The Experts below are selected from a list of 542310 Experts worldwide ranked by ideXlab platform
Mario Lemes Proenca - One of the best experts on this subject based on the ideXlab platform.
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Network anomaly detection using ip flows with principal component analysis and ant colony optimization
Journal of Network and Computer Applications, 2016Co-Authors: Gilberto Fernandes, Luiz F Carvalho, Joel J P C Rodrigues, Mario Lemes ProencaAbstract:It is remarkable how proactive Network management is in such demand nowadays, since Networks are growing in size and complexity and Information Technology services cannot be stopped. In this manner, it is necessary to use an approach which proactively identifies traffic behavior patterns which may harm the Network's normal operations. Aiming an automated management to detect and prevent potential problems, we present and compare two novel anomaly detection mechanisms based on statistical procedure Principal Component Analysis and the Ant Colony Optimization metaheuristic. These methods generate a traffic profile, called Digital Signature of Network Segment using Flow analysis (DSNSF), which is adopted as normal Network behavior. Then, this signature is compared with the Real Network traffic by using a modification of the Dynamic Time Warping metric in order to recognize anomalous events. Thus, a seven-dimensional analysis of IP flows is performed, allowing the characterization of bits, packets and flows traffic transmitted per second, and the extraction of descriptive flow attributes, like source IP address, destination IP address, source TCP/UDP port and destination TCP/UDP port. The systems were evaluated using a Real Network environment and showed promising results. Moreover, the correspondence between true-positive and false-positive rates demonstrates that the systems are able to enhance the detection of anomalous behavior by maintaining a satisfactory false-alarm rate. Display Omitted Anomaly detection issue is addressed based on Network traffic profiling.Proposal and comparison of detection methods belonging to distinct algorithm classes.Detection mechanism constructed over an adaptation of a pattern matching technique.Use of Real and simulated traffic to evaluate the proposed methods.Traffic patterns that may harm the Network operations are proactively identified.
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anomaly detection aiming pro active management of computer Network based on digital signature of Network segment
Journal of Network and Systems Management, 2007Co-Authors: Bruno Bogaz Zarpelão, Leonardo De Souza Mendes, Mario Lemes ProencaAbstract:Detecting anomalies accurately is fundamental to rapid diagnosis and repair of problems. This paper proposes a novel Anomaly detection system based on the comparison of Real traffic and DSNS (Digital Signature of Network Segment), generated by BLGBA (Baseline for Automatic Backbone Management) model, within a hysteresis interval using the residual mean and on the correlation of the detected deviations. Extensive experimental results on Real Network servers confirmed that our system is able to detect anomalies on the monitored devices, avoiding the high false alarms rate.
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the hurst parameter for digital signature of Network segment
International Conference on Telecommunications, 2004Co-Authors: Mario Lemes Proenca, Camiel Coppelmans, Mauricio Luis Bottoli, Antonio Marcos Alberti, Leonardo De Souza MendesAbstract:This paper presents results of the Hurst parameter for digital signature of Network segments. It’s also presented a model for digital signature automatic generation which aims at the characterization of traffic in Network segments. The use of the digital signature allows the manager to: identify limitations and crucial points of the Network; establish the Real use of Network resources; better control the use of resources and the establishment of thresholds for the generation of more accurate and intelligent alarms which suit the Real Network characteristics. The obtained results validate the experiment and show in practice significant advantages in Networks management.
Jaideep Srivastava - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of anomaly detection schemes in Network intrusion detection
SIAM International Conference on Data Mining, 2003Co-Authors: Aleksandar Lazarevic, Vipin Kumar, Levent Ertoz, Aysel Ozgur, Jaideep SrivastavaAbstract:Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and Network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different Network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of Network connections [9] as well as on Real Network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA’98 data and Real Network data.
Richard M Leahy - One of the best experts on this subject based on the ideXlab platform.
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statistically optimal graph partition method based on modularity
International Symposium on Biomedical Imaging, 2010Co-Authors: Yuteng Chang, Dimitrios Pantazis, Hua Brian Hui, Richard M LeahyAbstract:Graph theory provides a formal framework to investigate the functional and structural connectome of the brain. We extend previous work on modularity-based graph partitioning methods that are able to detect Network community structures. We estimate the conditional expected Network, provide exact analytical solutions for a Gaussian random Network, and also demonstrate that this Network is the best unbiased linear estimator even when the Gaussian assumption is violated. We use the conditional expected Network to partition graphs, and demonstrate its performance in simulations, a Real Network dataset, and a structural brain connectivity Network.
Aleksandar Lazarevic - One of the best experts on this subject based on the ideXlab platform.
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a comparative study of anomaly detection schemes in Network intrusion detection
SIAM International Conference on Data Mining, 2003Co-Authors: Aleksandar Lazarevic, Vipin Kumar, Levent Ertoz, Aysel Ozgur, Jaideep SrivastavaAbstract:Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and Network infrastructures. Anomaly detection is a key element of intrusion detection in which perturbations of normal behavior suggest the presence of intentionally or unintentionally induced attacks, faults, defects, etc. This paper focuses on a detailed comparative study of several anomaly detection schemes for identifying different Network intrusions. Several existing supervised and unsupervised anomaly detection schemes and their variations are evaluated on the DARPA 1998 data set of Network connections [9] as well as on Real Network data using existing standard evaluation techniques as well as using several specific metrics that are appropriate when detecting attacks that involve a large number of connections. Our experimental results indicate that some anomaly detection schemes appear very promising when detecting novel intrusions in both DARPA’98 data and Real Network data.
Hiroaki Nishi - One of the best experts on this subject based on the ideXlab platform.
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service oriented router based cdn system an sor based cdn infrastructure implementation on a Real Network environment
Computer Software and Applications Conference, 2013Co-Authors: Janaka Wijekoon, Shinichi Ishida, Erwin Harahap, Hiroaki NishiAbstract:Internet users are constantly demanding faster and higher quality services from their internet service providers. Therefore, for fast data delivery of such applications, Content Delivery Networks (CDNs) have been introduced. Most CDN providers use Domain Name Resolution (DNS) based request routing (RR) methods to find the nearest server for a particular client and it has both advantages and disadvantages. Importantly, disadvantages result high latencies of data delivery and Network congestions. To maintain rich information in the Internet and to shift the current Internet infrastructure to an information-based open environment platform, Service-oriented Routers (SoRs) have been introduced. An SoR has a high-throughput database and it is able to analyze all transactions on its interfaces. Therefore, we have used the basic functionalities of the SoR to diminish disadvantages of the DNS-based RR methods. Proposed system is independent from DNS-based RR and we have conducted experiments based on content-centric RR using the SoR basic functionalities and successfully evaluated and compared both of the round trip time (RTT) and the packet inter arrival time. Our results indicated that SoR-based method can reduce upto 40-50% latency in both connection initiation time and content migration time in-between servers.
