327 research outputs found
Performance Analysis Of Data-Driven Algorithms In Detecting Intrusions On Smart Grid
The traditional power grid is no longer a practical solution for power delivery due to several shortcomings, including chronic blackouts, energy storage issues, high cost of assets, and high carbon emissions. Therefore, there is a serious need for better, cheaper, and cleaner power grid technology that addresses the limitations of traditional power grids. A smart grid is a holistic solution to these issues that consists of a variety of operations and energy measures. This technology can deliver energy to end-users through a two-way flow of communication. It is expected to generate reliable, efficient, and clean power by integrating multiple technologies. It promises reliability, improved functionality, and economical means of power transmission and distribution. This technology also decreases greenhouse emissions by transferring clean, affordable, and efficient energy to users. Smart grid provides several benefits, such as increasing grid resilience, self-healing, and improving system performance. Despite these benefits, this network has been the target of a number of cyber-attacks that violate the availability, integrity, confidentiality, and accountability of the network. For instance, in 2021, a cyber-attack targeted a U.S. power system that shut down the power grid, leaving approximately 100,000 people without power. Another threat on U.S. Smart Grids happened in March 2018 which targeted multiple nuclear power plants and water equipment. These instances represent the obvious reasons why a high level of security approaches is needed in Smart Grids to detect and mitigate sophisticated cyber-attacks. For this purpose, the US National Electric Sector Cybersecurity Organization and the Department of Energy have joined their efforts with other federal agencies, including the Cybersecurity for Energy Delivery Systems and the Federal Energy Regulatory Commission, to investigate the security risks of smart grid networks. Their investigation shows that smart grid requires reliable solutions to defend and prevent cyber-attacks and vulnerability issues. This investigation also shows that with the emerging technologies, including 5G and 6G, smart grid may become more vulnerable to multistage cyber-attacks. A number of studies have been done to identify, detect, and investigate the vulnerabilities of smart grid networks. However, the existing techniques have fundamental limitations, such as low detection rates, high rates of false positives, high rates of misdetection, data poisoning, data quality and processing, lack of scalability, and issues regarding handling huge volumes of data. Therefore, these techniques cannot ensure safe, efficient, and dependable communication for smart grid networks. Therefore, the goal of this dissertation is to investigate the efficiency of machine learning in detecting cyber-attacks on smart grids. The proposed methods are based on supervised, unsupervised machine and deep learning, reinforcement learning, and online learning models. These models have to be trained, tested, and validated, using a reliable dataset. In this dissertation, CICDDoS 2019 was used to train, test, and validate the efficiency of the proposed models. The results show that, for supervised machine learning models, the ensemble models outperform other traditional models. Among the deep learning models, densely neural network family provides satisfactory results for detecting and classifying intrusions on smart grid. Among unsupervised models, variational auto-encoder, provides the highest performance compared to the other unsupervised models. In reinforcement learning, the proposed Capsule Q-learning provides higher detection and lower misdetection rates, compared to the other model in literature. In online learning, the Online Sequential Euclidean Distance Routing Capsule Network model provides significantly better results in detecting intrusion attacks on smart grid, compared to the other deep online models
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Anomaly detection for IoT networks using machine learning
This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe Internet of Things (IoT) is considered one of the trending technologies today. IoT affects various industries, including logistics tracking, healthcare, automotive and smart cities. A rising number of cyber-attacks and breaches are rapidly targeting networks equipped with IoT devices. This thesis aims to improve security in IoT networks by enhancing anomaly detection using machine learning.
This thesis identified the challenges and gaps related to securing the Internet of Things networks. The challenges are network size, the number of devices, the human factor, and the complexity of IoT networks. The gaps identified include the lack of research on signature-based intrusion detection systems used for anomaly detection, in addition to the lack of modelling input parameters required for anomaly detection in IoT networks. Furthermore, there is a lack of comparison of the performance of machine learning algorithms on standard and real IoT datasets.
This thesis creates a dataset to test the anomaly binary classification performance of the Neural Networks, Gaussian Naive Bayes, Support Vector Machine, and Decision Trees machine learning algorithms and compares their results with the KDDCUP99 dataset. The results show that Support Vector Machine and Gaussian Naive Bayes perform lower than the other models on the created IoT dataset. This thesis reduces the number of features required by machine learning algorithms for anomaly detection in the IoT networks to five features only, which resulted in reduced execution time by an average of 58%.
This thesis tests CNNwGFC, which is an enhanced Convolutional Neural Network model, in detecting and classifying anomalies in IoT networks. This model achieves an increase of 15.34% in the accuracy for IoT anomaly classification in the UNSW-NB15 compared to the classic Convolutional Neural Network. The CNNwGFC multi-classification accuracy (96.24%) is higher by 7.16 than the highest from the literature
Security Technologies and Methods for Advanced Cyber Threat Intelligence, Detection and Mitigation
The rapid growth of the Internet interconnectivity and complexity of communication systems has led us to a significant growth of cyberattacks globally often with severe and disastrous consequences. The swift development of more innovative and effective (cyber)security solutions and approaches are vital which can detect, mitigate and prevent from these serious consequences. Cybersecurity is gaining momentum and is scaling up in very many areas. This book builds on the experience of the Cyber-Trust EU project’s methods, use cases, technology development, testing and validation and extends into a broader science, lead IT industry market and applied research with practical cases. It offers new perspectives on advanced (cyber) security innovation (eco) systems covering key different perspectives. The book provides insights on new security technologies and methods for advanced cyber threat intelligence, detection and mitigation. We cover topics such as cyber-security and AI, cyber-threat intelligence, digital forensics, moving target defense, intrusion detection systems, post-quantum security, privacy and data protection, security visualization, smart contracts security, software security, blockchain, security architectures, system and data integrity, trust management systems, distributed systems security, dynamic risk management, privacy and ethics
Ensembles of Pruned Deep Neural Networks for Accurate and Privacy Preservation in IoT Applications
The emergence of the AIoT (Artificial Intelligence of Things) represents the powerful convergence of Artificial Intelligence (AI) with the expansive realm of the Internet of Things (IoT). By integrating AI algorithms with the vast network of interconnected IoT devices, we open new doors for intelligent decision-making and edge data analysis, transforming various domains from healthcare and transportation to agriculture and smart cities.
However, this integration raises pivotal questions: How can we ensure deep learning models are aptly compressed and quantised to operate seamlessly on devices constrained by computational resources, without compromising accuracy? How can these models be effectively tailored to cope with the challenges of statistical heterogeneity and the uneven distribution of class labels inherent in IoT applications? Furthermore, in an age where data is a currency, how do we uphold the sanctity of privacy for the sensitive data that IoT devices incessantly generate while also ensuring the unhampered deployment of these advanced deep learning models?
Addressing these intricate challenges forms the crux of this thesis, with its contributions delineated as follows:
Ensyth: A novel approach designed to synthesise pruned ensembles of deep learning models, which not only makes optimal use of limited IoT resources but also ensures a notable boost in predictability. Experimental evidence gathered from CIFAR-10, CIFAR-5, and MNIST-FASHION datasets solidify its merit, especially given its capacity to achieve high predictability.
MicroNets: Venturing into the realms of efficiency, this is a multi-phase pruning pipeline that fuses the principles of weight pruning, channel pruning. Its objective is clear: foster efficient deep ensemble learning, specially crafted for IoT devices. Benchmark tests conducted on CIFAR-10 and CIFAR-100 datasets demonstrate its prowess, highlighting a compression ratio of nearly 92%, with these pruned ensembles surpassing the accuracy metrics set by conventional models.
FedNets: Recognising the challenges of statistical heterogeneity in federated learning and the ever-growing concerns of data privacy, this innovative federated learning framework is introduced. It facilitates edge devices in their collaborative quest to train ensembles of pruned deep neural networks. More than just training, it ensures data privacy remains uncompromised. Evaluations conducted on the Federated CIFAR-100 dataset offer a testament to its efficacy.
In this thesis, substantial contributions have been made to the AIoT application domain. Ensyth, MicroNets, and FedNets collaboratively tackle the challenges of efficiency, accuracy, statistical heterogeneity arising from distributed class labels, and privacy concerns inherent in deploying AI applications on IoT devices. The experimental results underscore the effectiveness of these approaches, paving the way for their practical implementation in real-world scenarios. By offering an integrated solution that satisfies multiple key requirements simultaneously, this research brings us closer to the realisation of effective and privacy-preserved AIoT systems
Jornadas Nacionales de Investigación en Ciberseguridad: actas de las VIII Jornadas Nacionales de Investigación en ciberseguridad: Vigo, 21 a 23 de junio de 2023
Jornadas Nacionales de Investigación en Ciberseguridad (8ª. 2023. Vigo)atlanTTicAMTEGA: Axencia para a modernización tecnolóxica de GaliciaINCIBE: Instituto Nacional de Cibersegurida
Технология комплексной поддержки жизненного цикла семантически совместимых интеллектуальных компьютерных систем нового поколения
В издании представлено описание текущей версии открытой технологии онтологического проектирования, производства и эксплуатации семантически совместимых гибридных интеллектуальных компьютерных систем (Технологии OSTIS). Предложена стандартизация интеллектуальных компьютерных систем, а также стандартизация методов и
средств их проектирования, что является важнейшим фактором, обеспечивающим семантическую совместимость интеллектуальных компьютерных систем и их компонентов, что
существенное снижение трудоемкости разработки таких систем.
Книга предназначена всем, кто интересуется проблемами искусственного интеллекта, а также специалистам в области интеллектуальных компьютерных систем и инженерии знаний. Может быть использована студентами, магистрантами и аспирантами специальности «Искусственный интеллект».
Табл. 8. Ил. 223. Библиогр.: 665 назв
Towards Mobility Data Science (Vision Paper)
Mobility data captures the locations of moving objects such as humans,
animals, and cars. With the availability of GPS-equipped mobile devices and
other inexpensive location-tracking technologies, mobility data is collected
ubiquitously. In recent years, the use of mobility data has demonstrated
significant impact in various domains including traffic management, urban
planning, and health sciences. In this paper, we present the emerging domain of
mobility data science. Towards a unified approach to mobility data science, we
envision a pipeline having the following components: mobility data collection,
cleaning, analysis, management, and privacy. For each of these components, we
explain how mobility data science differs from general data science, we survey
the current state of the art and describe open challenges for the research
community in the coming years.Comment: Updated arXiv metadata to include two authors that were missing from
the metadata. PDF has not been change
Acoustic-based Smart Tactile Sensing in Social Robots
Mención Internacional en el título de doctorEl sentido del tacto es un componente crucial de la interacción social humana y es único
entre los cinco sentidos. Como único sentido proximal, el tacto requiere un contacto
físico cercano o directo para registrar la información. Este hecho convierte al tacto en
una modalidad de interacción llena de posibilidades en cuanto a comunicación social. A través
del tacto, podemos conocer la intención de la otra persona y comunicar emociones. De esta
idea surge el concepto de social touch o tacto social como el acto de tocar a otra persona en
un contexto social. Puede servir para diversos fines, como saludar, mostrar afecto, persuadir
y regular el bienestar emocional y físico.
Recientemente, el número de personas que interactúan con sistemas y agentes artificiales
ha aumentado, principalmente debido al auge de los dispositivos tecnológicos, como los smartphones
o los altavoces inteligentes. A pesar del auge de estos dispositivos, sus capacidades de
interacción son limitadas. Para paliar este problema, los recientes avances en robótica social han
mejorado las posibilidades de interacción para que los agentes funcionen de forma más fluida y
sean más útiles. En este sentido, los robots sociales están diseñados para facilitar interacciones
naturales entre humanos y agentes artificiales. El sentido del tacto en este contexto se revela
como un vehículo natural que puede mejorar la Human-Robot Interaction (HRI) debido a su
relevancia comunicativa en entornos sociales. Además de esto, para un robot social, la relación
entre el tacto social y su aspecto es directa, al disponer de un cuerpo físico para aplicar o recibir
toques.
Desde un punto de vista técnico, los sistemas de detección táctil han sido objeto recientemente
de nuevas investigaciones, sobre todo dedicado a comprender este sentido para crear sistemas
inteligentes que puedan mejorar la vida de las personas. En este punto, los robots sociales
se han convertido en dispositivos muy populares que incluyen tecnologías para la detección
táctil. Esto está motivado por el hecho de que un robot puede esperada o inesperadamente
tener contacto físico con una persona, lo que puede mejorar o interferir en la ejecución de sus
comportamientos. Por tanto, el sentido del tacto se antoja necesario para el desarrollo de aplicaciones
robóticas. Algunos métodos incluyen el reconocimiento de gestos táctiles, aunque
a menudo exigen importantes despliegues de hardware que requieren de múltiples sensores. Además, la fiabilidad de estas tecnologías de detección es limitada, ya que la mayoría de ellas
siguen teniendo problemas tales como falsos positivos o tasas de reconocimiento bajas. La detección
acústica, en este sentido, puede proporcionar un conjunto de características capaces de
paliar las deficiencias anteriores. A pesar de que se trata de una tecnología utilizada en diversos
campos de investigación, aún no se ha integrado en la interacción táctil entre humanos y robots.
Por ello, en este trabajo proponemos el sistema Acoustic Touch Recognition (ATR), un sistema
inteligente de detección táctil (smart tactile sensing system) basado en la detección acústica
y diseñado para mejorar la interacción social humano-robot. Nuestro sistema está desarrollado
para clasificar gestos táctiles y localizar su origen. Además de esto, se ha integrado en plataformas
robóticas sociales y se ha probado en aplicaciones reales con éxito. Nuestra propuesta
se ha enfocado desde dos puntos de vista: uno técnico y otro relacionado con el tacto social.
Por un lado, la propuesta tiene una motivación técnica centrada en conseguir un sistema táctil
rentable, modular y portátil. Para ello, en este trabajo se ha explorado el campo de las tecnologías
de detección táctil, los sistemas inteligentes de detección táctil y su aplicación en HRI. Por
otro lado, parte de la investigación se centra en el impacto afectivo del tacto social durante la
interacción humano-robot, lo que ha dado lugar a dos estudios que exploran esta idea.The sense of touch is a crucial component of human social interaction and is unique
among the five senses. As the only proximal sense, touch requires close or direct physical
contact to register information. This fact makes touch an interaction modality
full of possibilities regarding social communication. Through touch, we are able to ascertain
the other person’s intention and communicate emotions. From this idea emerges the concept
of social touch as the act of touching another person in a social context. It can serve various purposes,
such as greeting, showing affection, persuasion, and regulating emotional and physical
well-being.
Recently, the number of people interacting with artificial systems and agents has increased,
mainly due to the rise of technological devices, such as smartphones or smart speakers. Still,
these devices are limited in their interaction capabilities. To deal with this issue, recent developments
in social robotics have improved the interaction possibilities to make agents more seamless
and useful. In this sense, social robots are designed to facilitate natural interactions between
humans and artificial agents. In this context, the sense of touch is revealed as a natural interaction
vehicle that can improve HRI due to its communicative relevance. Moreover, for a social
robot, the relationship between social touch and its embodiment is direct, having a physical
body to apply or receive touches.
From a technical standpoint, tactile sensing systems have recently been the subject of further
research, mostly devoted to comprehending this sense to create intelligent systems that can
improve people’s lives. Currently, social robots are popular devices that include technologies
for touch sensing. This is motivated by the fact that robots may encounter expected or unexpected
physical contact with humans, which can either enhance or interfere with the execution
of their behaviours. There is, therefore, a need to detect human touch in robot applications.
Some methods even include touch-gesture recognition, although they often require significant
hardware deployments primarily that require multiple sensors. Additionally, the dependability
of those sensing technologies is constrained because the majority of them still struggle with issues
like false positives or poor recognition rates. Acoustic sensing, in this sense, can provide a
set of features that can alleviate the aforementioned shortcomings. Even though it is a technology that has been utilised in various research fields, it has yet to be integrated into human-robot
touch interaction.
Therefore, in thiswork,we propose theATRsystem, a smart tactile sensing system based on
acoustic sensing designed to improve human-robot social interaction. Our system is developed
to classify touch gestures and locate their source. It is also integrated into real social robotic platforms
and tested in real-world applications. Our proposal is approached from two standpoints,
one technical and the other related to social touch. Firstly, the technical motivation of thiswork
centred on achieving a cost-efficient, modular and portable tactile system. For that, we explore
the fields of touch sensing technologies, smart tactile sensing systems and their application in
HRI. On the other hand, part of the research is centred around the affective impact of touch
during human-robot interaction, resulting in two studies exploring this idea.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Pedro Manuel Urbano de Almeida Lima.- Secretaria: María Dolores Blanco Rojas.- Vocal: Antonio Fernández Caballer
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