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检索条件"丛书名=IFIP Advances in Information and Communication Technology 461"

共 10561 条记录，以下是191-200 订阅

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Detecting Control Injection Attacks Using Energy Data Anomalies in Computer Numerical Control Machining 18th

Detecting Control Injection Attacks Using Energy Data Anomal...

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18th International Conference on Critical Infrastructure Protection

作者： Kimmell, Jeffrey Orlyanchik, Vladimir Sturm, Logan Dawson, Joel Taylor, Curtis Oak Ridge Natl Lab Oak Ridge TN 37831 USA Cybersecur Mfg Innovat Inst San Antonio TX 78249 USA

ISBN: (纸本)9783031818905;9783031818882;9783031818875

The widespread adoption of networked devices, sophisticated automation and data-driven processes in industry - also known as Industry 4.0 - has boosted the quantity and quality of manufacturing products. However, the benefits come with substantial increases in the attack surfaces of manufacturing processes and systems. In addition to affecting the quality and readiness of critical products, attacks against manufacturing processes and systems can have severe physical consequences, including human injury and death. This chapter presents the results of remote network-based control injection attacks on a computer numerical control mill. The focus is on the impacts of attacks during computer numerical control mill operation. A machine-agnostic, affordable and scalable solution for attack monitoring is developed by considering the physical effects of the attacks on wax workpieces. A simple threshold-based method for detecting attacks is demonstrated and its effectiveness is evaluated.

关键词： Manufacturing Cyber Security Attack Detection Power Analysis Attack Quantification

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Actuation Conflict Management in Internet of Things Systems DevOps: A Discrete Event Modeling and Simulation Approach 7th

Actuation Conflict Management in Internet of Things Systems ...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Capocchi, Laurent Santucci, Jean-Francois Tigli, Jean-Yves Gomnin, Thibault Lavirotte, Stephane Rocher, Gerald Univ Cors SPE UMR CNRS 6134 Campus Mariani F-20250 Corte France Campus SophiaTech I3S UMR CNRS 7271 Campus SophiaTech F-06903 Sophia Antipolis France

ISBN: (纸本)9783031819025;9783031819001;9783031818998

In IoT DevOps, simulating actuation conflict management is crucial for enhancing conflict detection and resolution in concurrent IoT applications. This paper introduces a new discrete event modeling and simulation approach for IoT systems during the design phase. Its objectives are to identify potential conflicts arising from competing smart applications accessing shared actuators or physical properties and to validate actuation conflict management specifications aimed at resolving conflicts among concurrent access attempts to IoT devices. The formalism of discrete event system specification is employed to model IoT systems formally, incorporating an actuation conflict management simulation model. This aids designers in the resolution of actuation conflicts.

关键词： Actuation Conflicts Discrete-Event Internet of Things Modeling Discrete-Event Simulation

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Fingerprinting Malware Families Under Uncertainty 20th

Fingerprinting Malware Families Under Uncertainty

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20th ifip WG 11.9 International Conference on Digital Forensics

作者： Dunn, Cayden Ghosh, Krishnendu Coll Charleston Charleston SC 29424 USA

ISBN: (纸本)9783031710278;9783031710254;9783031710247

The majority of attacks on the information technology infrastructure are executed by sets of malware that are variants of each another. Therefore, it is critical to identify malware and their variants based on their behavior captured in trace data. This chapter presents a methodology for grouping malware and their variants based on similar behavior using traces that are imprecise and incomplete. Inspired by biological sequence analysis, the methodology represents traces as discretetime Markov chains. Kullback-Leibler divergence and Jensen-Shannon divergence are computed as similarity metrics for pairwise comparisons of the discrete-time Markov chains and edge-labeled graphs based on the traces and similarities are constructed. Following this, minimum spanning tree and community detection algorithms are successively applied to the edge-labeled graphs to construct malware families. The features extracted from the malware families and their variants are employed in machine learning models for automated malware detection and classification. The results of experiments conducted to validate the methodology demonstrate its efficacy at fingerprinting malware families.

关键词： Malware Fingerprinting Discrete-Time Markov Chain Minimum Spanning Tree Unsupervised Learning Community Detection

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Harnessing Generative AI for Sustainable Innovation: A Comparative Study of Prompting Techniques and Integration with Nature-Inspired Principles 24th

Harnessing Generative AI for Sustainable Innovation: A Compa...

引用

24th International TRIZ Future Conference

作者： Mas'udah Livotov, Pavel Kokoschko, Bjoern Offenburg Univ Appl Sci Badstr 24 D-77652 Offenburg Germany Otto von Guericke Univ Univ Pl 2 D-39106 Magdeburg Germany

ISBN: (纸本)9783031759215;9783031759192;9783031759185

Amidst growing environmental challenges and the imperative for sustainable solutions, this study explores how generative artificial intelligence (AI) can drive innovation in process engineering. It investigates the effectiveness of different prompting techniques and their integration with nature-inspired principles (NIP) in fostering sustainable innovation. The study employs a comparative methodology to assess the effectiveness of two distinct prompting techniques: basic and AI-automated prompting. It also examines the influence of integrating NIP derived from various natural ecosystems on the generated solutions. Experiments were conducted using a generative AI model and analysing the output, focusing on the number of unique and overlapping ideas. Furthermore, the quality of AI-generated solution concepts was evaluated using four parameters, such as feasibility, novelty, usefulness, and sustainability, each rated on a scale of 0 to 2. Three case studies within the process engineering domain were used, each representing a different problem-solving scenario. The results showed that the integration of NIP, particularly through the "one by one" strategy in AI-automated prompting, significantly increased the number of unique ideas compared to basic prompting, demonstrating its effectiveness in enhancing idea diversity and quality. Concepts generated through this approach also scored higher in novelty and sustainability, aligning with sustainable innovation goals. These findings have practical implications for developing innovative and sustainable engineering solutions, particularly in the early phases of design, offering insights into effective strategies for leveraging AI in eco-innovation.

关键词： Generative AI Sustainable Innovation Prompting Techniques Nature-Inspired Principles Idea Generation

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Prompting and Learning to Detect Major Life Events from Tweets 24th

Prompting and Learning to Detect Major Life Events from Twee...

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24th International TRIZ Future Conference

作者： Marginean, Anca Barcau, Emanuel Tech Univ Cluj Napoca Cluj Napoca Romania Life Hard Software Co Floresti Italy

ISBN: (纸本)9783031759253;9783031759239;9783031759222

Recently, LLM spervaded the world of text understanding. Their ability to generate text based on instructions comes with the price of having an extremely large number of parameters and, consequently extremely large hardware demands or inference times. We consider that certain classification tasks, such as the detection of fixed lists of events from text can be achieved with acceptable performance even with smaller language models. The longstanding issue of collecting and annotating text is significantly eased by the presence of LLMs. Therefore, we employ a prompting technique to create a synthetic dataset of tweets annotated with events from the speaker's life and train a small language model on it.

关键词： Major Life Events Detection Language Models Dataset Creation Generative Models Prompt Engineering

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Error Resiliency and Adversarial Robustness in Convolutional Neural Networks: An Empirical Analysis 7th

Error Resiliency and Adversarial Robustness in Convolutional...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Barbareschi, Mario Barone, Salvatore Casola, Valentina Della Torca, Salvatore Univ Naples Federico II I-80125 Naples Italy Univ Bergamo I-24044 Dalmine Italy

ISBN: (纸本)9783031819025;9783031819001;9783031818998

The increasing pervasiveness of Artificial Intelligence (AI), and Convolutional Neural Networks (CNNs) in edge-computing and Internet of Things applications pose several challenges, including the hunger for computational and power resources of predictive models, and their robustness w.r.t. security threats, e.g., adversarial attacks. As for the former, the approximate computing emerged as one of the most promising solutions to lower the computational effort of AI, since the output of approximate application is usually barely distinguishable from the exact one. Nevertheless, alterations to predictive models through approximation may actually jeopardize inner characteristics of CNNs, such as their adversarial robustness, that is their ability to discern legitimate inputs from systematically crafted malicious ones. In this paper, we investigate the vulnerability of the approximate CNNs to adversarial attacks. Specifically, we target approximate CNNs while resorting to different adversarial attacks in an aversion scenario, and we empirically prove approximation may actually compromise adversarial robustness.

关键词： Convolutional Neural Networks Approximate Computing Adversarial Attacks

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An information-Theoretic Approach for Anomaly Detection in RPL-Based Internet of Things 7th

An Information-Theoretic Approach for Anomaly Detection in R...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： La, Vinh Hoa de Oca, Edgardo Montes Cavalli, Ana Montimage 39 Rue Bobillot F-75013 Paris France

ISBN: (纸本)9783031819025;9783031819001;9783031818998

In recent years, cyber-attacks have increased significantly in both volume and sophistication, making the detection of security violations a crucial feature in computer systems. This is particularly true in the Internet of Things (IoT), where devices are vulnerable to failures and malicious attacks due to their resource-constrained nature. Given the proliferation of new security threats, anomaly-based detection approaches are essential for intrusion detection and prevention systems to effectively defend against attackers. This paper proposes an information-theoretic approach based on entropy to establish an anomaly detection model. A real case study in IoT networks based on Routing Over Low power and Lossy networks (RPL) illustrates the application of the proposed approach. Preliminary experimental results demonstrate that our method is both practical and extendable.

关键词： Intrusion Detection Anomaly Detection 6LoWPAN Wireless Sensor Networks IoT Security Monitoring

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Neuro-Symbolic AI-Driven Inventive Design of a Benzoic Acid Extraction Installation from Styrax Resin 24th

Neuro-Symbolic AI-Driven Inventive Design of a Benzoic Acid ...

引用

24th International TRIZ Future Conference

作者： Brad, Stelian Bartos, Vasile-Dragos Brad, Emilia Trifan, Costan-Vladut Tech Univ Cluj Napoca Cluj Napoca 400114 Romania

ISBN: (纸本)9783031759215;9783031759192;9783031759185

The extraction of benzoic acid from natural resins such as Styrax holds considerable industrial significance, given its widespread use in pharmaceuticals, food, and cosmetics. This study introduces an approach to enhance the extraction process by designing a novel installation in this respect. The design roadmap integrates Generative AI with neuro-symbolic AI algorithms. We employ a neuro-symbolic AI framework that merges AI's generative capabilities for initial design conceptualization with symbolic reasoning, enriched with TRIZ principles and Complex Systems Design Thinking (CSDT) methodologies. This combination aids in navigating complex problem-solving scenarios and promoting an innovative solution. Environmental issues are integrated throughout the design process to ensure that the solution also meets eco-sustainability objectives. Results indicate that the novel design markedly enhances the extraction efficiency of benzoic acid, reduces energy consumption, and lowers waste production. The design's adaptability for industrial applications has been validated, with future enhancements aimed at incorporating real-time monitoring AI systems for dynamic adjustments based on raw material variability.

关键词： Generative AI Neuro-Symbolic AI TRIZ Complex system Design Technique Benzoic Acid Extraction Sustainable Design Algorithmic Design Eco-Innovation Renewable Resources Process Optimization

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Relocation of Container-Based Services in a MEC-NFV Orchestrated Environment 7th

Relocation of Container-Based Services in a MEC-NFV Orchestr...

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7th ifip WG 5.5 International Cross-Domain Conference on Internet of Things

作者： Bernad, Cristina Kjorveziroski, Vojdan Roig, Pedro Alcaraz, Salvador Gilly, Katja Filiposka, Sonja Miguel Hernandez Univ Dept Comp Engn Elche Spain Ss Cyril & Methodius Univ Fac Comp Sci & Engn Skopje North Macedonia

ISBN: (纸本)9783031819025;9783031819001;9783031818998

With the rapid growth of real-time next-generation mobile services, it has become necessary to work towards holistic orchestration of the benefits promised with edge computing based on bringing the computing infrastructure closer to the end user. While the concept of Multi-access Edge Computing (MEC) integrated with Network Function Virtualisation (NFV) is being standardised, there is still a lot of work to be done to orchestrate the relocation of edge applications integrated in 5G and beyond systems in a smooth and efficient manner. In this paper, we document the current status of the transparent relocation of edge services in an experimentally deployed MEC-NFV environment based on OSM. Working towards gathering monitoring training datasets necessary for the development of proactive MEC application orchestrators that will implement seamless follow-me behaviour for MEC services, we provide benchmark results for the service downtime of three potential MEC services hosted in lightweight containers. Our analysis of results shows that containers exhibit improved performance over that of virtual machines, but there are still some issues that require improvement in both the orchestration implementation as well at the relocation process for containers.

关键词： Edge Computing Optimisation Standardisation Migration Mobile nodes Containers

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Text File Recovery Using an N-Gram Model 20th

Text File Recovery Using an N-Gram Model

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20th ifip WG 11.9 International Conference on Digital Forensics

作者： Srinivas, Kaparthi Gireesh, Chalicheemala Chunduru, Eswara Sai Prasad Temberveni, Venugopal Vasavi Coll Engn Hyderabad India Cent Forens Sci Lab Hyderabad India JNTUH Univ Coll Engn Jagtial India

ISBN: (纸本)9783031710278;9783031710254;9783031710247

This chapter describes a file carving method for recovering deleted text files without relying on file table information. Specifically, text files are reconstructed by analyzing file fragments and utilizing three n-gram language models, the absolute discounting interpolated trigram language model, absolute discounting interpolated bigram language model and Laplace bigram language model. The method selects text files and segments them into blocks that are copied to a memory buffer in random order to simulate the file allocation process. Graph theory is employed to formulate the text file recovery problem with clusters as nodes and probabilities of successor clusters as edges. The absolute discounting interpolated trigram language model yielded the highest average accuracy of 79.19%. The results underscore the effectiveness of leveraging linguistic patterns inherent in fragmented data to restore lost text content.

关键词： File Carving N-Gram Language Model Text File Recovery NLTK Library Greedy Algorithms

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