Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Detecção de ataques por canais laterais na camada física

Today, with the advent of IoT and the resulting fragmentation of wireless technologies, they bring not only benefits, but also concerns. Daily, several individuals communicate with each other using various communication methods. Individuals use a variety of devices for innocuous day-to-day activities; however, there are some malicious individuals (dishonest agents) whose aim is to cause harm, with the exfiltration of information being one of the biggest concerns. Since the security of Wi-Fi communications is one of the areas of greatest investment and research regarding Internet security, dishonest agents make use of side channels to exfiltrate information, namely Bluetooth. Most current solutions for anomaly detection on networks are based on analyzing frames or packets, which, inadvertently, can reveal user behavior patterns, which they consider to be private. In addition, solutions that focus on inspecting physical layer data typically use received signal power (RSSI) as a distance metric and detect anomalies based on the relative position of the network nodes, or use the spectrum values directly on models classification without prior data processing. This Dissertation proposes mechanisms to detect anomalies, while ensuring the privacy of its nodes, which are based on the analysis of radio activity in the physical layer, measuring the behavior of the network through the number of active and inactive frequencies and the duration of periods of silence and activity. After the extraction of properties that characterize these metrics,an exploration and study of the data is carried out, followed by the use of the result to train One-Class Classification models. The models are trained with data taken from a series of interactions between a computer, an AP, and a mobile phone in an environment with reduced noise, in an attempt to simulate a simplified home automation scenario. Then, the models were tested with similar data but containing a compromised node, which periodically sent a file to a local machine via a Bluetooth connection. The data show that, in both situations, it was possible to achieve detection accuracy rates in the order of 75 % and 99 %. This work ends with some ideas of resource work, namely changes in the level of pre-processing, ideas of new tests and how to reduce the percentage of false negatives.Hoje, com o advento da IoT e a resultante fragmentação das tecnologias sem fio, elas trazem não apenas benefícios, mas também preocupações. Diariamente vários indivíduos se comunicam entre si usando vários métodos de comunicação. Os indivíduos usam uma variedade de dispositivos para atividades inócuas do dia-adia; no entanto, existem alguns indivíduos mal-intencionados (agentes desonestos) cujo objetivo é causar danos, sendo a exfiltração de informação uma das maiores preocupações. Sendo a segurança das comunicações Wi-Fi uma das áreas de maior investimento e investigação no que toca a segurança na Internet, os agentes desonestos fazem uso de canais laterais para exfiltrar informação, nomeadamente o Bluetooth. A maioria das soluções atuais para deteção de anomalias em redes baseiam-se em analisar tramas ou pacotes, o que, inadvertidamente, pode revelar padrões de comportamento dos utilizadores, que estes considerem privados. Além disso, as soluções que se focam em inspecionar dados da camada física normalmente usam a potência de sinal recebido (RSSI) como uma métrica de distância e detetam anomalias baseadas na posição relativa dos nós da rede, ou usam os valores do espetro diretamente em modelos de classificação sem prévio tratamento de dados. Esta Dissertação propõe mecanismos para deteção de anomalias, assegurando simultaneamente a privacidade dos seus nós, que se baseiam na análise de atividade rádio na camada física, medindo os comportamentos da rede através do número de frequências ativas e inativas e a duração de períodos de silêncio e atividade. Depois da extração de propriedades que caracterizam estas métricas, é realizada uma exploração dos dados e um estudo das mesmas, sendo depois usadas para treinar modelos de classificação mono-classe. Os modelos são treinados com dados retirados de uma série de interações entre um computador, um AP, e um telemóvel num ambiente com ruído reduzido, numa tentativa de simular um cenário de automação doméstica simplificado. De seguida, os modelos foram testados com dados semelhantes mas contendo um nó comprometido, que periodicamente enviava um ficheiro para uma máquina local através de uma ligação Bluetooth. Os dados mostram que, em ambas as situações, foi possível atingir taxas de precisão de deteção na ordem dos 75% e 99%. Este trabalho finaliza com algumas ideias de trabalho futuro, nomeadamente alterações ao nível do pré-processamento, ideias de novos testes e como diminuir a percentagem de falsos negativos.Mestrado em Engenharia de Computadores e Telemátic

AI-enabled modeling and monitoring of data-rich advanced manufacturing systems

The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Symmetry-Adapted Machine Learning for Information Security

Symmetry-adapted machine learning has shown encouraging ability to mitigate the security risks in information and communication technology (ICT) systems. It is a subset of artificial intelligence (AI) that relies on the principles of processing future events by learning past events or historical data. The autonomous nature of symmetry-adapted machine learning supports effective data processing and analysis for security detection in ICT systems without the interference of human authorities. Many industries are developing machine-learning-adapted solutions to support security for smart hardware, distributed computing, and the cloud. In our Special Issue book, we focus on the deployment of symmetry-adapted machine learning for information security in various application areas. This security approach can support effective methods to handle the dynamic nature of security attacks by extraction and analysis of data to identify hidden patterns of data. The main topics of this Issue include malware classification, an intrusion detection system, image watermarking, color image watermarking, battlefield target aggregation behavior recognition model, IP camera, Internet of Things (IoT) security, service function chain, indoor positioning system, and crypto-analysis

Cyber Security and Critical Infrastructures 2nd Volume

The second volume of the book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles, including an editorial that explains the current challenges, innovative solutions and real-world experiences that include critical infrastructure and 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems

Advanced Sensors for Real-Time Monitoring Applications

It is impossible to imagine the modern world without sensors, or without real-time information about almost everything—from local temperature to material composition and health parameters. We sense, measure, and process data and act accordingly all the time. In fact, real-time monitoring and information is key to a successful business, an assistant in life-saving decisions that healthcare professionals make, and a tool in research that could revolutionize the future. To ensure that sensors address the rapidly developing needs of various areas of our lives and activities, scientists, researchers, manufacturers, and end-users have established an efficient dialogue so that the newest technological achievements in all aspects of real-time sensing can be implemented for the benefit of the wider community. This book documents some of the results of such a dialogue and reports on advances in sensors and sensor systems for existing and emerging real-time monitoring applications