1,417 research outputs found

    A Survey on Explainable Anomaly Detection

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    In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners. As anomaly detection algorithms are increasingly used in safety-critical domains, providing explanations for the high-stakes decisions made in those domains has become an ethical and regulatory requirement. Therefore, this work provides a comprehensive and structured survey on state-of-the-art explainable anomaly detection techniques. We propose a taxonomy based on the main aspects that characterize each explainable anomaly detection technique, aiming to help practitioners and researchers find the explainable anomaly detection method that best suits their needs.Comment: Paper accepted by the ACM Transactions on Knowledge Discovery from Data (TKDD) for publication (preprint version

    An Overview of the Use of Neural Networks for Data Mining Tasks

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    In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

    EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

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    Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

    Cyberattack triage using incremental clustering for intrusion detection systems

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    Intrusion detection systems (IDSs) are devices or software applications that monitor networks or systems for malicious activities and signals alerts/alarms when such activity is discovered. However, an IDS may generate many false alerts which affect its accuracy. In this paper, we develop a cyberattack triage algorithm to detect these alerts (so-called outliers). The proposed algorithm is designed using the clustering, optimization and distance-based approaches. An optimization-based incremental clustering algorithm is proposed to find clusters of different types of cyberattacks. Using a special procedure, a set of clusters is divided into two subsets: normal and stable clusters. Then, outliers are found among stable clusters using an average distance between centroids of normal clusters. The proposed algorithm is evaluated using the well-known IDS data sets—Knowledge Discovery and Data mining Cup 1999 and UNSW-NB15—and compared with some other existing algorithms. Results show that the proposed algorithm has a high detection accuracy and its false negative rate is very low. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.This research was conducted in Internet Commerce Security Laboratory (ICSL) funded by Westpac Banking Corporation Australia. In addition, the research by Dr. Sona Taheri and A/Prof. Adil Bagirov was supported by the Australian Government through the Australian Research Council’s Discovery Projects funding scheme (DP190100580)

    EXISTING OUTLIER VALUES IN FINANCIAL DATA VIA WAVELET TRANSFORM

    Get PDF
    Outlier detection is one of the major problems of large datasets. Outliers have been detected using several methods such as the use of asymmetric winsorized mean. Al-Khazaleh et al. (2015) has proposed new methods of detecting the outlier values. This is achieved by combining the asymmetric winsorized mean with the famous spectral analysis function which is the Wavelet Transform (WT). Thus, this method is regarded as MTAWM. In this article, we will expand this work using the modern Wavelet function known as the Maximum Overlapping Wavelet Transform (MODWT). The results of the study shows that after comparing the new technique with the previous mentioned techniques using financial data from Amman Stock Exchange (ASE), the Maximum overlapping wavelet transform- asymmetric winsorized mean (MWAW) was considered the best method in outlier detections

    Meta-analysis of fraud, waste and abuse detection methods in healthcare

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    Fraud, waste and abuse have been a concern in healthcare system due to the exponential increase in the loss of revenue, loss of reputation and goodwill, and a rapid decline in the relationship between healthcare providers and patients. Consequently, fraud, waste and abuse result in a high cost of healthcare services, decreased quality of care, and threat to patients’ lives. Its enormous side effects in healthcare have attracted diverse efforts in the healthcare industry, data analytics industry and research communities towards the development of fraud detection methods. Hence, this study examines and analyzes fraud, waste and abuse detection methods used in healthcare, to reveal the strengths and limitations of each approach. Eighty eight literatures obtained from journal articles, conference proceedings and books based on their relevance to the research problem were reviewed. The result of this review revealed that fraud detection methods are difficult to implement in the healthcare system because new fraud patterns are constantly developed to circumvent fraud detection methods. Research in medical fraud assessment is limited due to data limitations as well as privacy and confidentiality concerns.Keywords: abuse, fraud, healthcare, waste, fraud detection method
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