Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation

IEEE Access Special Section Editorial: Data-Driven Monitoring, Fault Diagnosis and Control of Cyber-Physical Systems

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Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Warming Up a Cold Front-End with Ignite

Serverless computing is a popular software deployment model for the cloud, in which applications are designed as a collection of stateless tasks. Developers are charged for the CPU time and memory footprint during the execution of each  serverless function, which incentivizes them to reduce both runtime and memory usage. As a result, functions tend to be short (often on the order of a few milliseconds) and compact (128–256 MB). Cloud providers can pack thousands of such functions on a server, resulting in frequent context switches and a tremendous degree of interleaving. As a result, when a given memory-resident function is re-invoked, it commonly finds its on-chip microarchitectural state completelycold due to thrashing by other functions — a phenomenon termed lukewarm invocation. Our analysis shows that the cold microarchitectural state due to lukewarm invocations is highly detrimental to performance, which corroborates prior work. The main source of performance degradation is the front-end, composed of instruction delivery, branch identification via the BTB and the conditional branch prediction. State-of-the-art front-end prefetchers show only limited effectiveness on lukewarm invocations, falling considerably short of an ideal front-end. We demonstrate that the reason for this is the cold microarchitectural state of the branch identification and prediction units. In response, we introduce Ignite, a comprehensive restoration mechanism for front-end microarchitectural state targeting instructions, BTB and branch predictor via unified metadata. Ignite records an invocation’s control flow graph in compressed format and uses that to restore the front-end structures the next time the function is invoked. Ignite outperforms state-of-the-art front-end prefetchers, improving performance by an average of 43% by significantly reducing instruction, BTB and branch predictor MPKI

Privacy-Preserved Linkable Social-Physical Data Publication

In this dissertation, we investigate the privacy-preserved data publication problems towards pervasively existing linkable social-physical contents. On the one hand, data publication has been considered as a critical approach to facilitate numerous utilities for individuals, populations, platform owners, and all third-party service providers. On the other hand, the unprecedented adoption of mobile devices and the dramatic development of Internet-of-Thing (IoT) systems have pushed the collection of surrounding physical information among populations to a totally novel stage. The collected contents can provide a fine-grained access to both physical and social aspects of the crowds, which introduces a comprehensively linkable and potentially sensitive information domain. The linkage includes the related index like privacy, utility, and efficiency for sophisticated applications, the inherent correlations among multiple data sources or information dimensions, and the connections among individuals. As the linkage leads to various novel challenges for privacy preservation, there should be a body of novel mechanisms for linkable social-physical data publications. As a result, this dissertation proposes a series of mechanisms for privacy-preserved linkable social-physical data publication. Firstly, we study the publication of physical data where the co-existing useful social proles and the sensitive physical proles of the data should be carefully maintained. Secondly, we investigate the data publication problem jointly considering the privacy preservation, data utility, and resource efficiency for task completion in crowd-sensing systems. Thirdly, we investigate the publication of private contents used for the recommendation, where contents of a user contribute to the recommendation results for others. Fourthly, we study the publications of reviews in local business service systems, where users expect to conceal their frequently visited locations while cooperatively maintain the utility of the whole system. Fifthly, we study the acquisition of privacy-preserved knowledge on cyber-physical social networks, where third-party service providers can derive the community structure without accessing the sensitive social links. We also provide detailed analysis and discussion for proposed mechanisms, and extensively validate their performance via real-world datasets. Both results demonstrate that the proposed mechanisms can properly preserve the privacy while maintaining the data utility. At last, we also propose the future research topics to complete the whole dissertation. The first topic focuses on the privacy preservation towards correlations beneath multiple data sources. The second topic studies more privacy issues for the whole population during data publication, including both the novel threats for related communities, and the disclosure of trends within crowds

Personalized Resource Allocation in Wireless Networks: An AI-Enabled and Big Data-Driven Multi-Objective Optimization

The design and optimization of wireless networks have mostly been based on strong mathematical and theoretical modeling. Nonetheless, as novel applications emerge in the era of 5G and beyond, unprecedented levels of complexity will be encountered in the design and optimization of the network. As a result, the use of Artificial Intelligence (AI) is envisioned for wireless network design and optimization due to the flexibility and adaptability it offers in solving extremely complex problems in real-time. One of the main future applications of AI is enabling user-level personalization for numerous use cases. AI will revolutionize the way we interact with computers in which computers will be able to sense commands and emotions from humans in a non-intrusive manner, making the entire process transparent to users. By leveraging this capability, and accelerated by the advances in computing technologies, wireless networks can be redesigned to enable the personalization of network services to the user level in real-time. While current wireless networks are being optimized to achieve a predefined set of quality requirements, the personalization technology advocated in this article is supported by an intelligent big data-driven layer designed to micro-manage the scarce network resources. This layer provides the intelligence required to decide the necessary service quality that achieves the target satisfaction level for each user. Due to its dynamic and flexible design, personalized networks are expected to achieve unprecedented improvements in optimizing two contradicting objectives in wireless networks: saving resources and improving user satisfaction levels

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security

Study and development of a web-based software for hybrid energy system design and solar prediction analysis

La sfida di integrare la potenza intermittente proveniente da fonti energetiche rinnovabili nella rete elettrica non può essere considerata come un problema isolato, ma deve essere visto come uno strumento per integrare e mettere in primo piano i sistemi energetici rinnovabili. Questo progetto di tesi di dottorato presenta l'analisi, lo studio e lo sviluppo di un software ad interfaccia web in grado di progettare sistemi energetici ibridi in qualsiasi luogo del mondo, in grado di migliorare l'affidabilità, la disponibilità e la sostenibilità sia di sistemi connessi alla rete che di sistemi isolati. Il software EHS (Energy Hybrid System) è stato sviluppato per ottenere la configurazione ottimale per varie tipologie di sistemi energetici ibridi. Lo studio della configurazione ottimale del sistema ibrido si basa sul valore del LCC (Life Cycle Cost) calcolato sulla durata potenziale dell'intero sistema considerando tutti i costi presenti e futuri. La tesi presenta un caso di studio di progettazione, effettuata tramite software EHS, di un sistema energetico ibrido situato in Uganda. I risultati rivelano che la configurazione ottimale del sistema ibrido (generatori FV-baterie-diesel), nonostante il suo elevato costo di investimento, presenta un beneficio economico del 25,5 e del 22,2% rispetto all'utilizzo di solo FV e generatori diesel e solo generatori diesel e una riduzione del consumo di carburante pari rispettivamente al 74,7 e al 77%. Al fine di migliorare l'efficienza del sistema energetico ibrido, il progetto di tesi propone anche uno sviluppo di uno strumento in grado di fare una previsione affidabile della produzione fotovoltaica attraverso uno strumento sperimentale chiamato "predittore solare". In questo studio è stato utilizzato un sistema di acquisizione di immagini, basato su una fotocamera digitale commerciale, utilizzate per ottenere l'elaborazione delle immagini, rilevamento dei corpi nuvolosi, previsione del loro movimento e predizione dell’irraggiamento.The challenge of integrating fluctuating power from renewable energy sources in the electricity grid cannot be looked upon as an isolated issue but should be seen as one out of various means and challenges of approaching sustainable energy systems. The presented PhD thesis project illustrates the analysis, study and development of a web-based software able to design hybrid energy systems in any location of the world, able improve the reliability, availability and sustainability of both grid-connected and isolated energy systems. The software EHS (Energy Hybrid System) is programmed to evaluate the optimal design for various configuration of energy hybrid systems. The evaluation of the optimal hybrid system configuration is based on the value of the LCC (Life Cycle Cost) calculated along the potential lifetime of the entire system, considering all the future costs. The thesis presents a design case study, carried out through EHS software, of a hybrid power system located in Uganda. The results of the simulation through the software EHS show that the usage of battery storage is economically crucial. Results disclose that the optimal configuration of the hybrid system (PV-storage-diesel generators), despite its high investment cost, presents an economic benefit of 25.5 and 22.2% compared to the usage of only PV array and diesel generators and only diesel generators and a reduction of fuel consumption equal to 74.7 and 77%, respectively. In order to improve the hybrid energy system efficiency, the thesis project also proposes a development of an instrument able to make a reliable prediction of PV production and a solar irradiance forecast methodology to predict the photovoltaic production through an experimental instrument called "solar predictor". Within this study a sky image system, based on a commercial digital camera, has been used and characterised with respect to get image elaboration, cloudy shape detection, motion estimation and tracking

A systematic literature review on the use of big data for sustainable tourism

Sustainable tourism research focuses on mitigating or remediating environmental, social and economic impacts on tourism. In the past years, Big Data approaches have been applied to the field of tourism allowing for remarkable progress. However, there seems to be little evidence to support that such approaches are an inspiration to sustainable tourism and are being implemented. In this context, we aim to obtain a comprehensive overview of the use of Big Data in sustainable tourism to address various issues and understand how Big Data can support decision-making in such scenarios. To that end, this paper reports on the results of a literature review via a combination of a Systematic Literature Review (SLR) in Software Engineering, and the use of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. In summary, we investigated four facets: (a) sources of big data, (b) approaches, (c) purposes, and (d) contexts of application. The results suggest that the use of various approaches have impacted practices in sustainable tourism. The findings provide a thorough understanding of the state of the art of Big Data application in sustainable tourism and provide valuable insights to foster growth both in terms of research and practice

Fault Detection of Inter-Turn Short-Circuited Stator Windings in Permanent Magnet Synchronous Machines

Vannkraftverk leverer grønn og pålitelig energi til befolkningen i Norge, og bidrar med rundt 88 % av landets årlige strømbehov. Uventede avbrudd og stans for kraftverkene vil resultere i store økonomiske tap, samt at kraftverkene ikke får levert nødvendig kraft til nettet. Med fremveksten av Industri 4.0 benytter industriene nyskapende teknologier som skytjenester, Kunstig Intelligens (KI) og tingenes internett for å forbedre de ulike operasjonene i selskapet. Innen vannkraft-industrien vil KI-baserte systemer bli brukt som grunnlag for prediktive vedlikehold. I dag utføres det meste av vedlikeholdsarbeid i henhold til en planlagt tidsplan, og industrien ser derfor på bruk av maskinlærings-metoder for tidlig feilgjenkjenning i vannkraftverkene. Denne masteroppgaven ser på anvendelsen av maskinlærings-algoritmer for å tidlig forutsi kortslutninger i aramturviklingene i en Permanent Magnet Synkronmaskin (PMSM), ved bruk av trefaset strøm-data. Data A ble samlet inn i et internt laboratorium med en Permanent Magnet Synkrongenerator (PMSG) som hadde en implementert 4.8 % kortslutning i aramturviklingen. Dataen bestod av sunne og defekte datasett med RMSverdier for den trefasede strømmen. Data B ble hentet fra et tidligere arbeid av den samme typen PMSM med en 6.0 % kortslutning i aramturviklingen. Data B bestod av signal-verdier for den trefasede strømmen. Ved bruk av Python ble de to datasettene visuelt inspisert og forbehandlet ved hjelp av ‘Z-score’-metoden for å fjerne avvikende verdier. Denne prosessen hadde imidlertid ingen merkbar effekt på nøyaktigheten til maskinlærings-modellene. Enkel signalbehandling i tidsplanet ble anvendt på strømdataene, men klarte ikke å oppdage kortslutningsfeilen implementert på den andre faseviklingen. Statistiske parameter som gjennomsnitt, standard avvik, skjevhet, kurtose, toppverdifaktor, peak-to-peak, RMS, klaringsfaktor, formfaktor og impulsfaktor ble beregnet for alle tre fasene. En Principal Component Analysis (PCA)- algoritme ble anvendt på datasettene med de statistiske parameterne og reduserte Data A fra 18 parameter til tre Principal Components. Data B ble redusert fra 33 parametere til fire Principal Components. Før dataen kjøres i maskinlørings-modellene, ble feilindikatorer som flagger verdier utenfor den 95. persentilen av gjennomsnittsverdiene til parameterne lagt til i datasettet . Fire overvåkede maskinlærings-modeller – ‘Random Forest’, ‘Decision Trees’, ‘k-NN’ og ‘Naive Bayes’ – ble kjørt for datasettene. Random Forest- og Decision Tree-modellene hadde en tendens til å overtilpasse maskinlærings-prediksjonene på datasettene som inneholdt de statistisk parameterne. Datasettet med PCA-komponentene reduserte overtilpasningen av disse modellene og forbedret nøyaktigheten til Naive Bayes-modellen. Ettersom Naive Bayes-modellen ga varierende resultater og ble ansett som inkonsekvent, samt overtilpasnings-tendensene til Random Forest og Decision Tree, ble k-NN-modellen vurdert som den mest pålitelige av maskinlærings-modellene. De beste feilindikatorene for Data A var kurtose- og skjevhet-indikatorene, mens klaringsfaktor og formfaktor ga best nøyaktighet for Data B. Videre arbeid bør unngå bruk av data som inneholder RMS-verdier, og fokusere på bruk av signalbaserte verdier slik som i Data B. Dataprosessering og feilmerking bør også utføres i frekvensplanet, ettersom en stor svakhet ved avhandlingen er at metodikken kun ble anvendt i tidplanet. Andre ytelsesindikatorer som robusthet bør også brukes for å vurdere ytelsen til maskinlærings-modellene