dataset collected from India. In binary classification, we investigated the effectiveness of various over-sampling techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) and its variants, Generative Adversarial Networks (GAN), to achieve oversampling. Gradient Boosting Tree (GBT), outperformed the rest of the techniques by achieving an AUC of 0.963, and Decision Tree (DT) stands second with an AUC of 0.958. In terms of complexity and interpretability, DT is the winner. Among the oversampling approaches, SMOTE and its variants performed better. We incorporated explainable artificial intelligence (XAI) and Causal Inference (CI) in the fraud detection framework and studied them via various analyses. Further, we provided managerial impact.
Perception of establishing a Security Operations Centre (SOC) depends on the criticality of information assurance and security operations in response to the ever-changing security threat landscape and adoption of form...
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Securing devices used in the Internet of Things (IoT) applications is a challenging task. This paper provides a succinct review of the challenges in securing the Internet of Things (IoT) devices, types of attacks, sof...
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Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive su...
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Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.
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