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Mario Lemes Proenca - One of the best experts on this subject based on the ideXlab platform.

  • Anomaly detection using the correlational paraconsistent machine with digital signatures of Network Segment
    Information Sciences, 2017
    Co-Authors: Eduardo H. M. Pena, J.j.p.c. Rodrigues, Luiz F. Carvalho, Sylvio Barbon, Mario Lemes Proenca
    Abstract:

    Abstract This study presents the correlational paraconsistent machine (CPM), a tool for anomaly detection that incorporates unsupervised models for traffic characterization and principles of paraconsistency, to inspect irregularities at the Network traffic flow level. The CPM is applied for the mathematical foundation of uncertainties that may arise when establishing normal Network traffic behavior profiles, providing means to support the consistency of the information sources chosen for anomaly detection. The experimental results from a real traffic trace evaluation suggest that CPM responses could improve anomaly detection rates.

  • A Pratical Approach for Automatic Generation of Network Segment Traffic Baselines DOI: 10.14209/jcis.2005.2
    Journal of Communication and Information Systems, 2015
    Co-Authors: Mario Lemes Proenca, Camiel Coppelmans, Mauricio Luis Bottoli, Antonio Marcos Alberti, Fábio Sakuray, Leonardo De Souza Mendes
    Abstract:

    This paper presents a model for automatic generation of a baseline which characterizes the traffic of Network Segments. The use of the baseline concept allows the manager to: identify limitations and crucial points of the Network: learn about the actual status of use of the Network resources: be able to gain better control of the use of Network resources and to establish thresholds for the generation of more accurate and intelligent alarms, better suited to the actual characteristics of the Network. Also presented is an alarm system that relies on the baseline and that provides the automation of a task that can be performed manually by the Network manager, based on his empirical knowledge of the Network. A tool known as GBA was used for the development, implementation and testing of these functions. Moreover, some results obtained with the practical use of the baseline as well as of the alaml system in the management of Network Segments, are also presented. The results obtained validate the experiment and show, in practice, significant advantages in their use for Network management.

  • ACO and GA metaheuristics for anomaly detection
    2015 34th International Conference of the Chilean Computer Science Society (SCCC), 2015
    Co-Authors: Anderson H. Hamamoto, Luiz F. Carvalho, Mario Lemes Proenca
    Abstract:

    Computer Networks have become an essential technology to society, providing information and services to its users. Due to its importance, Network management is necessary to maintain communication reability and security. Thus, in order to assist Network administrators achieve these properties, we propose a Digital Signature of Network Segment using Flows Analysis (DSNSF), which uses the Network behavior of previous weeks to predict the Network traffic of a given day. For this purpose, we have developed an algorithm derived from Genetic Algorithm (GA) able to construct the DSNSF. Also, this approach is compared with a Ant Colony Optimization (ACO) modification used to the same objective. Both methods are bio-inspired models and are widely applied to optimization problems. We compare the resulting digital signature with the real traffic and use Correlation Coefficient and Normalized Square Mean Error to evaluate the performance of the algorithms.

  • GLOBECOM - A novel anomaly detection system based on seven-dimensional flow analysis
    2013 IEEE Global Communications Conference (GLOBECOM), 2013
    Co-Authors: Marcos V.o. De Assis, J.j.p.c. Rodrigues, Mario Lemes Proenca
    Abstract:

    Anomaly detection in large-scale Networks is not a simple task, although there are several studies in this area. The continuous expansion of computer Networks results in increased complexity of management processes. Thus, simple and efficient anomaly detection mechanisms are required in order to assist the management of these Networks. In this paper, we present an anomaly detection system using a seven-dimensional flow analysis. To accomplish this objective, we used the improved Holt-Winters forecasting method on the traffic characterization of each one of the different analyzed dimensions, here called Digital Signature of Network Segment using Flow analysis (DSNSF). The system not only warns the Network administrator about the problem, but also provides the necessary information to solve it. Real data are collected and used by the system to measure its efficiency and accuracy.

  • Digital signature to help Network management using principal component analysis and K-means clustering
    2013 IEEE International Conference on Communications (ICC), 2013
    Co-Authors: Gilberto Fernandes, J.j.p.c. Rodrigues, Alexandro M. Zacaron, Mario Lemes Proenca
    Abstract:

    The complexity of a Network nowadays and its increasingly amount of traffic data has contributed to the occurrence of problems and anomalies. A traffic characterization, called Digital Signature for Network Segment using Flow Analysis (DSNSF) is important to help Network Management in avoiding these problems. We propose two methods to generate a digital signature capable of describing the traffic behavior. For this purpose, we used the statistical method Principal Component Analysis (PCA) and the clustering algorithm K-Means. The resulting DSNSFs are then submitted to testing with real data to evaluate its precision.

Mario Lemes Proença - One of the best experts on this subject based on the ideXlab platform.

  • Firefly Algorithm in Telecommunications
    Bio-Inspired Computation in Telecommunications, 2015
    Co-Authors: Mario H.a.c. Adaniya, Lucas D.h. Sampaio, Luiz F. Carvalho, Paul Jean E. Jeszensky, Bruno Bogaz Zarpelão, Taufik Abrao, Mario Lemes Proença
    Abstract:

    This chapter discusses the nature-inspired metaheuristic firefly algorithm (FA) applied in telecommunications. FA has been developed based on the behavior of the fireflies and the light emitted, where the brightest firefly attracts the others in his direction. Besides combining stochastic behavior and a population-based multimodal characteristic, the FA approach is able to solve optimization problems in different areas of knowledge such as engineering, robotics, combinatorial optimization, and so on. This chapter aims to show the FA performance in two distinct Network optimization problems: traffic characterization and energy-efficient cooperative Networks. In the first optimization problem, FA is applied as a clustering algorithm to create a Network traffic pattern called Digital Signature of Network Segment using Flow analysis (DSNSF); in the second, FA has been applied to the energy-efficiency maximization problem in multicarrier direct sequence code division multiple access (MC-DS/CDMA) cooperative Networks.

  • SCCC - Digital Signature of Network Segment Using Flow Analysis through Genetic Algorithm and ACO Metaheuristics
    2014 33rd International Conference of the Chilean Computer Science Society (SCCC), 2014
    Co-Authors: Paulo Galego R. Hernandes, Luiz F. Carvalho, Mario Lemes Proença
    Abstract:

    Traffic monitoring is an important task for Network administrators who require tools to aid in the detection of changes in the Network's routine. In this paper, we use a Digital Signature of Network Segment using Flow Analysis (DSNSF) as a technique to describe standard Network behavior aiming to support Network management through traffic characterization. We have collected real data set from State University of Londrina (UEL), using data flow attributes such as bits, packets and number of flows. Our novel model uses Genetic Algorithm to optimize the process, which consists or organizing the data to display graphically a standard Network behavior. To accomplish this task, we compared our novel model with another similar method, Ant Colony Optimization for Digital Signature (ACODS), evaluating these models to measure their accuracy.

  • Anomaly detection using digital signature of Network Segment with adaptive ARIMA model and Paraconsistent Logic
    2014 IEEE Symposium on Computers and Communications (ISCC), 2014
    Co-Authors: Eduardo H. M. Pena, J.j.p.c. Rodrigues, Sylvio Barbon, Mario Lemes Proença
    Abstract:

    Detecting anomalies accurately in Network traffic behavior is essential for a variety of Network management and security tasks. This paper presents an anomaly detection approach employing Digital Signature of Network Segment using Flow Analysis (DSNSF), generated with an ARIMA model. Also, a functional algorithm based on a non-classical logic called Paraconsistent Logic is proposed aiming to avoid high false alarms rates. The key idea of the proposed approach is to characterize the normal behavior of Network traffic and then identify the traffic patterns behavior that might harm Networks services. Experimental results on a real Network demonstrate the effectiveness the proposed approach. The results are promising, showing that the flow analysis performed is able to detect anomalous traffic with precision, sensitivity and good performance.

  • ISCC - Anomaly detection using digital signature of Network Segment with adaptive ARIMA model and Paraconsistent Logic
    2014 IEEE Symposium on Computers and Communications (ISCC), 2014
    Co-Authors: Eduardo H. M. Pena, J.j.p.c. Rodrigues, Sylvio Barbon, Mario Lemes Proença
    Abstract:

    Detecting anomalies accurately in Network traffic behavior is essential for a variety of Network management and security tasks. This paper presents an anomaly detection approach employing Digital Signature of Network Segment using Flow Analysis (DSNSF), generated with an ARIMA model. Also, a functional algorithm based on a non-classical logic called Paraconsistent Logic is proposed aiming to avoid high false alarms rates. The key idea of the proposed approach is to characterize the normal behavior of Network traffic and then identify the traffic patterns behavior that might harm Networks services. Experimental results on a real Network demonstrate the effectiveness the proposed approach. The results are promising, showing that the flow analysis performed is able to detect anomalous traffic with precision, sensitivity and good performance.

  • Digital Signature of Network Segment Using Flow Analysis through Genetic Algorithm and ACO Metaheuristics
    2014 33rd International Conference of the Chilean Computer Science Society (SCCC), 2014
    Co-Authors: Paulo Galego R. Hernandes, Luiz F. Carvalho, Mario Lemes Proença
    Abstract:

    Traffic monitoring is an important task for Network administrators who require tools to aid in the detection of changes in the Network's routine. In this paper, we use a Digital Signature of Network Segment using Flow Analysis (DSNSF) as a technique to describe standard Network behavior aiming to support Network management through traffic characterization. We have collected real data set from State University of Londrina (UEL), using data flow attributes such as bits, packets and number of flows. Our novel model uses Genetic Algorithm to optimize the process, which consists or organizing the data to display graphically a standard Network behavior. To accomplish this task, we compared our novel model with another similar method, Ant Colony Optimization for Digital Signature (ACODS), evaluating these models to measure their accuracy.

Marcos V.o. De Assis - One of the best experts on this subject based on the ideXlab platform.

  • Digital signature of Network Segment for healthcare environments support
    IRBM, 2014
    Co-Authors: L. F. Carvalho, Marcos V.o. De Assis, J.j.p.c. Rodrigues, Gabriela Fernandes, M. Lemes Proença
    Abstract:

    Network technologies have facilitated the implementation of health services based on ubiquitous systems, allowing pervasive monitoring of patients in their daily activities without significantly interfering in their lifestyle. This event entails the need to ensure adequate management and security of healthcare environment Networks. However, traffic monitoring became an arduous work, requiring autonomic mechanisms to describe the Network's normal behavior. Thus, Digital Signature of Network Segment using Flow analysis(DSNSF) as a mechanism to assist the Networks management through traffic characterization is introduced. For this purpose, three methods belonging to different groups of algorithms are used: the statistical procedure Principal Component Analysis (PCA), the Ant Colony Optimization (ACO) metaheuristic and Holt'Winters forecasting method. These methods characterize the traffic into two distinct levels. The first one is the Network infrastructure, which encompasses the entire Network, including non-healthcare data from different sectors which compose an e-health environment. Profile creation about traffic used for monitoring of patients' vital and behavioral signs identifies the second level. Also, an approach for anomaly detection is proposed, which is able to recognize unusual events that may affect the proper operation of the services provided by the Network.

  • GLOBECOM - A novel anomaly detection system based on seven-dimensional flow analysis
    2013 IEEE Global Communications Conference (GLOBECOM), 2013
    Co-Authors: Marcos V.o. De Assis, J.j.p.c. Rodrigues, Mario Lemes Proenca
    Abstract:

    Anomaly detection in large-scale Networks is not a simple task, although there are several studies in this area. The continuous expansion of computer Networks results in increased complexity of management processes. Thus, simple and efficient anomaly detection mechanisms are required in order to assist the management of these Networks. In this paper, we present an anomaly detection system using a seven-dimensional flow analysis. To accomplish this objective, we used the improved Holt-Winters forecasting method on the traffic characterization of each one of the different analyzed dimensions, here called Digital Signature of Network Segment using Flow analysis (DSNSF). The system not only warns the Network administrator about the problem, but also provides the necessary information to solve it. Real data are collected and used by the system to measure its efficiency and accuracy.

  • Healthcom - Digital Signature of Network Segment using PCA, ACO and Holt-Winters for Network management
    2013 IEEE 15th International Conference on e-Health Networking Applications and Services (Healthcom 2013), 2013
    Co-Authors: Luiz F. Carvalho, Marcos V.o. De Assis, J.j.p.c. Rodrigues, F. Gilberto, Mario Lemes Proença
    Abstract:

    The practicality and convenience provided by computer Networks made them indispensable, which resulted on their continuous growth both in size and complexity. Traffic monitoring became an arduous work, requiring autonomic mechanisms to describe the Network's normal behavior. Thus, we introduce Digital Signature of Network Segment using Flow Analysis (DSNSF) as a mechanism to assist the Networks management through traffic characterization. For this purpose, three methods belonging to different groups of algorithms are used: the statistical procedure Principal Component Analysis (PCA), the Ant Colony Optimization (ACO) metaheuristic and Holt-Winters forecasting method. We use real data for traffic characterization and evaluation of the proposed methods. The results demonstrate a good adaptability of these methods, and that generated DSNSFs can characterize the Network's traffic effectively.

  • Holt-Winters statistical forecasting and ACO metaheuristic for traffic characterization
    2013 IEEE International Conference on Communications (ICC), 2013
    Co-Authors: Marcos V.o. De Assis, J.j.p.c. Rodrigues, Luiz F. Carvalho, Mario Lemes Proenca
    Abstract:

    Due to modernization, expansion of computer Networks has become an inevitable process. However, this growth is also accompanied by increased complexity, which makes it necessary to use resources that assist the management of these Networks. In this paper, we propose a traffic characterization using two-dimensional flow analysis for modeling the behavior traffic pattern, here called Digital Signature of Network Segment Using Flow Analysis (DSNSF). To accomplish this task we have used the improved Holt-Winters forecasting and Ant Colony Optimization metaheuristic methods. The DSNSF obtained by each model are compared to a real traffic of packets and bits and then subjected to specific evaluations in order to measure its accuracy.

  • Digital Signature of Network Segment using PCA, ACO and Holt-Winters for Network management
    2013 IEEE 15th International Conference on e-Health Networking Applications and Services (Healthcom 2013), 2013
    Co-Authors: Luiz F. Carvalho, Marcos V.o. De Assis, J.j.p.c. Rodrigues, F. Gilberto, Mario Lemes Proença
    Abstract:

    The practicality and convenience provided by computer Networks made them indispensable, which resulted on their continuous growth both in size and complexity. Traffic monitoring became an arduous work, requiring autonomic mechanisms to describe the Network's normal behavior. Thus, we introduce Digital Signature of Network Segment using Flow Analysis (DSNSF) as a mechanism to assist the Networks management through traffic characterization. For this purpose, three methods belonging to different groups of algorithms are used: the statistical procedure Principal Component Analysis (PCA), the Ant Colony Optimization (ACO) metaheuristic and Holt-Winters forecasting method. We use real data for traffic characterization and evaluation of the proposed methods. The results demonstrate a good adaptability of these methods, and that generated DSNSFs can characterize the Network's traffic effectively.

J.j.p.c. Rodrigues - One of the best experts on this subject based on the ideXlab platform.

  • Anomaly detection using the correlational paraconsistent machine with digital signatures of Network Segment
    Information Sciences, 2017
    Co-Authors: Eduardo H. M. Pena, J.j.p.c. Rodrigues, Luiz F. Carvalho, Sylvio Barbon, Mario Lemes Proenca
    Abstract:

    Abstract This study presents the correlational paraconsistent machine (CPM), a tool for anomaly detection that incorporates unsupervised models for traffic characterization and principles of paraconsistency, to inspect irregularities at the Network traffic flow level. The CPM is applied for the mathematical foundation of uncertainties that may arise when establishing normal Network traffic behavior profiles, providing means to support the consistency of the information sources chosen for anomaly detection. The experimental results from a real traffic trace evaluation suggest that CPM responses could improve anomaly detection rates.

  • Anomaly detection using digital signature of Network Segment with adaptive ARIMA model and Paraconsistent Logic
    2014 IEEE Symposium on Computers and Communications (ISCC), 2014
    Co-Authors: Eduardo H. M. Pena, J.j.p.c. Rodrigues, Sylvio Barbon, Mario Lemes Proença
    Abstract:

    Detecting anomalies accurately in Network traffic behavior is essential for a variety of Network management and security tasks. This paper presents an anomaly detection approach employing Digital Signature of Network Segment using Flow Analysis (DSNSF), generated with an ARIMA model. Also, a functional algorithm based on a non-classical logic called Paraconsistent Logic is proposed aiming to avoid high false alarms rates. The key idea of the proposed approach is to characterize the normal behavior of Network traffic and then identify the traffic patterns behavior that might harm Networks services. Experimental results on a real Network demonstrate the effectiveness the proposed approach. The results are promising, showing that the flow analysis performed is able to detect anomalous traffic with precision, sensitivity and good performance.

  • ISCC - Anomaly detection using digital signature of Network Segment with adaptive ARIMA model and Paraconsistent Logic
    2014 IEEE Symposium on Computers and Communications (ISCC), 2014
    Co-Authors: Eduardo H. M. Pena, J.j.p.c. Rodrigues, Sylvio Barbon, Mario Lemes Proença
    Abstract:

    Detecting anomalies accurately in Network traffic behavior is essential for a variety of Network management and security tasks. This paper presents an anomaly detection approach employing Digital Signature of Network Segment using Flow Analysis (DSNSF), generated with an ARIMA model. Also, a functional algorithm based on a non-classical logic called Paraconsistent Logic is proposed aiming to avoid high false alarms rates. The key idea of the proposed approach is to characterize the normal behavior of Network traffic and then identify the traffic patterns behavior that might harm Networks services. Experimental results on a real Network demonstrate the effectiveness the proposed approach. The results are promising, showing that the flow analysis performed is able to detect anomalous traffic with precision, sensitivity and good performance.

  • Digital signature of Network Segment for healthcare environments support
    IRBM, 2014
    Co-Authors: L. F. Carvalho, Marcos V.o. De Assis, J.j.p.c. Rodrigues, Gabriela Fernandes, M. Lemes Proença
    Abstract:

    Network technologies have facilitated the implementation of health services based on ubiquitous systems, allowing pervasive monitoring of patients in their daily activities without significantly interfering in their lifestyle. This event entails the need to ensure adequate management and security of healthcare environment Networks. However, traffic monitoring became an arduous work, requiring autonomic mechanisms to describe the Network's normal behavior. Thus, Digital Signature of Network Segment using Flow analysis(DSNSF) as a mechanism to assist the Networks management through traffic characterization is introduced. For this purpose, three methods belonging to different groups of algorithms are used: the statistical procedure Principal Component Analysis (PCA), the Ant Colony Optimization (ACO) metaheuristic and Holt'Winters forecasting method. These methods characterize the traffic into two distinct levels. The first one is the Network infrastructure, which encompasses the entire Network, including non-healthcare data from different sectors which compose an e-health environment. Profile creation about traffic used for monitoring of patients' vital and behavioral signs identifies the second level. Also, an approach for anomaly detection is proposed, which is able to recognize unusual events that may affect the proper operation of the services provided by the Network.

  • GLOBECOM - A novel anomaly detection system based on seven-dimensional flow analysis
    2013 IEEE Global Communications Conference (GLOBECOM), 2013
    Co-Authors: Marcos V.o. De Assis, J.j.p.c. Rodrigues, Mario Lemes Proenca
    Abstract:

    Anomaly detection in large-scale Networks is not a simple task, although there are several studies in this area. The continuous expansion of computer Networks results in increased complexity of management processes. Thus, simple and efficient anomaly detection mechanisms are required in order to assist the management of these Networks. In this paper, we present an anomaly detection system using a seven-dimensional flow analysis. To accomplish this objective, we used the improved Holt-Winters forecasting method on the traffic characterization of each one of the different analyzed dimensions, here called Digital Signature of Network Segment using Flow analysis (DSNSF). The system not only warns the Network administrator about the problem, but also provides the necessary information to solve it. Real data are collected and used by the system to measure its efficiency and accuracy.

Huaibin Wang - One of the best experts on this subject based on the ideXlab platform.

  • BMEI - A Distributed Intrusion Detection System Based on Mobile Agents
    2009 2nd International Conference on Biomedical Engineering and Informatics, 2009
    Co-Authors: Xiu-liang Mo, Chundong Wang, Huaibin Wang
    Abstract:

    In this paper, the tool "sniffer" is introduced and controlled as a sensor by the IDS via mobile agents; these agents gather intrusion detection data and send them back to the server for analysis. We propose a distributed intrusion detection system (DIDS) which detects intrusion from outside the Network Segment as well as from inside using mobile agents. The proposed model consists of three major components: Intrusion Detection Component, Mobile Agent Environment, Data Analysis Component and distributed sensors residing on every device in the Network Segment. Compared with traditional central sniffing IDS techniques, the system shows superior performances and saves Network resources.

  • ICNC (6) - An Improved RED Congestion Algorithm Based on Partition of Network Segment
    2009 Fifth International Conference on Natural Computation, 2009
    Co-Authors: Chundong Wang, Ting Li, Huaibin Wang
    Abstract:

    Aiming at the growing problem of Network congestion, an improved random early detection algorithm is proposed for the internal Network and applied on a TCP congestion control algorithm based on partition of Network Segment. By using known Network information and Network topology information, the method is more effective, and better results are achieved.

  • An Improved RED Congestion Algorithm Based on Partition of Network Segment
    2009 Fifth International Conference on Natural Computation, 2009
    Co-Authors: Chundong Wang, Ting Li, Huaibin Wang
    Abstract:

    Aiming at the growing problem of Network congestion, an improved random early detection algorithm is proposed for the internal Network and applied on a TCP congestion control algorithm based on partition of Network Segment. By using known Network information and Network topology information, the method is more effective, and better results are achieved.