The Experts below are selected from a list of 179967 Experts worldwide ranked by ideXlab platform
Joao Paulo Papa - One of the best experts on this subject based on the ideXlab platform.
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a nature inspired approach to speed up optimum path forest clustering and its application to intrusion detection in Computer Networks
Information Sciences, 2015Co-Authors: Kelton A P Costa, Luis A M Pereira, Rodrigo Y M Nakamura, Clayton R Pereira, Joao Paulo Papa, Alexandre X FalcaoAbstract:A new meta-heuristic optimization approach to speed up Optimum-Path Forest clustering.Intrusion detection in Computer Networks by means of Optimum-Path Forest clustering.Comparison of several meta-heuristics for Optimum-Path Forest optimization. We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in Computer Networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k.
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an optimum path forest framework for intrusion detection in Computer Networks
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Clayton R Pereira, Kelton A P Costa, Rodrigo Y M Nakamura, Joao Paulo PapaAbstract:Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In order to overcome such limitations, we have introduced a new pattern recognition technique called optimum-path forest (OPF) to this task. Our proposal is composed of three main contributions: to apply OPF for intrusion detection, to identify redundancy in some public datasets and also to perform feature selection over them. The experiments have been carried out on three datasets aiming to compare OPF against Support Vector Machines, Self Organizing Maps and a Bayesian classifier. We have showed that OPF has been the fastest classifier and the always one with the top results. Thus, it can be a suitable tool to detect intrusions on Computer Networks, as well as to allow the algorithm to learn new attacks faster than other techniques.
Clayton R Pereira - One of the best experts on this subject based on the ideXlab platform.
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a nature inspired approach to speed up optimum path forest clustering and its application to intrusion detection in Computer Networks
Information Sciences, 2015Co-Authors: Kelton A P Costa, Luis A M Pereira, Rodrigo Y M Nakamura, Clayton R Pereira, Joao Paulo Papa, Alexandre X FalcaoAbstract:A new meta-heuristic optimization approach to speed up Optimum-Path Forest clustering.Intrusion detection in Computer Networks by means of Optimum-Path Forest clustering.Comparison of several meta-heuristics for Optimum-Path Forest optimization. We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in Computer Networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k.
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an optimum path forest framework for intrusion detection in Computer Networks
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Clayton R Pereira, Kelton A P Costa, Rodrigo Y M Nakamura, Joao Paulo PapaAbstract:Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In order to overcome such limitations, we have introduced a new pattern recognition technique called optimum-path forest (OPF) to this task. Our proposal is composed of three main contributions: to apply OPF for intrusion detection, to identify redundancy in some public datasets and also to perform feature selection over them. The experiments have been carried out on three datasets aiming to compare OPF against Support Vector Machines, Self Organizing Maps and a Bayesian classifier. We have showed that OPF has been the fastest classifier and the always one with the top results. Thus, it can be a suitable tool to detect intrusions on Computer Networks, as well as to allow the algorithm to learn new attacks faster than other techniques.
Kelton A P Costa - One of the best experts on this subject based on the ideXlab platform.
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a nature inspired approach to speed up optimum path forest clustering and its application to intrusion detection in Computer Networks
Information Sciences, 2015Co-Authors: Kelton A P Costa, Luis A M Pereira, Rodrigo Y M Nakamura, Clayton R Pereira, Joao Paulo Papa, Alexandre X FalcaoAbstract:A new meta-heuristic optimization approach to speed up Optimum-Path Forest clustering.Intrusion detection in Computer Networks by means of Optimum-Path Forest clustering.Comparison of several meta-heuristics for Optimum-Path Forest optimization. We propose a nature-inspired approach to estimate the probability density function (pdf) used for data clustering based on the optimum-path forest algorithm (OPFC). OPFC interprets a dataset as a graph, whose nodes are the samples and each sample is connected to its k-nearest neighbors in a given feature space (a k-nn graph). The nodes of the graph are weighted by their pdf values and the pdf is computed based on the distances between the samples and their k-nearest neighbors. Once the k-nn graph is defined, OPFC finds one sample (root) at each maximum of the pdf and propagates one optimum-path tree (cluster) from each root to the remaining samples of its dome. Clustering effectiveness will depend on the pdf estimation, and the proposed approach efficiently computes the best value of k for a given application. We validate our approach in the context of intrusion detection in Computer Networks. First, we compare OPFC with data clustering based on k-means, and self-organization maps. Second, we evaluate several metaheuristic techniques to find the best value of k.
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an optimum path forest framework for intrusion detection in Computer Networks
Engineering Applications of Artificial Intelligence, 2012Co-Authors: Clayton R Pereira, Kelton A P Costa, Rodrigo Y M Nakamura, Joao Paulo PapaAbstract:Intrusion detection systems that make use of artificial intelligence techniques in order to improve effectiveness have been actively pursued in the last decade. However, their complexity to learn new attacks has become very expensive, making them inviable for a real time retraining. In order to overcome such limitations, we have introduced a new pattern recognition technique called optimum-path forest (OPF) to this task. Our proposal is composed of three main contributions: to apply OPF for intrusion detection, to identify redundancy in some public datasets and also to perform feature selection over them. The experiments have been carried out on three datasets aiming to compare OPF against Support Vector Machines, Self Organizing Maps and a Bayesian classifier. We have showed that OPF has been the fastest classifier and the always one with the top results. Thus, it can be a suitable tool to detect intrusions on Computer Networks, as well as to allow the algorithm to learn new attacks faster than other techniques.
Faouzi Kamoun - One of the best experts on this subject based on the ideXlab platform.
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neural Networks for shortest path computation and routing in Computer Networks
IEEE Transactions on Neural Networks, 1993Co-Authors: Mehmet M Ali, Faouzi KamounAbstract:The application of neural Networks to the optimum routing problem in packet-switched Computer Networks, where the goal is to minimize the network-wide average time delay, is addressed. Under appropriate assumptions, the optimum routing algorithm relies heavily on shortest path computations that have to be carried out in real time. For this purpose an efficient neural network shortest path algorithm that is an improved version of previously suggested Hopfield models is proposed. The general principles involved in the design of the proposed neural network are discussed in detail. Its computational power is demonstrated through Computer simulations. One of the main features of the proposed model is that it will enable the routing algorithm to be implemented in real time and also to be adaptive to changes in link costs and network topology. >
Mandeep Pannu - One of the best experts on this subject based on the ideXlab platform.
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network intrusion detection systems in high speed traffic in Computer Networks
International Conference on e-Business Engineering, 2013Co-Authors: Waleed Bulajoul, Anne James, Mandeep PannuAbstract:With the various and increasingly malicious attacks on Networks and wireless systems, traditional security tools such as anti-virus programs and firewalls are not sufficient to provide free, integrated, reliable and secure Networks. Intrusion detection systems (IDSs) are one of the most tested and reliable technologies to monitor incoming and outgoing network traffic to identify unauthorized usage and mishandling of Computer system Networks. It is critical to implement network intrusion detection systems (NIDSs) in Computer Networks that have high traffic and high-speed connectivity. Due to the fact that software NIDSs are still unable to detect all the growing threats to high-speed environments, such as flood attacks (UDP, TCP, ICMP and HTTP) or Denial and Distributed Denial of Service Attacks (DoS/DDoS), because the main function of these kinds of attacks is simply to send more traffic in high speed to systems to stop or slow down the performance of systems. Here we have designed a suitable real network to present experiments that use Snort NIDSs to demonstrate the weaknesses of NIDSs, such as its inability to process multiple packets at high speeds and its propensity to drop packets without analysing them. This paper outlines Snort NIDSs' failures in high-speed and heavy traffic and its propensity to drop more packets as the speed and volume of traffic increase. We ran some consecutive tests to analyse the Snort performance using the number of packets received, the number of packets analysed, the number of packets filtered and the number of packets dropped. We suggest a parallel NIDS technology to reduce dropping packets as a solution.
