1,536 research outputs found

    Insertion Detection System Employing Neural Network MLP and Detection Trees Using Different Techniques

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    by addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available mechanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures to maintain the confidentiality, integrity, and availability of the services. This paper shows how the NSL-KDD dataset may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step to create an acceptable and balanced experimental data set to improve accuracy and minimize false positives. For this study, the approaches J48 and MLP were employed. The Decision Trees classifier has been demonstrated to have the highest accuracy rate for detecting and categorizing all NSL-KDD dataset attacks

    Study of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearables

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    This paper discusses the possibility of detecting personal stress making use of popular wearable devices available in the market. Different instruments found in the literature to measure stress-related features are reviewed, distinguishing between subjective tests and mechanisms supported by the analysis of physiological signals from clinical devices. Taking them as a reference, a solution to estimate stress based on the use of commercial-off-the-shelf wrist wearables and machine learning techniques is described. A mobile app was developed to induce stress in a uniform and systematic way. The app implements well-known stress inducers, such as the Paced Auditory Serial Addition Test, the Stroop Color-Word Interference Test, and a hyperventilation activity. Wearables are used to collect physiological data used to train classifiers that provide estimations on personal stress levels. The solution has been validated in an experiment involving 19 subjects, offering an average accuracy and F-measures close to 0.99 in an individual model and an accuracy and F-measure close to 0.85 in a global 2-level classifier model. Stress can be a worrying problem in different scenarios, such as in educational settings. Thus, the last part of the paper describes the proposal of a set of stress related indicators aimed to support the management of stress over time in such settings.Agencia Estatal de Investigación | Ref. TIN2016-80515-RUniversidade de Vig

    Evaluation of commercial-off-the-shelf wrist wearables to estimate stress on students

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    Wearable commercial-off-the-shelf (COTS) devices have become popular during the last years to monitor sports activities, primarily among young people. These devices include sensors to gather data on physiological signals such as heart rate, skin temperature or galvanic skin response. By applying data analytics techniques to these kinds of signals, it is possible to obtain estimations of higher-level aspects of human behavior. In the literature, there are several works describing the use of physiological data collected using clinical devices to obtain information on sleep patterns or stress. However, it is still an open question whether data captured using COTS wrist wearables is sufficient to characterize the learners' psychological state in educational settings. This paper discusses a protocol to evaluate stress estimation from data obtained using COTS wrist wearables. The protocol is carried out in two phases. The first stage consists of a controlled laboratory experiment, where a mobile app is used to induce different stress levels in a student by means of a relaxing video, a Stroop Color and Word test, a Paced Auditory Serial Addition test, and a hyperventilation test. The second phase is carried out in the classroom, where stress is analyzed while performing several academic activities, namely attending to theoretical lectures, doing exercises and other individual activities, and taking short tests and exams. In both cases, both quantitative data obtained from COTS wrist wearables and qualitative data gathered by means of questionnaires are considered. This protocol involves a simple and consistent method with a stress induction app and questionnaires, requiring a limited participation of support staff.Agencia Estatal de Investigación | Ref. TIN2016-80515-

    Algorithm selection using edge ML and case-based reasoning

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    In practical data mining, a wide range of classification algorithms is employed for prediction tasks. However, selecting the best algorithm poses a challenging task for machine learning practitioners and experts, primarily due to the inherent variability in the characteristics of classification problems, referred to as datasets, and the unpredictable performance of these algorithms. Dataset characteristics are quantified in terms of meta-features, while classifier performance is evaluated using various performance metrics. The assessment of classifiers through empirical methods across multiple classification datasets, while considering multiple performance metrics, presents a computationally expensive and time-consuming obstacle in the pursuit of selecting the optimal algorithm. Furthermore, the scarcity of sufficient training data, denoted by dimensions representing the number of datasets and the feature space described by meta-feature perspectives, adds further complexity to the process of algorithm selection using classical machine learning methods. This research paper presents an integrated framework called eML-CBR that combines edge edge-ML and case-based reasoning methodologies to accurately address the algorithm selection problem. It adapts a multi-level, multi-view case-based reasoning methodology, considering data from diverse feature dimensions and the algorithms from multiple performance aspects, that distributes computations to both cloud edges and centralized nodes. On the edge, the first-level reasoning employs machine learning methods to recommend a family of classification algorithms, while at the second level, it recommends a list of the top-k algorithms within that family. This list is further refined by an algorithm conflict resolver module. The eML-CBR framework offers a suite of contributions, including integrated algorithm selection, multi-view meta-feature extraction, innovative performance criteria, improved algorithm recommendation, data scarcity mitigation through incremental learning, and an open-source CBR module, reshaping research paradigms. The CBR module, trained on 100 datasets and tested with 52 datasets using 9 decision tree algorithms, achieved an accuracy of 94% for correct classifier recommendations within the top k=3 algorithms, making it highly suitable for practical classification applications

    Applying machine learning: a multi-role perspective

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    Machine (and deep) learning technologies are more and more present in several fields. It is undeniable that many aspects of our society are empowered by such technologies: web searches, content filtering on social networks, recommendations on e-commerce websites, mobile applications, etc., in addition to academic research. Moreover, mobile devices and internet sites, e.g., social networks, support the collection and sharing of information in real time. The pervasive deployment of the aforementioned technological instruments, both hardware and software, has led to the production of huge amounts of data. Such data has become more and more unmanageable, posing challenges to conventional computing platforms, and paving the way to the development and widespread use of the machine and deep learning. Nevertheless, machine learning is not only a technology. Given a task, machine learning is a way of proceeding (a way of thinking), and as such can be approached from different perspectives (points of view). This, in particular, will be the focus of this research. The entire work concentrates on machine learning, starting from different sources of data, e.g., signals and images, applied to different domains, e.g., Sport Science and Social History, and analyzed from different perspectives: from a non-data scientist point of view through tools and platforms; setting a problem stage from scratch; implementing an effective application for classification tasks; improving user interface experience through Data Visualization and eXtended Reality. In essence, not only in a quantitative task, not only in a scientific environment, and not only from a data-scientist perspective, machine (and deep) learning can do the difference

    Essays on Innovations in Public Sector Auditing

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    The current antecedents of innovation in the public sector, that is, the adoption of SDGs and the unprecedented technological advancements exert pressures on the Supreme audit institutions’(SAIs) current socio-technical system. This has led SAIs to adopt different strategies to maintain their relevance and improve the quality of their work and operations. This thesis investigated the different types of innovations currently happening in the SAIs environment and how SAIs are reacting to the demands of these changes. This exploratory work captured public sector audit innovation through the following three essays: The first essay focused on Digital Transformation (DT), investigated how SAIs approach, and interpret DT. In this regard, DT was investigated from a SAIs perspective. Due to it being a novel topic in public sector auditing research, a qualitative research method was adopted, this method was supported with expert interviews and archival and or document data. Key findings revealed that the definition of DT varies from SAI to SAI, and this variation resulted from the differences in the level of digital development in each country. SAIs applied reactive and, in some situations proactive change strategies were applied. In the reactive strategy, SAIs reacted to change induced by a situational demand while in the proactive strategy, they experiment with technologies in advance. Most of the SAIs applying proactive change strategy operates an innovation lab or an experimentation space(see Bojovic, Sabatier, and Coblence 2020; Bucher and Langley 2016; Cartel, Boxenbaum, and Aggeri 2019; Wulf 2000). As an impact on public sector auditing profession, the research addresses the popular narrative of SAI’s equating digitization or the use of digital technologies to Digital transformation. It reiterated the holistic nature of DT, by pointing at the risk involved when DT is tied solely to technology adoption strategy ignoring other aspects such as people, organizational structure, strategy, culture, etc.La trasformazione in corso dell'ambiente esterno delle Istituzioni Superiori di Controllo (ISC, Corte dei conti) sta modificando le esigenze di controllo e le aspettative dei vari stakeholders coinvolti. Infatti, questa trasformazione, innescato dai progressi tecnologici, dall'adozione degli Obiettivi di Sviluppo Sostenibile (OSS) e dalla trasparenza sta modificando il modo e gli strumenti con cui viene esercitata l’attività di controllo. Ciò ha portato le ISC a adottare diverse strategie ed a introdurre diverse innovazioni per mantenere la loro rilevanza e migliorare la qualità del loro servizio. Vari autori hanno evidenziato la necessità di indagare circa le implicazioni del cambio della strategia di controllo e dell’adozione delle varie innovazioni tecnologiche nelle ISC. Il lavoro di tesi contribuisce in questa direzione e indaga sulle varie innovazioni tecnologiche adottate dalle ISC e come questi Istituzioni hanno reagito alle pressioni esterne di cambiamento. La tesi adotta un approccio esplorativo e sviluppa tre diverse ricerche per rispondere alla domanda principale di ricerca. La prima ricerca si concentra sulla trasformazione digitale (TD), e indaga su come le ISC hanno affrontato e interpretato la TD. La metodologia utilizzata è di tipo qualitativo. Sono state effettuate varie interviste a esperti del settore a livello internazionale oltre all’analisi documentale degli archivi delle varie istituzioni analizzate. I risultati hanno mostrato una diversa interpretazione e percezione, tra le istituzioni oggetto dello studio, del concetto della TD, dovuta alle differenze di sviluppo digitale nei vari paesi analizzati. Inoltre, i risultati mostrano che le ISC hanno adottato strategie reattive di cambiamento e, in alcune situazioni, hanno adottato strategie proattive. Nel primo caso, che rappresenta la maggioranza dei casi analizzati, le ISC hanno reagito al bisogno ovvero quando si presenta una necessità di cambiamento. Mentre nel secondo caso, ovvero di strategia di cambiamento proattivo, le ISC hanno sperimentato le tecnologie in anticipo. La maggior parte delle Istituzioni che ha adottato strategie proattive di cambiamento gestisce un laboratorio di innovazione o uno spazio di sperimentazione (vedi Bojovic, Sabatier e Coblence 2020; Bucher e Langley 2016; Cartel, Boxenbaum e Aggeri 2019; Wulf 2000). Inoltre, la ricerca mostra come la digitalizzazione o l'uso delle tecnologie digitali vengono equiparati alla TD nelle ISC. Questo rischio di interpretazione del concetto si concretizza soprattutto, come mostrano i risultati, quando la TD viene legata esclusivamente alla strategia di adozione della tecnologia ignorando altri aspetti come le persone, la struttura organizzativa, la strategia, la cultura, ecc

    Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology

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    Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/

    Learning-Based Ubiquitous Sensing For Solving Real-World Problems

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    Recently, as the Internet of Things (IoT) technology has become smaller and cheaper, ubiquitous sensing ability within these devices has become increasingly accessible. Learning methods have also become more complex in the field of computer science ac- cordingly. However, there remains a gap between these learning approaches and many problems in other disciplinary fields. In this dissertation, I investigate four different learning-based studies via ubiquitous sensing for solving real-world problems, such as in IoT security, athletics, and healthcare. First, I designed an online intrusion detection system for IoT devices via power auditing. To realize the real-time system, I created a lightweight power auditing device. With this device, I developed a distributed Convolutional Neural Network (CNN) for online inference. I demonstrated that the distributed system design is secure, lightweight, accurate, real-time, and scalable. Furthermore, I characterized potential Information-stealer attacks via power auditing. To defend against this potential exfiltration attack, a prototype system was built on top of the botnet detection system. In a testbed environment, I defined and deployed an IoT Information-stealer attack. Then, I designed a detection classifier. Altogether, the proposed system is able to identify malicious behavior on endpoint IoT devices via power auditing. Next, I enhanced athletic performance via ubiquitous sensing and machine learning techniques. I first designed a metric called LAX-Score to quantify a collegiate lacrosse team’s athletic performance. To derive this metric, I utilized feature selection and weighted regression. Then, the proposed metric was statistically validated on over 700 games from the last three seasons of NCAA Division I women’s lacrosse. I also exam- ined the biometric sensing dataset obtained from a collegiate team’s athletes over the course of a season. I then identified the practice features that are most correlated with high-performance games. Experimental results indicate that LAX-Score provides insight into athletic performance quality beyond wins and losses. Finally, I studied the data of patients with Parkinson’s Disease. I secured the Inertial Measurement Unit (IMU) sensing data of 30 patients while they conducted pre-defined activities. Using this dataset, I measured tremor events during drawing activities for more convenient tremor screening. Our preliminary analysis demonstrates that IMU sensing data can identify potential tremor events in daily drawing or writing activities. For future work, deep learning-based techniques will be used to extract features of the tremor in real-time. Overall, I designed and applied learning-based methods across different fields to solve real-world problems. The results show that combining learning methods with domain knowledge enables the formation of solutions

    Automated Static Warning Identification via Path-based Semantic Representation

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    Despite their ability to aid developers in detecting potential defects early in the software development life cycle, static analysis tools often suffer from precision issues (i.e., high false positive rates of reported alarms). To improve the availability of these tools, many automated warning identification techniques have been proposed to assist developers in classifying false positive alarms. However, existing approaches mainly focus on using hand-engineered features or statement-level abstract syntax tree token sequences to represent the defective code, failing to capture semantics from the reported alarms. To overcome the limitations of traditional approaches, this paper employs deep neural networks' powerful feature extraction and representation abilities to generate code semantics from control flow graph paths for warning identification. The control flow graph abstractly represents the execution process of a given program. Thus, the generated path sequences of the control flow graph can guide the deep neural networks to learn semantic information about the potential defect more accurately. In this paper, we fine-tune the pre-trained language model to encode the path sequences and capture the semantic representations for model building. Finally, this paper conducts extensive experiments on eight open-source projects to verify the effectiveness of the proposed approach by comparing it with the state-of-the-art baselines.Comment: 17 pages, in Chinese language, 9 figure

    Global Cyber Attack Forecast using AI Techniques

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    The advancement of internet technology and growing involvement in the cyber world have made us prone to cyber-attacks inducing severe damage to individuals and organizations, including financial loss, identity theft, and reputational damage. The rapid emergence and evolution of new networks and new opportunities for businesses and technologies are increasing threats to security vulnerabilities. Hence cyber-crime analysis is one of the wide range applications of Data Mining that can be eventually used to predict and detect crime. However, there are several constraints while analyzing cyber-attacks, which are yet to be resolved for more accurate cyber security inspection. Although there are many strategies for intrusion detection, predicting upcoming cyber threats remains an open research challenge. Hence, this thesis seeks to utilize temporal correlations among attack frequencies within specific time periods to predict the future severity of cyber incidents. The research aims to address the current research limitations by introducing a real-time data collection framework that will provide up-to-date cyber-attack data. Furthermore, a platform for cyber-attack trend analysis has been developed using Power BI to provide insight into the current cyber-attack trend. A correlation was identified in the reported attack volume across consecutive time frames through collected attack data analysis. This thesis introduces a predictive model that forecasts the frequency of cyber-attacks within a specified time window, using solely a historical record of attack counts. The research includes various machine learning and deep learning methods to develop a prediction system based on multiple time frames with an over 15% improvement in accuracy compared to the conventional baseline model. Namely, our research demonstrates that cyber incidents are not entirely random, and by analyzing patterns and trends in past incidents, developed AI techniques can be used to improve cybersecurity measures and prevent future attacks
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