1,363 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.

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    Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (ForlĂŹ Campus) in collaboration with the Romagna Chamber of Commerce (ForlĂŹ-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices

    Implementation of ISO Frameworks to Risk Management in IPv6 Security

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    The Internet of Things is a technology wave sweeping across various industries and sectors. It promises to improve productivity and efficiency by providing new services and data to users. However, the full potential of this technology is still not realized due to the transition to IPv6 as a backbone. Despite the security assurances that IPv6 provides, privacy and concerns about the Internet of Things remain. This is why it is important that organizations thoroughly understand the protocol and its migration to ensure that they are equipped to take advantage of its many benefits. Due to the lack of available IPv4 addresses, organizations are in an uncertain situation when it comes to implementing IoT technologies. The other aim is to fill in the gaps left by the ISO to identify and classify the risks that are not yet apparent. The thesis seeks to establish and implement the use of ISO to manage risks. It will also help to align security efforts with organizational goals. The proposed solution is evaluated through a survey that is designed to gather feedback from various levels of security and risk management professionals. The suggested modifications are also included in the study. A survey on the implementation of ISO frameworks to risk management in IPv6 was conducted and with results as shown in the random sampling technique that was used for conducting the research a total of 75 questionnaires were shared online, 50 respondents returned responses online through emails and social media platforms. The result of the analysis shows that system admin has the highest pooling 26% of all the overall participants, followed by network admin with 20%, then cybersecurity specialists with 16%. 14% of the respondents were network architects while senior management and risk management professionals were 4% and 2% respectively. The majority of the respondents agreed that risk treatment enhances the risk management performance of the IPv6 network resulting from the proper selection and implementation of correct risk prevention strategies

    Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study

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    This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives

    XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection

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    With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advantages of large-scale pre-training, harnessing vast raw data, and transfer learning by learning representations for training data through graph convolution. The research results demonstrate that the XGV-BERT method significantly improves vulnerability detection accuracy compared to two existing methods such as VulDeePecker and SySeVR. For the VulDeePecker dataset, XGV-BERT achieves an impressive F1-score of 97.5%, significantly outperforming VulDeePecker, which achieved an F1-score of 78.3%. Again, with the SySeVR dataset, XGV-BERT achieves an F1-score of 95.5%, surpassing the results of SySeVR with an F1-score of 83.5%

    20th SC@RUG 2023 proceedings 2022-2023

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    SUTMS - Unified Threat Management Framework for Home Networks

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    Home networks were initially designed for web browsing and non-business critical applications. As infrastructure improved, internet broadband costs decreased, and home internet usage transferred to e-commerce and business-critical applications. Today’s home computers host personnel identifiable information and financial data and act as a bridge to corporate networks via remote access technologies like VPN. The expansion of remote work and the transition to cloud computing have broadened the attack surface for potential threats. Home networks have become the extension of critical networks and services, hackers can get access to corporate data by compromising devices attacked to broad- band routers. All these challenges depict the importance of home-based Unified Threat Management (UTM) systems. There is a need of unified threat management framework that is developed specifically for home and small networks to address emerging security challenges. In this research, the proposed Smart Unified Threat Management (SUTMS) framework serves as a comprehensive solution for implementing home network security, incorporating firewall, anti-bot, intrusion detection, and anomaly detection engines into a unified system. SUTMS is able to provide 99.99% accuracy with 56.83% memory improvements. IPS stands out as the most resource-intensive UTM service, SUTMS successfully reduces the performance overhead of IDS by integrating it with the flow detection mod- ule. The artifact employs flow analysis to identify network anomalies and categorizes encrypted traffic according to its abnormalities. SUTMS can be scaled by introducing optional functions, i.e., routing and smart logging (utilizing Apriori algorithms). The research also tackles one of the limitations identified by SUTMS through the introduction of a second artifact called Secure Centralized Management System (SCMS). SCMS is a lightweight asset management platform with built-in security intelligence that can seamlessly integrate with a cloud for real-time updates

    Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices

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    Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time requirements, power constraints, etc.). This required the development of specific solutions and development tools for what is now referred to as TinyML. In this dissertation, we focus on improving the deployment and performance of TinyML applications, taking into consideration the aforementioned challenges, especially memory requirements. This dissertation contributed to the construction of the Edge Learning Machine environment (ELM), a platform-independent open-source framework that provides three main TinyML services, namely shallow ML, self-supervised ML, and binary deep learning on constrained devices. In this context, this work includes the following steps, which are reflected in the thesis structure. First, we present the performance analysis of state-of-the-art shallow ML algorithms including dense neural networks, implemented on mainstream microcontrollers. The comprehensive analysis in terms of algorithms, hardware platforms, datasets, preprocessing techniques, and configurations shows similar performance results compared to a desktop machine and highlights the impact of these factors on overall performance. Second, despite the assumption that TinyML only permits models inference provided by the scarcity of resources, we have gone a step further and enabled self-supervised on-device training on microcontrollers and tiny IoT devices by developing the Autonomous Edge Pipeline (AEP) system. AEP achieves comparable accuracy compared to the typical TinyML paradigm, i.e., models trained on resource-abundant devices and then deployed on microcontrollers. Next, we present the development of a memory allocation strategy for convolutional neural networks (CNNs) layers, that optimizes memory requirements. This approach reduces the memory footprint without affecting accuracy nor latency. Moreover, e-skin systems share the main requirements of the TinyML fields: enabling intelligence with low memory, low power consumption, and low latency. Therefore, we designed an efficient Tiny CNN architecture for e-skin applications. The architecture leverages the memory allocation strategy presented earlier and provides better performance than existing solutions. A major contribution of the thesis is given by CBin-NN, a library of functions for implementing extremely efficient binary neural networks on constrained devices. The library outperforms state of the art NN deployment solutions by drastically reducing memory footprint and inference latency. All the solutions proposed in this thesis have been implemented on representative devices and tested in relevant applications, of which results are reported and discussed. The ELM framework is open source, and this work is clearly becoming a useful, versatile toolkit for the IoT and TinyML research and development community

    Security and Authenticity of AI-generated code

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    The intersection of security and plagiarism in the context of AI-generated code is a critical theme through- out this study. While our research primarily focuses on evaluating the security aspects of AI-generated code, it is imperative to recognize the interconnectedness of security and plagiarism concerns. On the one hand, we do an extensive analysis of the security flaws that might be present in AI-generated code, with a focus on code produced by ChatGPT and Bard. This analysis emphasizes the dangers that might occur if such code is incorporated into software programs, especially if it has security weaknesses. This directly affects developers, advising them to use caution when thinking about integrating AI-generated code to protect the security of their applications. On the other hand, our research also covers code plagiarism. In the context of AI-generated code, plagiarism, which is defined as the reuse of code without proper attribution or in violation of license and copyright restrictions, becomes a significant concern. As open-source software and AI language models proliferate, the risk of plagiarism in AI-generated code increases. Our research combines code attribution techniques to identify the authors of AI-generated insecure code and identify where the code originated. Our research emphasizes the multidimensional nature of AI-generated code and its wide-ranging repercussions by addressing both security and plagiarism issues at the same time. This complete approach adds to a more profound understanding of the problems and ethical implications associated with the use of AI in code generation, embracing both security and authorship-related concerns

    Urbanised forested landscape: Urbanisation, timber extraction and forest care on the Vișeu Valley, northern Romania

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    By looking at urbanisation processes from the vantage point of the forest, and the ways in which it both constitutes our living space while having been separated from the bounded space of the urban in modern history, the thesis asks: How can we (re)imagine urbanisation beyond the limits of the urban? How can a feminine line of thinking engage with the forest beyond the capitalist-colonial paradigm and its extractive project? and How can we “think with care” (Puig de la Bellacasa 2017) towards the forest as an inhabitant of our common world, instead of perpetuating the image of the forest as a space outside the delimited boundaries of the city? Through a case study research, introducing the Vișeu Valley in northern Romania as both a site engaged in the circulation of the global timber flow, a part of what Brenner and Schmid (2014) name “planetary urbanisation”, where the extractive logging operations beginning in the late XVIIIth century have constructed it as an extractive landscape, and a more than human landscape inhabited by a multitude of beings (animal, plant, and human) the thesis argues towards the importance of forest care and indigenous knowledge in landscape management understood as a trans-generational transmission of knowledge, that is interdependent with the persistence of the landscape as such. Having a trans-scalar approach, the thesis investigates the ways in which the extractive projects of the capitalist-colonial paradigm have and still are shaping forested landscapes across the globe in order to situate the case as part of a planetary forest landscape and the contemporary debates it is engaged in. By engaging with emerging paradigms within the fields of plant communication, forestry, legal scholarship and landscape urbanism that present trees and forests as intelligent beings, and look at urbanisation as a way of inhabiting the landscape in both indigenous and modern cultures, the thesis argues towards viewing forested landscapes as more than human living spaces. Thinking urbanisation through the case of the Vișeu Valley’s urbanised forested landscape, the thesis aligns with alternate ways of viewing urbanisation as co-habitation with more than human beings, particularly those emerging from interdisciplinary research in the Amazon river basin (Tavares 2017, Heckenberger 2012) and, in light of emerging discourses on the rights of nature, proposes an expanded concept of planetary citizenship, to include non-human personhood
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