Development of security mechanisms for a multi-agent cyber-physical conveyor system using machine learning

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáOne main foundation of the Industry 4.0 is the connectivity of devices and systems using Internet of Things technologies, where Cyber-physical systems (CPS) act as the backbone infrastructure based on distributed and decentralized structures. CPS requires the use of Artificial Intelligence (AI) techniques, such as Multi-Agent Systems (MAS), allowing the incorporation of intelligence into the CPS through autonomous, proactive and cooperative entities. The adoption of this new generation of systems in the industrial environment opens new doors for various attacks that can cause serious damage to industrial production systems. This work presents the development of security mechanisms for systems based on MAS, where these mechanisms are used in an experimental case study that consists of a multiagent cyber-physical conveyor system. For this purpose, simple security mechanisms were employed in the system, such as user authentication, signature and message encryption, as well as other more complex mechanisms, such as machine learning techniques that allows the agents to be more intelligent in relation to the exchange of messages protecting the system against attacks, through the classification of the messages as reliable or not, and also an intrusion detection system was carried out. Based on the obtained results, the efficient protection of the system was reached, mitigating the main attack vectors present in the system architecture.Uma das principais bases da Indústria 4.0 é a conectividade de dispositivos e sistemas utilizando as tecnologias da Internet das Coisas, onde os sistemas ciber-físicos atuam como a infraestrutura principal com base em estruturas distribuídas e descentralizadas. Os sistemas ciber-físicos requerem o uso de técnicas de Inteligência Artificial, como por exemplo, Sistemas Multi-Agentes, permitindo a incorporação de inteligência nos sistemas ciber-físicos através de entidades autônomas, proativas e cooperativas. A adoção dessa nova geração de sistemas no ambiente industrial abre novas portas para vários ataques que podem causar sérios danos aos sistemas de produção industrial. Este trabalho apresenta o desenvolvimento de mecanismos de segurança para sistemas baseados em sistemas multi-agentes, em que esses mecanismos são utilizados em um caso de estudo experimental que consiste em um sistema de transporte ciber-físico baseado em sistemas multi-agentes. Para isso, mecanismos simples de segurança foram empregados no sistema, como autenticação do usuário, assinatura e criptografia de mensagens, além de outros mecanismos mais complexos, como técnicas de aprendizagem de máquina, que permite que os agentes sejam mais inteligentes em relação à troca de mensagens, protegendo o sistema contra ataques, através da classificação das mensagens como confiáveis ou não, e também foi realizado um sistema de detecção de intrusões. Com base nos resultados obtidos, obteve-se uma proteção eficiente do sistema, mitigando os principais vetores de ataque presentes na arquitetura do sistema

Model-driven and Data-driven Approaches for some Object Recognition Problems

Recognizing objects from images and videos has been a long standing problem in computer vision. The recent surge in the prevalence of visual cameras has given rise to two main challenges where, (i) it is important to understand different sources of object variations in more unconstrained scenarios, and (ii) rather than describing an object in isolation, efficient learning methods for modeling object-scene `contextual' relations are required to resolve visual ambiguities. This dissertation addresses some aspects of these challenges, and consists of two parts. First part of the work focuses on obtaining object descriptors that are largely preserved across certain sources of variations, by utilizing models for image formation and local image features. Given a single instance of an object, we investigate the following three problems. (i) Representing a 2D projection of a 3D non-planar shape invariant to articulations, when there are no self-occlusions. We propose an articulation invariant distance that is preserved across piece-wise affine transformations of a non-rigid object `parts', under a weak perspective imaging model, and then obtain a shape context-like descriptor to perform recognition; (ii) Understanding the space of `arbitrary' blurred images of an object, by representing an unknown blur kernel of a known maximum size using a complete set of orthonormal basis functions spanning that space, and showing that subspaces resulting from convolving a clean object and its blurred versions with these basis functions are equal under some assumptions. We then view the invariant subspaces as points on a Grassmann manifold, and use statistical tools that account for the underlying non-Euclidean nature of the space of these invariants to perform recognition across blur; (iii) Analyzing the robustness of local feature descriptors to different illumination conditions. We perform an empirical study of these descriptors for the problem of face recognition under lighting change, and show that the direction of image gradient largely preserves object properties across varying lighting conditions. The second part of the dissertation utilizes information conveyed by large quantity of data to learn contextual information shared by an object (or an entity) with its surroundings. (i) We first consider a supervised two-class problem of detecting lane markings from road video sequences, where we learn relevant feature-level contextual information through a machine learning algorithm based on boosting. We then focus on unsupervised object classification scenarios where, (ii) we perform clustering using maximum margin principles, by deriving some basic properties on the affinity of `a pair of points' belonging to the same cluster using the information conveyed by `all' points in the system, and (iii) then consider correspondence-free adaptation of statistical classifiers across domain shifting transformations, by generating meaningful `intermediate domains' that incrementally convey potential information about the domain change

Game Theory Solutions in Sensor-Based Human Activity Recognition: A Review

The Human Activity Recognition (HAR) tasks automatically identify human activities using the sensor data, which has numerous applications in healthcare, sports, security, and human-computer interaction. Despite significant advances in HAR, critical challenges still exist. Game theory has emerged as a promising solution to address these challenges in machine learning problems including HAR. However, there is a lack of research work on applying game theory solutions to the HAR problems. This review paper explores the potential of game theory as a solution for HAR tasks, and bridges the gap between game theory and HAR research work by suggesting novel game-theoretic approaches for HAR problems. The contributions of this work include exploring how game theory can improve the accuracy and robustness of HAR models, investigating how game-theoretic concepts can optimize recognition algorithms, and discussing the game-theoretic approaches against the existing HAR methods. The objective is to provide insights into the potential of game theory as a solution for sensor-based HAR, and contribute to develop a more accurate and efficient recognition system in the future research directions

A computational academic integrity framework

L'abast creixent i la naturalesa canviant dels programes acadèmics constitueixen un repte per a la integritat dels protocols tradicionals de proves i exàmens. L'objectiu d¿aquesta tesi és introduir una alternativa als enfocaments tradicionals d'integritat acadèmica, per a cobrir la bretxa del buit de l'anonimat i donar la possibilitat als instructors i administradors acadèmics de fer servir nous mitjans que permetin mantenir la integritat acadèmica i promoguin la responsabilitat, accessibilitat i eficiència, a més de preservar la privadesa i minimitzin la interrupció en el procés d'aprenentatge. Aquest treball té com a objectiu començar un canvi de paradigma en les pràctiques d'integritat acadèmica. La recerca en l'àrea de la identitat de l'estudiant i la garantia de l'autoria són importants perquè la concessió de crèdits d'estudi a entitats no verificades és perjudicial per a la credibilitat institucional i la seguretat pública. Aquesta tesi es basa en la noció que la identitat de l'alumne es compon de dues capes diferents, física i de comportament, en les quals tant els criteris d'identitat com els d'autoria han de ser confirmats per a mantenir un nivell raonable d'integritat acadèmica. Per a això, aquesta tesi s'organitza en tres seccions, cadascuna de les quals aborda el problema des d'una de les perspectives següents: (a) teòrica, (b) empírica i (c) pragmàtica.El creciente alcance y la naturaleza cambiante de los programas académicos constituyen un reto para la integridad de los protocolos tradicionales de pruebas y exámenes. El objetivo de esta tesis es introducir una alternativa a los enfoques tradicionales de integridad académica, para cubrir la brecha del vacío anonimato y dar la posibilidad a los instructores y administradores académicos de usar nuevos medios que permitan mantener la integridad académica y promuevan la responsabilidad, accesibilidad y eficiencia, además de preservar la privacidad y minimizar la interrupción en el proceso de aprendizaje. Este trabajo tiene como objetivo iniciar un cambio de paradigma en las prácticas de integridad académica. La investigación en el área de la identidad del estudiante y la garantía de la autoría son importantes porque la concesión de créditos de estudio a entidades no verificadas es perjudicial para la credibilidad institucional y la seguridad pública. Esta tesis se basa en la noción de que la identidad del alumno se compone de dos capas distintas, física y de comportamiento, en las que tanto los criterios de identidad como los de autoría deben ser confirmados para mantener un nivel razonable de integridad académica. Para ello, esta tesis se organiza en tres secciones, cada una de las cuales aborda el problema desde una de las siguientes perspectivas: (a) teórica, (b) empírica y (c) pragmática.The growing scope and changing nature of academic programmes provide a challenge to the integrity of traditional testing and examination protocols. The aim of this thesis is to introduce an alternative to the traditional approaches to academic integrity, bridging the anonymity gap and empowering instructors and academic administrators with new ways of maintaining academic integrity that preserve privacy, minimize disruption to the learning process, and promote accountability, accessibility and efficiency. This work aims to initiate a paradigm shift in academic integrity practices. Research in the area of learner identity and authorship assurance is important because the award of course credits to unverified entities is detrimental to institutional credibility and public safety. This thesis builds upon the notion of learner identity consisting of two distinct layers (a physical layer and a behavioural layer), where the criteria of identity and authorship must both be confirmed to maintain a reasonable level of academic integrity. To pursue this goal in organized fashion, this thesis has the following three sections: (a) theoretical, (b) empirical, and (c) pragmatic

A Computational Academic Integrity Framework

L'abast creixent i la naturalesa canviant dels programes acadèmics constitueixen un repte per a la integritat dels protocols tradicionals de proves i exàmens. L'objectiu d'aquesta tesi és introduir una alternativa als enfocaments tradicionals d'integritat acadèmica, per a cobrir la bretxa del buit de l'anonimat i donar la possibilitat als instructors i administradors acadèmics de fer servir nous mitjans que permetin mantenir la integritat acadèmica i promoguin la responsabilitat, accessibilitat i eficiència, a més de preservar la privadesa i minimitzin la interrupció en el procés d'aprenentatge. Aquest treball té com a objectiu començar un canvi de paradigma en les pràctiques d'integritat acadèmica. La recerca en l'àrea de la identitat de l'estudiant i la garantia de l'autoria són importants perquè la concessió de crèdits d'estudi a entitats no verificades és perjudicial per a la credibilitat institucional i la seguretat pública. Aquesta tesi es basa en la noció que la identitat de l'alumne es compon de dues capes diferents, física i de comportament, en les quals tant els criteris d'identitat com els d'autoria han de ser confirmats per a mantenir un nivell raonable d'integritat acadèmica. Per a això, aquesta tesi s'organitza en tres seccions, cadascuna de les quals aborda el problema des d'una de les perspectives següents: (a) teòrica, (b) empírica i (c) pragmàtica.El creciente alcance y la naturaleza cambiante de los programas académicos constituyen un reto para la integridad de los protocolos tradicionales de pruebas y exámenes. El objetivo de esta tesis es introducir una alternativa a los enfoques tradicionales de integridad académica, para cubrir la brecha del vacío anonimato y dar la posibilidad a los instructores y administradores académicos de usar nuevos medios que permitan mantener la integridad académica y promuevan la responsabilidad, accesibilidad y eficiencia, además de preservar la privacidad y minimizar la interrupción en el proceso de aprendizaje. Este trabajo tiene como objetivo iniciar un cambio de paradigma en las prácticas de integridad académica. La investigación en el área de la identidad del estudiante y la garantía de la autoría son importantes porque la concesión de créditos de estudio a entidades no verificadas es perjudicial para la credibilidad institucional y la seguridad pública. Esta tesis se basa en la noción de que la identidad del alumno se compone de dos capas distintas, física y de comportamiento, en las que tanto los criterios de identidad como los de autoría deben ser confirmados para mantener un nivel razonable de integridad académica. Para ello, esta tesis se organiza en tres secciones, cada una de las cuales aborda el problema desde una de las siguientes perspectivas: (a) teórica, (b) empírica y (c) pragmática.The growing scope and changing nature of academic programmes provide a challenge to the integrity of traditional testing and examination protocols. The aim of this thesis is to introduce an alternative to the traditional approaches to academic integrity, bridging the anonymity gap and empowering instructors and academic administrators with new ways of maintaining academic integrity that preserve privacy, minimize disruption to the learning process, and promote accountability, accessibility and efficiency. This work aims to initiate a paradigm shift in academic integrity practices. Research in the area of learner identity and authorship assurance is important because the award of course credits to unverified entities is detrimental to institutional credibility and public safety. This thesis builds upon the notion of learner identity consisting of two distinct layers (a physical layer and a behavioural layer), where the criteria of identity and authorship must both be confirmed to maintain a reasonable level of academic integrity. To pursue this goal in organized fashion, this thesis has the following three sections: (a) theoretical, (b) empirical, and (c) pragmatic

Adaptive Graph-Based Algorithms for Conditional Anomaly Detection and Semi-Supervised Learning

We develop graph-based methods for conditional anomaly detection and semi-supervised learning based on label propagation on a data similarity graph. When data is abundant or arrive in a stream, the problems of computation and data storage arise for any graph-based method. We propose a fast approximate online algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local representative points that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. Anomaly detection techniques are used to identify anomalous (unusual) patterns in data. In clinical settings, these may concern identifications of unusual patient--state outcomes or unusual patient-management decisions. Therefore, we also present graph-based methods for detecting conditional anomalies and apply it to the identification of unusual clinical actions in hospitals. Our hypothesis is that patient-management actions that are unusual with respect to the past patients may be due to errors and that it is worthwhile to raise an alert if such a condition is encountered. Conditional anomaly detection extends standard unconditional anomaly framework but also faces new problems known as fringe and isolated points. We devise novel nonparametric graph-based methods to tackle these problems. Our methods rely on graph connectivity analysis and soft harmonic solution. Finally, we conduct an extensive human evaluation study of our conditional anomaly methods by 15 experts in critical care

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Machine learning applications for the topology prediction of transmembrane beta-barrel proteins

The research topic for this PhD thesis focuses on the topology prediction of beta-barrel transmembrane proteins. Transmembrane proteins adopt various conformations that are about the functions that they provide. The two most predominant classes are alpha-helix bundles and beta-barrel transmembrane proteins. Alpha-helix proteins are present in larger numbers than beta-barrel transmembrane proteins in structure databases. Therefore, there is a need to find computational tools that can predict and detect the structure of beta-barrel transmembrane proteins. Transmembrane proteins are used for active transport across the membrane or signal transduction. Knowing the importance of their roles, it becomes essential to understand the structures of the proteins. Transmembrane proteins are also a significant focus for new drug discovery. Transmembrane beta-barrel proteins play critical roles in the translocation machinery, pore formation, membrane anchoring, and ion exchange. In bioinformatics, many years of research have been spent on the topology prediction of transmembrane alpha-helices. The efforts to TMB (transmembrane beta-barrel) proteins topology prediction have been overshadowed, and the prediction accuracy could be improved with further research. Various methodologies have been developed in the past to predict TMB proteins topology. Methods developed in the literature that are available include turn identification, hydrophobicity profiles, rule-based prediction, HMM (Hidden Markov model), ANN (Artificial Neural Networks), radial basis function networks, or combinations of methods. The use of cascading classifier has never been fully explored. This research presents and evaluates approaches such as ANN (Artificial Neural Networks), KNN (K-Nearest Neighbors, SVM (Support Vector Machines), and a novel approach to TMB topology prediction with the use of a cascading classifier. Computer simulations have been implemented in MATLAB, and the results have been evaluated. Data were collected from various datasets and pre-processed for each machine learning technique. A deep neural network was built with an input layer, hidden layers, and an output. Optimisation of the cascading classifier was mainly obtained by optimising each machine learning algorithm used and by starting using the parameters that gave the best results for each machine learning algorithm. The cascading classifier results show that the proposed methodology predicts transmembrane beta-barrel proteins topologies with high accuracy for randomly selected proteins. Using the cascading classifier approach, the best overall accuracy is 76.3%, with a precision of 0.831 and recall or probability of detection of 0.799 for TMB topology prediction. The accuracy of 76.3% is achieved using a two-layers cascading classifier. By constructing and using various machine-learning frameworks, systems were developed to analyse the TMB topologies with significant robustness. We have presented several experimental findings that may be useful for future research. Using the cascading classifier, we used a novel approach for the topology prediction of TMB proteins