Thiago Quirino

Learn More
Recently, network security has become an extremely vital issue that beckons the development of accurate and efficient solutions capable of effectively defending our network systems and the valuable information journeying through them. In this article, a distributed multiagent intrusion detection system (IDS) architecture is proposed, which attempts to(More)
The behavior of the genetic algorithm (GA), a popular approach to search and optimization problems, is known to depend, among other factors, on the fitness function formula, the recombination operator, and the mutation operator. What has received less attention is the impact of the mating strategy that selects the chromosomes to be paired for recombination.(More)
The development of effective classification techniques, particularly unsupervised classification, is important for real-world applications since information about the training data before classification is relatively unknown. In this paper, a novel unsupervised classification algorithm is proposed to meet the increasing demand in the domain of network(More)
In this paper, a novel supervised classification approach called collateral representative subspace projection modeling (C-RSPM) is presented. C-RSPM facilitates schemes for collateral class modeling, class-ambiguity solving, and classification, resulting a multi-class supervised classifier with high detection rate and various operational benefits including(More)
In this paper, a novel agent-based distributed intrusion detection system (IDS) is proposed, which integrates the desirable features provided by the distributed agent-based design methodology with the high accuracy and speed response of the principal component classifier (PCC). Experimental results have shown that the PCC lightweight anomaly detection(More)
In this paper, a supervised multi-class classification approach called Adaptive Selection of Information Components (ASIC) is presented. ASIC has the facilities to (i) handle both numerical and nominal features in a data set, (ii) pre-process the training data set to accentuate the spatial differences among the classes in the training data set to reduce(More)
Forecasting tropical cyclone (TC) intensification remains difficult despite research efforts to improve numerical weather prediction models. This study aims to examine Hurricane Earl’s rapid intensification in order to gain a better understanding of the observed and modeled intensification process. 113 dropwindsondes were analyzed before, during, and after(More)
  • 1