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Robust intrusion detection for network communication on the Internet of Things: a hybrid machine learning approach

Cluster Computing 2024年第7期27卷 9975-9991页

作者： Soltani, Nasim Rahmani, Amir Masoud Bohlouli, Mahdi Hosseinzadeh, Mehdi Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliu Taiwan Research and Innovation Department Petanux GmbH Bonn Germany Institute of Research and Development Duy Tan University Da Nang Viet Nam Department of Computer Science University of Human Development Sulaymaniyah Iraq

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Intrusion detection

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Investigating the Role of Metaphors in human-AI Conversation and Engagement 24

Investigating the Role of Metaphors in Human-AI Conversation...

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24th IEEE International Conference on Data Mining Workshops, ICDMW 2024

作者： Maheshwary, Pragati Liu, Qiawen Mao, Rui Carnegie Mellon University Human-Computer Interaction Institute United States University of Wisconsin-Madison Department of Psychology United States Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331530631

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages. © 2024 IEEE.

关键词： Modeling languages

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Granularity is crucial when applying differential privacy to text: An investigation for neural machine translation

Granularity is crucial when applying differential privacy to...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Vu, Doan Nam Long Igamberdiev, Timour Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

ISBN: (纸本)9798891761681

Applying differential privacy (DP) by means of the DP-SGD algorithm to protect individual data points during training is becoming increasingly popular in NLP. However, the choice of granularity at which DP is applied is often neglected. For example, neural machine translation (NMT) typically operates on the sentence-level granularity. From the perspective of DP, this setup assumes that each sentence belongs to a single person and any two sentences in the training dataset are independent. This assumption is however violated in many real-world NMT datasets, e.g., those including dialogues. For proper application of DP we thus must shift from sentences to entire documents. In this paper, we investigate NMT at both the sentence and document levels, analyzing the privacy/utility trade-off for both scenarios, and evaluating the risks of not using the appropriate privacy granularity in terms of leaking personally identifiable information (PII). Our findings indicate that the document-level NMT system is more resistant to membership inference attacks, emphasizing the significance of using the appropriate granularity when working with DP. © 2024 Association for Computational Linguistics.

关键词： Differential privacy

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Pseudo-Haptics for Weight Perception in VR: Controller vs. Bare Hand interactions with Tracking Delay and Vertical Offset

Pseudo-Haptics for Weight Perception in VR: Controller vs. B...

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Virtual Reality (ICVR), IEEE International Conference on

作者： Chiwoong Hwang Tiare Feuchtner Kaj Grønbæk Department of Computer Science Aarhus University Denmark Human-Computer Interaction Group Konstanz University Germany

ISBN: (数字)9798350364231

ISBN: (纸本)9798350364248

Training in virtual reality (VR) offers advantages such as a safe learning environment, good scalability, and cost saving. However, state-of-the-art simulations are limited in faithfully conveying real-world physical object properties such as weight without complex technologies and instrumentation of the user or environment, which may negatively impact learning. A potential solution is the application of pseudo-haptics: illusory haptic sensations induced by a mismatch between real and virtual hand positions. Researchers have proposed various pseudo-haptic techniques to alter the perception of weight in virtual environments, but their effectiveness with hand tracking compared to VR controllers is underexplored. Hence, we investigate impacts of the interaction method for two different types of pseudo-haptic feedback based on Tracking Delay and Vertical Offset. Our results indicate that pseudo-haptics can effectively facilitate the sensation of weight both with controllers and bare hands, whereby each has benefits: hand tracking can enhance body ownership, while controllers may improve perceived responsiveness of virtual environments. Also, pseudo-haptics with Tracking Delay may be preferable for repetitive lifting tasks, as Vertical Offset can be prone to higher physical demand. The current study provides insights into the design of pseudo-haptic feedback and interaction technologies for weight perception in VR.

关键词： Hands Training Manufacturing industries Solid modeling Scalability Instruments Virtual environments User experience Delays Haptic interfaces

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Rethinking (human) Bodies in HRI: Embodied interactions Every Day 6

Rethinking (Human) Bodies in HRI: Embodied Interactions Ever...

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6th Social Robots with AI: Prospects, Risks, and Responsible Methods Robophilosophy, RP 2024

作者： Fantasia, Valentina Winkle, Katie Harrison, Katherine Ziemke, Tom Lisy, Dominika Dobrosovestnova, Anna Department of Philosophy and Cognitive Science Lund University Sweden Department of Information Technology Uppsala University Sweden Department of Thematic Studies Linköping University Sweden Department of Cognitive Science Linköping University Sweden Human Computer Interaction Group TU Wien Austria

ISBN: (纸本)9781643685670

Significant amounts of HRI research effort are spent on the design/evaluation of robot bodies. Critical discussion and debates concerning the way bodies are treated and considered within HRI have tended to focus on the implications of designing/interacting with highly anthropomorphic robots, yet drawing attention away from the human body in HRI, particularly in the context of everyday interactions with "unexpectedly" social robots, i.e. those non-humanoid robots that human users interact with in a somewhat "social" way. These kind of robots crystallise particular aspects of (and tensions between) the different ways in which bodies are understood across disciplines. At the same time, a fundamental, and largely neglected tool for HRI research is the researchers' own embodied experience of the intersubjective role. This workshop has invited speakers and audience to engage in cross-disciplinary conversations on embodiment in HRI, highlighting the different ways in which "the body" is or can be variously conceptualised and researched. © 2025 IOS Press. All rights reserved.

关键词： Social robots

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Artificial Intelligence Driven Predictive Analysis of Acoustic and Linguistic Behaviors for ASD Identification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5709-5719页

作者： Ashwini, B. Deeptanshu Gulati, Sheffali Shukla, Jainendra Human-Machine Interaction Laboratory Delhi New Delhi110020 India All India Institute of Medical Sciences Department of Pediatrics Child Neurology Division Centre of Excellence and Advanced Research for Childhood Neurodevelopmental Disorders New Delhi110029 India Department of Computer Science and Engineering Delhi New Delhi110020 India

The identification of autism spectrum disorder (ASD) faces challenges due to the lack of reliable biomarkers and the subjectivity in diagnostic procedures, necessitating improved tools for objectivity and efficiency. Being a key characteristic of autism, language impairments are regarded as potential markers for identifying ASD. However, current research predominantly focuses on analyzing language characteristics in English, overlooking linguistic and contextual specificities in other resource-constrained languages. Motivated by these, we developed an artificial intelligence (AI)-based system to detect ASD, utilizing a range of acoustic and linguistic features extracted from dyadic conversations between a child and their communication partner. Validating our model on 76 English-speaking children [35 ASD and 41 typically developing (TD)] and 33 Hindi-speaking children (15 ASD and 18 TD), our extensive analysis of a diverse and comprehensive set of acoustic and linguistic speech attributes, including lexical, syntactic, semantic, and pragmatic elements revealed reliable speech attributes as predictors of ASD. This comprehensive analysis achieved a remarkable macro F1-score of approximately ∼91.30%. We further addressed the influence of linguistic diversity on speech-based ASD assessment by examining speech behaviors in both English and the low-resource language, Hindi. Specific features such as adverbs and distinct roots contributed significantly to ASD classification in English, while the proportion of unintelligible utterances and adposition use held greater importance in Hindi. This study underscores the reliability of speech-based biomarkers in ASD assessment, emphasizing their effectiveness across diverse linguistic backgrounds and highlighting the need for language-specific research in this domain. © 2024 IEEE.

关键词： Semantics

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A Dual-Prompting for Interpretable Mental Health Language Models 9

A Dual-Prompting for Interpretable Mental Health Language Mo...

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9th Workshop on Computational Linguistics and Clinical Psychology, CLPsych 2024

作者： Jeon, Hyolim Yoo, Dongje Lee, Daeun Son, Sejung Kim, Seungbae Han, Jinyoung Department of Applied Artificial Intelligence Sungkyunkwan University Seoul Korea Republic of Department of Human-AI Interaction Sungkyunkwan University Seoul Korea Republic of Department of Computer Engineering Chung-Ang University Seoul Korea Republic of Computer Science & Engineering Department University of South Florida TampaFL United States

ISBN: (纸本)9798891760806

Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task1 aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM;and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach’s potential to aid clinicians in assessing mental state progression. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Predicting the Noticeability of Dynamic Virtual Elements in Virtual Reality 24

Predicting the Noticeability of Dynamic Virtual Elements in ...

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Proceedings of the CHI Conference on human Factors in Computing Systems

作者： Zhipeng Li Yi Fei Cheng Yukang Yan David Lindlbauer Human-Computer Interaction Institute Carnegie Mellon University United States and Department of Computer Science and Technology Tsinghua University China Human-Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9798400703300

While Virtual Reality (VR) systems can present virtual elements such as notifications anywhere, designing them so they are not missed by or distracting to users is highly challenging for content creators. To address this challenge, we introduce a novel approach to predict the noticeability of virtual elements. It computes the visual saliency distribution of what users see, and analyzes the temporal changes of the distribution with respect to the dynamic virtual elements that are animated. The computed features serve as input for a long short-term memory (LSTM) model that predicts whether a virtual element will be noticed. Our approach is based on data collected from 24 users in different VR environments performing tasks such as watching a video or typing. We evaluate our approach (n = 12), and show that it can predict the timing of when users notice a change to a virtual element within 2.56 sec compared to a ground truth, and demonstrate the versatility of our approach with a set of applications. We believe that our predictive approach opens the path for computational design tools that assist VR content creators in creating interfaces that automatically adapt virtual elements based on noticeability.

关键词： Computational interaction Mixed Reality Virtual Reality

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How are ML-Based Online Content Moderation Systems Actually Used? Studying Community Size, Local Activity, and Disparate Treatment 22

How are ML-Based Online Content Moderation Systems Actually ...

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5th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Wang, Leijie Zhu, Haiyi Department of Computer Science and Technology Tsinghua University Beijing China Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450393522

Machine learning-based predictive systems are increasingly used to assist online groups and communities in various content moderation tasks. However, there are limited quantitative understandings of whether and how different groups and communities use such predictive systems differently according to their community characteristics. In this research, we conducted a field evaluation of how content moderation systems are used in 17 Wikipedia language communities. We found that 1) larger communities tend to use predictive systems to identify the most damaging edits, while smaller communities tend to use them to identify any edit that could be damaging;2) predictive systems are used less in content areas where there are more local editing activities;3) predictive systems have mixed effects on reducing disparate treatment between anonymous and registered editors across communities of different characteristics. Finally, we discuss the theoretical and practical implications for future human-centered moderation algorithms. © 2022 Owner/Author.

关键词： Online systems

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices 35

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Zhang, Shenglin Che, Zeyu Pan, Zhongjie Nie, Xiaohui Sun, Yongqian Pan, Lemeng Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Chinese Academy of Sciences Computer Network Information Center China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei China Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350353884

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-Automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service's failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F1-score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators' labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-Automatic trace labeling tool capable of achieving efficient and accurate trace labeling. © 2024 IEEE.

关键词： Labeled data

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