Examinando por Autor "Torres, Romina"

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  • Miniatura
    Ítem
    Air quality assessment and pollution forecasting using artifcial neural networks in Metropolitan Lima‐Peru
    (Springer, 2021) Hoyos Cordova, Chardin; Lopez Portocarrero, Manuel Niño; Salas, Rodrigo; Torres, Romina; Canas Rodrigues, Paulo; López‐Gonzales, Javier Linkolk
    The prediction of air pollution is of great importance in highly populated areas because it directly impacts both the management of the city’s economic activity and the health of its inhabitants. This work evaluates and predicts the Spatio-temporal behavior of air quality in Metropolitan Lima, Peru, using artificial neural networks. The conventional feedforward backpropagation known as Multilayer Perceptron (MLP) and the Recurrent Artificial Neural network known as Long Short-Term Memory networks (LSTM) were implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The models were validated using two schemes: The Hold-Out and the Blocked-Nested Cross-Validation (BNCV). The simulation results show that periods of moderate PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the performance of both models, the MLP and LSTM, were diminished. On the other hand, the prediction performance improved slightly when the models were trained and validated with the BNCV scheme. The simulation results showed that the models obtained a good performance for the CDM, CRB, and SMP monitoring stations, characterized by a moderate to low level of contamination. However, the results show the difficulty of predicting this contaminant in those stations that present critical contamination episodes, such as ATE and HCH. In conclusion, the LSTM recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better predictability performance for this type of environmental data.
  • Miniatura
    Ítem
    Machine Learning techniques for Behavioral Feature Selection in Network Intrusion Detection Systems
    (IEEE, 2021) Martinez, Vicente; Salas, Rodrigo; Tessini, Oliver; Torres, Romina
    Information systems are prone to receiving multiple types of attacks over the network. Therefore, Network Intrusion Detection Systems (NIDSs) analyze the behavior of the network traffic to detect anomalies and eventual cyberattacks. The NIDS must be able to detect these cyberattacks in an efficient and effective manner based on a set of features where it is expected that the performance depends on both the selected features and the machine learning technique used. The main goal of this work is to identify the most relevant characteristics required to detect, with a high sensitivity and precision, between normal traffic and a network intrusion, together with the most relevant features associated to the identification of a specific type of attack. In this work, a comparative study of different decision tree-based machine learning techniques combined with several feature selection techniques in order to accomplish the goal. Random Forest and the XGBoost achieved a performance that reaches up to 98.5% in the F-measure when the complete set of features were used. Results show the performance was just slightly reduced to 98% when the 10 most relevant features were used. Moreover, results also show that the model using only the 10 most relevant features was able to separately identify the type of attack with a performance of at least 90% in the F-measure. We conclude that it is possible to obtain and rank a subset of the most relevant features that characterize the intrusion pattern in the network traffic in order to support the decision of how many features to include during runtime under a real network environment.
  • Miniatura
    Ítem
    Taxonomies using the clique percolation method for building a threats observatory
    (IEEE, 2021) Torres, Romina; González, Nicolás; Cabrera, Mathías; Salas, Rodrigo
    Cyberattacks are increasing every day, demanding that security incident response teams proactively determine potential threats early. Although social networks such as Twitter are a rich and up-to-date source of information where users use to tweet about different topics, it is complex to efficiently and effectively obtain results that support decision-making on a specific subject, such as cyberattacks. Therefore, in this work, we propose to use an offline mining process based on the clique percolation method over a corpus of tweets in order to generate an indexed knowledge base about cyberattacks. Results are promising to observe threats under evolution. Then, to show results properly, we generate an observatory prototype to allow cybersecurity researchers to explore threats over time and space.