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Τεκμήριο An application of supervised and unsupervised learning approaches to telecommunications fraud detection(2008-10) Hilas, Constantinos S.; Mastorocostas, Paris A.This paper investigates the usefulness of applying different learning approaches to a problem of telecommunications fraud detection. Five different user models are compared by means of both supervised and unsupervised learning techniques, namely the multilayer perceptron and the hierarchical agglomerative clustering. One aim of the study is to identify the user model that best identifies fraud cases. The second task is to explore different views of the same problem and see what can be learned form the application of each different technique. All data come from real defrauded user accounts in a telecommunications network. The models are compared in terms of their performances. Each technique’s outcome is evaluated with appropriate measures.Τεκμήριο A block-diagonal recurrent fuzzy neural network for system identification(2009-10) Mastorocostas, Paris A.; Hilas, Constantinos S.A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled dynamic block-diagonal fuzzy neural network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark identification problem, where a dynamic system is to be identified. Additionally, an application of the proposed model to the problem of the analysis of lung sounds is presented. Particularly, a filter based on the DBD-FNN is developed, trained with the RENNCOM method. Extensive experimental and simulation results are given and performance comparisons with a series of other models are conducted, highlighting the modeling characteristics of DBD-FNN as an identification tool and the effectiveness of the proposed separation filter.Τεκμήριο A block-diagonal recurrent fuzzy neural network for system identification(2009-10-01) Mastorocostas, Paris A.; Hilas, Constantinos S.A recurrent fuzzy neural network with internal feedback is suggested in this paper. The network is entitled dynamic block-diagonal fuzzy neural network (DBD-FNN), and constitutes a generalized Takagi-Sugeno-Kang fuzzy system, where the consequent parts of the fuzzy rules are small Block-Diagonal Recurrent Neural Networks. The proposed model is applied to a benchmark identification problem, where a dynamic system is to be identified. Additionally, an application of the proposed model to the problem of the analysis of lung sounds is presented. Particularly, a filter based on the DBD-FNN is developed, trained with the RENNCOM method. Extensive experimental and simulation results are given and performance comparisons with a series of other models are conducted, highlighting the modeling characteristics of DBD-FNN as an identification tool and the effectiveness of the proposed separation filter.Τεκμήριο Change Point Detection in Time Series Using Higher-Order Statistics: A Heuristic Approach(2013) Hilas, Constantinos S.; Rekanos, Ioannis T.; Mastorocostas, Paris Ast.Changes in the level of a time series are usually attributed to an intervention that affects its temporal evolution. The resulting time series are referred to as interrupted time series and may be used to identify the events that caused the intervention and to quantify their impact. In the present paper, a heuristic method for level change detection in time series is presented. The method uses higher-order statistics, namely, the skewness and the kurtosis, and can identify both the existence of a change in the level of the time series and the time instance when it has happened. The technique is straightforwardly applicable to the detection of outliers in time series and promises to have several applications. The method is tested with both simulated and real-world data and is compared to other popular change detection techniques.Τεκμήριο Clustering of telecommunications user profiles for fraud detection and security enhancement in large corporate networks: a case study(2015-07-01) Hilas, Constantinos S.; Mastorocostas, Paris A.; Rekanos, Ioannis T.A user’s transactions with modern networks and services produce a vast amount of user related data. The byproduct of every phone call a person makes or every web page one visits is translated into a log record with usage data. By studying these log records, the user’s behavior is revealed and one may come up with clues about user preferences, identify security issues, or discover fraudulent use of the network or the service one provides. Thus, the modeling of network users’ behavior may serve as an invaluable tool for the IT manager. In this paper, many of these issues are discussed and emphasis is given on the construction of appropriate user behavior representation in telecommunications. As an example, the application of two clustering techniques is presented, with the task to identify appropriate user behavior representations (profiles) inside a large organization’s telecommunications network, in order to spot fraudulent usage. Through this study a researcher and/or the organization’s network manager may gain more insight into the problems of user profiling and fraud detection.Τεκμήριο Designing an expert system for fraud detection in private telecommunications networks(2009-11) Hilas, Constantinos S.Telecommunications fraud not only burdens telecom provider’s accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate user’s account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firm or organization, superimposed fraud may go undetected for some time. The present paper deals with the detection of fraudulent telecom activity inside large organizations’ premises. Focus is given on superimposed fraud detection. The problem is attacked via the construction of an expert system which incorporates both the network administrator’s expert knowledge and knowledge derived from the application of data mining techniques on real world data.Τεκμήριο A genetic programming approach to telecommunications fraud detection and classification(2014-03) Hilas, Constantinos S.; Kazarlis, Spyridon A.; Rekanos, Ioannis T.; Mastorocostas, Paris A.Telecommunications fraud has drawn the researchers’ attention due to the huge economic burden on companies and to the interesting aspect of users’ behavior modeling. In the present paper, an application of genetic programming to fraud detection is presented. Genetic programming is used for case classification in order to distinguish between normal and fraudulent activities in a telecommunications network. Implications to appropriate user behavior modeling are, also, discussed. Real world cases of defrauded user accounts are modeled by means of selected usage features and comparisons with other approaches are made.Τεκμήριο Motivating students’ participation in a computer networks course by means of magic, drama and games(2014) Hilas, Constantinos S.; Politis, AnastasiosThe recent economic crisis has forced many universities to cut down expenses by packing students into large lecture groups. The problem with large auditoria is that they discourage dialogue between students and faculty and they burden participation. Adding to this, students in computer science courses usually find the field to be full of theoretical and technical concepts. Lack of understanding leads them to lose interest and / or motivation. Classroom experience shows that the lecturer could employ alternative teaching methods, especially for early-year undergraduate students, in order to grasp their interest and introduce basic concepts. This paper describes some of the approaches that may be used to keep students interested and make them feel comfortable as they comprehend basic concepts in computer networks. The lecturing procedure was enriched with games, magic tricks and dramatic representations. This approach was used experimentally for two semesters and the results were more than encouraging.Τεκμήριο A Recurrent Neural Filter for Adaptive Noise Cancellation(2006) Mastorocostas, Paris A.; Varsamis, Dimitris N.; Mastorocostas, Constantinos A.; Hilas, Constantinos S.This paper presents a dynamic neural filter for adaptive noise cancellation. The cancellation task is transformed to a system-identification problem, which is tackled by use of the Block-Diagonal Recurrent Neural Network. The filter is applied to a benchmark noise cancellation problem, where a comparative analysis with a series of other dynamic models is conducted, underlining the effectiveness of the proposed filter and its superior performance over its competing rivals.Τεκμήριο ReNFFor: a recurrent neurofuzzy forecaster for telecommunications data(2012-01-21) Mastorocostas, Paris A.; Hilas, Constantinos S.In this work, a dynamic neurofuzzy system for forecasting outgoing telephone calls in a University Campus is proposed. The system comprises modified Takagi–Sugeno–Kang fuzzy rules, where the rules’ consequent parts are small neural networks with unit internal recurrence. The characteristics of the proposed forecaster, which is entitled recurrent neurofuzzy forecaster, are depicted via a comparative analysis with a series of well-known forecasting models.Τεκμήριο SCOLS-FuM: A Hybrid Fuzzy Modeling Method for Telecommunications Time-Series Forecasting(2014) Mastorocostas, Paris A.; Hilas, Constantinos S.An application of fuzzy modeling to the problem of telecommunications time-series prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on Subtractive Clustering (SC) and the Orthogonal Least Squares (OLS) techniques. Particularly, the SC is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world telecommunications data, where the characteristics of the proposed forecaster are highlighted.Τεκμήριο Seasonal decomposition and forecasting of telecommunication data: A comparative case study(2006-06) Hilas, Constantinos S.; Goudos, Sotirios K.; Sahalos, John N.In this paper, forecasting models for the monthly outgoing telephone calls in a University Campus are presented. The data have been separated in the categories of international and national calls as well as calls to mobile phones. The total number of calls has also been analyzed. Three different methods, namely the Seasonal Decomposition, Exponential Smoothing Method and SARIMA Method, have been used. Forecasts with 95% confidence intervals were calculated for each method and compared with the actual data. The outcome of this work can be used to predict future demands for the telecommunications network of the University.Τεκμήριο Telecommunications data forecasting based on a dynamic neuro-fuzzy network(2011-05) Mastorocostas, Paris A.; Hilas, Constantinos S.In this work a dynamic neuro-fuzzy network (DyNF-Net) is proposed, which is applied on the outgoing telephone traffic of a large organization. It is a modified Takagi-Sugeno-Kang fuzzy neural network, where the consequent parts of the fuzzy rules are neural networks with internal recurrence, thus introducing dynamics to the overall system. Real world telecommunications data are used in order to compare the DyNF-Net to well-established forecasting models. The comparison highlights the particular characteristics of the proposed neuro-fuzzy network.Τεκμήριο User Profiling for Fraud Detection in Telecommunication Networks(2005) Hilas, Constantinos S.; Sahalos, John N.Telecommunications fraud is increasing dramatically each year resulting in loss of a large amount of euros worldwide. A statistical machine learning method is presented that constructs user profiles for the detection of fraudulent activities in telecommunications networks. The approach presented here can be used for the detection of superimposed or hacking fraud, works well for mid-term decisions and cannot be used for on-line account comparison.