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An Introduction to Threat Modelling: Modelling Steps, Model Types, Benefits and Challenges

An Introduction to Threat Modelling: Modelling Steps, Model ...

引用

2024 International Conference on Computing, Communication, Cybersecurity and AI

作者： Naik, Nitin Jenkins, Paul Grace, Paul Naik, Dishita Prajapat, Shaligram Song, Jingping Aston Univ Sch Comp Sci & Digital Technol Birmingham W Midlands England Cardiff Metropolitan Univ Cardiff Sch Technol Cardiff Wales Birmingham City Univ Birmingham W Midlands England Devi Ahilya Univ Int Inst Profess Studies Indore India Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9783031744426;9783031744433

The proliferation of cybersecurity threats is posing substantial security risks to organisations;therefore, it requires robust countermeasures and defence mechanisms for organisational IT systems, applications and data. Threat modelling is a process of identifying, analysing, prioritising and mitigating threats and their associated vulnerabilities in a system or network. Understanding the threat modelling process, as well as its benefits and limitations, whilst selecting an appropriate threat modelling method that may assist cybersecurity experts in their comprehensive security assessments. The assessments are designed to uncover security gaps and potential threats, to develop robust countermeasures against these potential threats and strengthening the security of organisational IT systems, applications and data. This paper will present a comprehensive study concerning threat modelling including the phases involved in threat modelling, types of threat models and benefits and challenges of threat modelling. Therefore, this comprehensive study concerning threat modelling will simplify the essential terminologies of threat modelling to users in a clear and concise manner.

关键词： Threat Modelling Threat Models Cyberattack Cyberthreat Cyber Threat STRIDE Model DREAD Model VAST Model PASTA Model OCTAVE Model LINDDUN Model

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An Integrated Analysis for Identifying Iconic Gestures in Human-Robot Interactions 1

引用

Intelligent systems Conference (IntelliSys)

作者： Singh, Aditi Bansal, Arvind K. Kent State Univ Dept Comp Sci Kent OH 44242 USA

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

Co-speech human gesture analysis is an important aspect for social conversational interactions involving human-robot interfaces. Co-speech gestures require synchronous integration of speech, human posture, and motions. Iconic gestures are amajor subclass of co-speech gestures that express entities and actions by their attributes such as shape-contours, magnitude, and proximity using the synchronous motions of fingers, palms, and spoken phrases. The attributes of entities and actions correlate directly with the displayed contours. In this research, we describe an integrated technique that combines motion analysis to derive contours, synchronization of motion with speech to identify words corresponding to iconic gestures, and conceptual dependency of action words to drive iconic gestures. This technique models motion-sketched contour as a combination of synchronous color Petri net extended to model composite motions and contour-segment patterns. We present high-level algorithms and the corresponding implementation for the proposed technique and evaluate its performance. Performance results show approximately 90% recognition of simple contours, including closed contours.

关键词： Co-speech gesture Contour detection Human-robot interaction Iconic gesture Synchronization Synchronous colored Petri net

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Wind Turbine Surface Damage Detection Using YOLOv7 with Slicing Aided Hyper Inference (SAHI)

Wind Turbine Surface Damage Detection Using YOLOv7 with Slic...

引用

Intelligent systems Conference (IntelliSys)

作者： Best, Oscar Khan, Asiya Gianni, Mario Sharma, Sanjay Collins, Keri Univ Plymouth Sch Engn Comp & Math SECaM Plymouth England Univ Liverpool Dept Comp Sci Liverpool England

ISBN: (纸本)9783031477232;9783031477249

This paper expands on the publicly available dataset of wind turbine surface damage and uses this dataset to finetune all P5 models for both YOLOv5 and YOLOv7 object detection frameworks. YOLOv7 outperformed YOLOv5, with the YOLOv7x model achieving the highest recall score and best F1-confidence. This model was therefore selected for inference on both images and video of wind turbines. Slicing Aided Hyper Inference (SAHI) has also been used to improve detection capability for smaller instances of damage. The model was further evaluated on a dataset collected from a scaled model of a wind turbine, with hand drawn damages. Lastly, this dataset was used for inference using SAHI, which showed slight improvement for detecting damage instances. More accurate results were observed when evaluating the model on real damage examples compared to simulated damage.

关键词： Computer Vision Machine Learning Wind Turbine Inspection

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Explainable Prediction of Machine-Tool Breakdowns Based on Combination of Natural Language Processing and Classifiers 1

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Intelligent systems Conference (IntelliSys)

作者： Ben Ayed, Maha Soualhi, Moncef Mairot, Nicolas Giampiccolo, Sylvain Ketata, Raouf Zerhouni, Noureddine Univ Franche Comte CNRS Femto St Supmicrotech ENSMM 24 Rue Alain Savary F-25000 Besanon France SCODER 1 Rue Foret ZA Oree Bois F-25480 Pirey France Natl Inst Appl Sci & Technol Tunis Northern Urban Ctr Tunis 1080 Tunisia

ISBN: (数字)9783031477188

ISBN: (纸本)9783031477171;9783031477188

Prognostics and Health Management (PHM) process has been developed to enhance predictive maintenance (PM) policies and decision support (DS). One of the PHM modules is fault diagnostics, it allows identifying and predicting future faults. Among diagnostic techniques, one can find Natural Language Processing (NLP) that can be used to exploit textual monitoring data such as logging data for fault prediction. However, there exists some unstructured texts that reduce the data quality and provide an un-explainable prediction of faults. To remedy this situation, this paper proposes a NLP methodology for system breakdown prediction. This methodology starts by cleaning textual data. Then, cleaned data and their labels, which represent the breakdown origin, are injected into feature encoding models. These two previous steps address special and redundant characters and non-standard spelling terms. Thus, they allow classifier models to learn mapping input texts to their corresponding labels without confusion for the fault prediction, making these predictions explainable. The proposed methodology is applied to real logging data carried out from a machine tool of a French company SCODER. The machine tool generates six failure labels that classifiers learn to predict. The prediction accuracy obtained by the proposed methodology, compared to existing methods, is promising and can be useful for a failure prognostics.

关键词： Prognostics and health management Natural language processing Data quality Feature encoding Machine learning Machine-tools

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From Open Defecation to Digital Empowerment: A Participatory IoT Solution for a Rural South Indian Village 8th

From Open Defecation to Digital Empowerment: A Participatory...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Kulkarni, Pradyumna P. Santhosh, Athira Kamath, Ekta Girish Okyere, Edmond Gyaidoo Dharani, Bathula Achutha Priyanshi, Atta Greshma Mukhopadhyay, Adwitiya Muruganandam, Narendran Sobanapuram Nandanan, Krishna Twist, Rondine Amrita Vishwa Vidyapeetham Amrita Sch Comp Mysuru India Amrita Vishwa Vidyapeetham Amrita Sch Engn Bengaluru India Amrita Vishwa Vidyapeetham Amrita Sch Sustainable Futures Amritapuri India

ISBN: (纸本)9789819713257;9789819713264

Open defecation remains a matter of overarching concern in developing economies, leading to a significant number of inadvertent healthcare encumbrances and preventable deaths. These problems call for rigorous examination, particularly in cases where infrastructure for sanitation is available, but underutilized. This study explored the age-old practice of open defecation in the rural Indian village of Kurvakurdha (Karnataka state), despite the provision of community-built toilets. The research focuses on participatory methods, specifically Participatory Rural Appraisal (PRA), to engage the community in the consequences of unsanitary practices and related challenges. The findings of the study revealed a high prevalence of contagious diseases, including typhoid, malaria, anemia, and skin problems, traceable to the open defecation practice. Furthermore, the water sample tests have indeed detected the presence of E. coli bacteria in the Krishna River, a source that the community relies on for both domestic and irrigation purposes. Building upon these findings, the authors propose an Internet of Things (IoT)-based integrated toilet addressing the open defecation challenge, aimed at alleviating the associated sanitation challenges in the Kurvakurdha community. In conclusion, this research presents a seminal sustainable model for rural development through health and sanitation management. The study emphasizes the importance of community engagement in addressing complex societal challenges and leveraging scientific and technological advancements to implement smart sanitation solution.

关键词： Live-in-Labs (R) Open defecation free (ODF) Sustainable development Sanitation Rural development SDG 6 IoT

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Predicting the Evolution of Cancer Stem Cell Subtypes Using a Machine Learning Framework 2nd

Predicting the Evolution of Cancer Stem Cell Subtypes Using ...

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2nd Serbian International Conference on Applied Artificial Intelligence (SICAAI)

作者： Nikolic, D. Ljujic, B. Hamzagic, A. Ramovic Jankovic, M. Gazdic Miric, A. Virijevic, K. Seklic, D. Jovanovic, M. Kastratovic, N. Petrovic, I. Jurisic, V. Milivojevic, N. Zivanovic, M. Filipovic, N. Univ Kragujevac Inst Informat Technol Jovana Cvijica bb Kragujevac 34000 Serbia Univ Kragujevac BioIRC Bioengn Res & Dev Ctr Prvoslava Stojanovica 6 Kragujevac 34000 Serbia Univ Kragujevac Fac Med Sci Svetozara Markovica 69 Kragujevac 34000 Serbia Univ Kragujevac Fac Sci Radoja Domanovica 12 Kragujevac 34000 Serbia Univ Kragujevac Fac Engn Sestre Janjic 6 Kragujevac 34000 Serbia

ISBN: (纸本)9783031608391;9783031608407

Cancer stem cells (CSCs) are subpopulation of cells in a tumor that are very important for analysis and treatment in clinical practice. The aim of this study is the use of Machine learning (ML) methodology to predict the development of CSCs subpopulation in colon and breast cancer cells. Input data for training Genetic algorithm (GA) and fitting was used from experimental measurements on flow cytometry of CSCs surface markers expression in cancer cells. Based on the results, GA prediction model has archived high accuracy in estimating the expression rate of CSCs markers on cancer cells. Artificial intelligence can be used as a powerful tool for predicting of behavior of cancer stem cell subpopulation.

关键词： Machine learning Genetic Algorithm Cancer Stem Cells Flow cytometry

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An ML-Based Quality Features Extraction (QFE) Framework for Android Apps 1

引用

11st World Conference on Information systems and Technologies (WorldCIST)

作者： Chand, Raheela Khan, Saif Ur Rehman Hussain, Shahid Wang, Wen-Li COMSATS Univ Islamabad CUI Dept Comp Sci Islamabad Pakistan Penn State Univ Sch Engn Dept Comp Sci & Software Engn Erie PA USA

ISBN: (数字)9783031456510

ISBN: (纸本)9783031456503;9783031456510

Context: The generic quality attributes fail to comprehend the current state-of-the-art challenges and constraints of mobile apps. Objectives: The goal of this study is to fill the gap in the systematic procedures to identify and extract specific quality features relevant to Android apps. Method: To accomplish the objective, we have proposed an ML-based Quality Features Extraction (QFE) framework for Android apps. QFE analyzes, parses, and gains insights from use reviews utilizing Natural Language Processing (NLP), Sentimental Analysis, Topic Modelling, and Lexical Semantics. Results: This study was tested on three different datasets and QFE successfully discovered 23 unique Androidspecific quality features. Moreover, a comparative study with related studies was conducted and the analysis delineates that QFE provides a more reliable, efficient, and easy-to-use approach. Contribution: Briefly, (i) an ML-based empirical framework is proposed for discovering quality features for Android apps;(ii) the popular Topic Modelling technique is enhanced by RBLSALT, that is to automate the manual process of labeling topics in Topic Modelling;and finally, (iii) the pseudo-code and Python implemented notebook of the framework is also given to provide ease in the applicability of QFE. Conclusion and Future Work: Future work, is planned to evaluate the framework by comparing it with different techniques of feature extraction and to propose a specific featuresoriented comprehensive quality model based on Android apps.

关键词： Quality Framework Quality Features Topic Modelling Feature Extraction Android Games

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Anomalous EEG Signal Time Series Classification Using Modified Metaheuristic Optimized RNN 5th

Anomalous EEG Signal Time Series Classification Using Modifi...

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Bacanin, Nebojsa Jovanovic, Luka Toskovic, Ana Zivkovic, Miodrag Petrovic, Aleksandar Antonijevic, Milos Singidunum Univ Danijelova 32 Belgrade 11000 Serbia Univ Pristina Kosovska Mitrovica Teacher Educ Fac Pristina Serbia

ISBN: (纸本)9789819720811;9789819720828

This study delves into the significance of employing the electroencephalogram (EEG) as a noninvasive epilepsy detection method, emphasizing the potential for improvement through the integration of artificial intelligence (AI) techniques. The conventional EEG, while an invaluable tool, presents opportunities for enhancement in terms of cost-effectiveness, precision, and the specialized expertise required for its interpretation. The research at hand proposes the utilization of a particle swarm optimization (PSO) to optimize recurrent neural networks (RNNs) in the context of epilepsy prediction within a multivariate time series framework. By harnessing the power of metaheuristic optimizers, this study demonstrates their effectiveness in fine-tuning the hyperparameters of RNNs for the precise classification of seizures in EEG data. The results are promising, with the best performing models achieving a remarkable accuracy rate exceeding 99.8% when tested on previously unseen data. This research not only underscores the potential of AI-driven EEG analysis in epilepsy detection but also contributes to addressing the existing challenges associated with traditional EEG methodologies.

关键词： Electroencephalogram Recurrent neural networks Particle swarm optimization Hybridization Medical diagnosis

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A Soft Robotic Gripper for Pancake Handling: Design, Modelling, and Experimental Assessment 27th

A Soft Robotic Gripper for Pancake Handling: Design, Modelli...

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27th International Conference on Climbing and Walking Robots and Mobile Machine Support Technologies (CLAWAR)

作者： Chan, Ser Vin Sohani, Behnaz Goher, Khaled Univ Nottingham Fac Engn Nottingham England Univ Lincoln Sch Engn Lincoln England Sultan Qaboos Univ Coll Engn Muscat Oman

ISBN: (纸本)9783031713002;9783031713019

This paper studies the design and assessment of an innovative soft-rigid robotic gripper for handling pancakes with a focus on achieving a high success rate in gripping while preserving the pancake's original shape. Being a hybrid softrigid design, the gripper benefits from being flexible so as not to damage the pancake and has enough rigidity to grip a stack of pancakes at a time. The gripper is designed to be versatile, accommodating different pancake sizes through part exchanges, and offers convenient independent replacement of the finger without requiring the entire gripper to be replaced. The gripper is manufactured using 3D printing, exploiting Flexible 80 A for its flexible components and polylactic acid (PLA) for the rigid elements. Particularly, the gripper features a shovel-like fingertip design, aimed at gently scooping pancakes to prevent undesired compression caused by excessive pressure. To assess the design's feasibility and functionality, finite element analysis (FEA) is conducted. The gripper is experimentally validated using various types of pancakes as well as using FEA to assess its capabilities. Experimental trials involving real pancakes were conducted to evaluate the gripper's performance to hold efficiently single and stacks.

关键词： Soft robotic gripper Finite element analysis (FEA) Independent dfinger replacement 3D printing technology

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Economic Power: Security, Military and Political Resource 10th

Economic Power: Security, Military and Political Resource

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Jovetic, Ivan Katnic, Ivana Univ Donja Gorica Fac Int Econ Finance & Business Oktoih 1 Podgorica 81000 Montenegro

ISBN: (纸本)9783031662706;9783031662713

Economic power impacts all other forms of power, primarily military and political. Moreover, history of civilizations is actually history of interrupted wars. It seems, respectively, that quest for wealth, money and life quality has been the engine of technological, military and historical processes. Economic wealth and in particular quest for it (re)shaped technological and military power but also the war and its forms. Capitalism as the system enabled respective even more as it networks science, industry and military technology. The economy (re)shapes technology and research while they impact advances in technology and trade which form political-military power. Consequentially, aforementioned (re)shapes also the notion, context as well as security itself. Economy should be seen as the security resource in both directions.

关键词： economic power military power technology security economy resource

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