Advances in Computational Intelligence [electronic resource] : 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, Guadalajara, Mexico, October 22–27, 2018, Proceedings, Part II /

The two-volume set LNAI 11288 and 11289 constitutes the proceedings of the 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, held in Guadalajara, Mexico, in October 2018. The total of 62 papers presented in these two volumes was carefully reviewed and selected from 149 submissions. The contributions are organized in topical as follows: Part I: evolutionary and nature-inspired intelligence; machine learning; fuzzy logic and uncertainty management. Part II: knowledge representation, reasoning, and optimization; natural language processing; and robotics and computer vision.Knowledge Representation, Reasoning, and Optimization -- Coding 3D connected regions with F26 chain code -- Finding optimal farming practices to increase crop yield through Global-best Harmony Search and predictive models, a data-driven approach -- On the Modelling of the Energy System of a Country for Decision Making Using Bayesian Artificial Intelligence { A case study for Mexico -- Natural Language Processing -- Enhancement of performance of document clustering in the authorship identification problem with a weighted cosine similarity -- Exploring the Context of Lexical Functions -- Towards a Natural Language Compiler -- Comparative analysis and implementation of semantic-based classifiers -- Best Paper award, second place: Topic-Focus Articulation: A Third Pillar of Automatic Evaluation of Text Coherence -- A Multilingual Study of Compressive Cross-Language Text Summarization -- WiSeBE: Window-based Sentence Boundary Evaluation -- Readability Formula for Russian Texts: a Modified Version -- Timed automaton RVT-grammar for workflow translating -- Extraction of Typical Client Requests from Bank Chat Logs -- A Knowledge-based Methodology for Building a Conversational Chatbot as an Intelligent Tutor -- Top-k Context-Aware Tour Recommendations for Groups -- A Knowledge-based Weighted kKNN for Detecting Irony in Twitter -- Model for Personality Detection based on Text Analysis -- Analysis of Emotions through Speech Using the Combination of Multiple Input Sources with Deep Convolutional and LSTM Networks -- Robustness of LSTM Neural Networks for the Enhancement of Spectral Parameters in Noisy Speech Signals -- Tensor Decomposition for Imagined Speech Discrimination in EEG -- Robotics and Computer Vision -- A new software library for mobile sensing using FIWARE technologies -- Free model task space controller based on adaptive gain for robot manipulator using Jacobian estimation -- Design and Equilibrium Control of a Force-Balanced One-Leg Mechanism -- An Adaptive Robotic Assistance Platform for Neurorehabilitation Therapy of Upper Limb -- ROBMMOR: An experimental robotic manipulator for motor rehabilitation of knee -- A Bio-inspired Cybersecurity Scheme to Protect a Swarm of Robots -- Chaos optimization applied to a beamforming algorithm for source location -- Data Augmentation in Deep Learning-based Obstacle Detection System for Autonomous Navigation on Aquatic Surfaces -- Best Paper award, third place: Combining Deep Learning and RGBD SLAM for Monocular Indoor Autonomous Flight.The two-volume set LNAI 11288 and 11289 constitutes the proceedings of the 17th Mexican International Conference on Artificial Intelligence, MICAI 2018, held in Guadalajara, Mexico, in October 2018. The total of 62 papers presented in these two volumes was carefully reviewed and selected from 149 submissions. The contributions are organized in topical as follows: Part I: evolutionary and nature-inspired intelligence; machine learning; fuzzy logic and uncertainty management. Part II: knowledge representation, reasoning, and optimization; natural language processing; and robotics and computer vision

Machine Learning Based Detection and Evasion Techniques for Advanced Web Bots.

Web bots are programs that can be used to browse the web and perform different types of automated actions, both benign and malicious. Such web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour which reduce their detectability. Several effective behaviour-based web bot detection techniques have been pro- posed in literature. However, the performance of these detection techniques when target- ing malicious web bots that try to evade detection has not been examined in depth. Such evasive web bot behaviour is achieved by different techniques, including simple heuris- tics and statistical distributions, or more advanced machine learning based techniques. Motivated by the above, in this thesis we research novel web bot detection techniques and how effective these are against evasive web bots that try to evade detection using, among others, recent advances in machine learning. To this end, we initially evaluate state-of-the-art web bot detection techniques against web bots of different sophistication levels and show that, while the existing approaches achieve very high performance in general, such approaches are not very effective when faced with only advanced web bots that try to remain undetected. Thus, we propose a novel web bot detection framework that can be used to detect effectively bots of varying levels of sophistication, including advanced web bots. This framework comprises and combines two detection modules: (i) a detection module that extracts several features from web logs and uses them as input to several well-known machine learning algo- rithms, and (ii) a detection module that uses mouse trajectories as input to Convolutional Neural Networks (CNNs). Moreover, we examine the case where advanced web bots utilise themselves the re- cent advances in machine learning to evade detection. Specifically, we propose two novel evasive advanced web bot types: (i) the web bots that use Reinforcement Learning (RL) to update their browsing behaviour based on whether they have been detected or not, and (ii) the web bots that have in their possession several data from human behaviours and use them as input to Generative Adversarial Networks (GANs) to generate images of humanlike mouse trajectories. We show that both approaches increase the evasiveness of the web bots by reducing the performance of the detection framework utilised in each case. We conclude that malicious web bots can exhibit high sophistication levels and com- bine different techniques that increase their evasiveness. Even though web bot detection frameworks can combine different methods to effectively detect such bots, web bots can update their behaviours using, among other, recent advances in machine learning to in- crease their evasiveness. Thus, the detection techniques should be continuously updated to keep up with new techniques introduced by malicious web bots to evade detection

Anales del XIII Congreso Argentino de Ciencias de la Computación (CACIC)

Contenido: Arquitecturas de computadoras Sistemas embebidos Arquitecturas orientadas a servicios (SOA) Redes de comunicaciones Redes heterogéneas Redes de Avanzada Redes inalámbricas Redes móviles Redes activas Administración y monitoreo de redes y servicios Calidad de Servicio (QoS, SLAs) Seguridad informática y autenticación, privacidad Infraestructura para firma digital y certificados digitales Análisis y detección de vulnerabilidades Sistemas operativos Sistemas P2P Middleware Infraestructura para grid Servicios de integración (Web Services o .Net)Red de Universidades con Carreras en Informática (RedUNCI