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Unsupervised Distributed Anomaly Detection Framework for IoT in Edge AI Network

IEEE Internet of Things Journal 2025年

作者： Jiang, Xingguo Lu, Chang Luo, Hong Sun, Yan Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China

Anomaly detection of sensor data is crucial to ensure the stability and effectiveness of IoT system. The task requires high accuracy and low latency, which makes distributed anomaly detection gradually become a research hotspot. Edge AI networks further enhance the computing power of edge servers, allowing distributed anomaly detection to be gradually applied to complex scenarios under the Industrial Internet of Things (IIoT), such as smart factories. However, in such cases, the data reported by different sensors in different fields are not independent and identically distributed (non-IID). Simultaneously, during unsupervised training, anomaly data mixed into the training data will reduce the recognition ability of the anomaly detector. To address these challenges, we propose an unsupervised distributed anomaly detection framework. On the cloud, we train an unsupervised anomaly detection model with global factors, using global and local factors to learn the distribution patterns of different fields. At the edge, a multi-dimensional threshold and its automatic selection algorithm are proposed to overcome the problem of decreased anomaly recognition ability introduced by anomaly training data. Extensive experiments on six datasets show that our approach outperforms SOTA methods in F1-score, can detect anomalies with high accuracy and efficiency in distributed IoT scenarios. © 2014 IEEE.

关键词： Industrial internet of things (IIoT)

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Decompose-Compose Feature Augmentation for Imbalanced Crack Recognition in Industrial Scenarios

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IEEE Transactions on Automation Science and engineering 2025年

作者： Chen, Zhuangzhuang Xu, Chengqi Hu, Tao Wang, Li Chen, Jie Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China University of Southern California Department of computer science Los AngelesCA90007 United States

Automated crack recognition has achieved remarkable progress in the past decades as a critical task in structure health monitoring, to ensure safety and durability in many industrial scenarios. However, imbalanced crack recognition remains challenging due to the scarcity of crack samples and the consequential limited diversity. To resolve this, Artificial Intelligence Generated Content (AIGC) has been gradually adopted to generate synthetic data and reduce reliance on large amounts of labeled crack samples. This paper assumes that a crack sample in the feature space can be regarded as a combination of crack and background semantics. Then, the decompose-compose feature augmentation framework (DeCo) is proposed to perform crack data synthesis in the feature space by randomly composing crack and background semantic-relevant features. Specifically, the contrastive learning-based decomposing loss is proposed to enforce two encoders to separately learn crack and background semantics from crack samples with the theoretical guarantee. After that, an effective cross-instance feature union strategy is proposed to synthesize diverse crack samples by composing the crack-relevant features from a crack sample and background-relevant features across other training samples. To address the limited availability of related benchmarks, we collect INPP2022 and IRC2022 datasets from real-world applications in nuclear power plants and road pavement. Experimental results show that DeCo performs favorably against state-of-the-art competitors in imbalanced crack recognition tasks. © 2004-2012 IEEE.

关键词： Nuclear fuels

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KANOCLIP: ZERO-SHOT ANOMALY DETECTION THROUGH KNOWLEDGE-DRIVEN PROMPT LEARNING AND ENHANCED CROSS-MODAL INTEGRATION

arXiv

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arXiv 2025年

作者： Li, Chengyuan Zhou, Suyang Kong, Jieping Qi, Lei Xue, Hui College of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly prompts is time-consuming and prone to semantic ambiguity, and CLIP struggles with pixel-level anomaly segmentation, focusing more on global semantics than local details. To address these limitations, We introduce KAnoCLIP, a novel ZSAD framework that leverages vision-language models. KAnoCLIP combines general knowledge from a Large Language Model (GPT-3.5) and fine-grained, image-specific knowledge from a Visual Question Answering system (Llama3) via Knowledge-Driven Prompt Learning (KnPL). KnPL uses a knowledge-driven (KD) loss function to create learnable anomaly prompts, removing the need for fixed text prompts and enhancing generalization. KAnoCLIP includes the CLIP visual encoder with V-V attention (CLIP-VV), Bi-Directional Cross-Attention for Multi-Level Cross-Modal Interaction (Bi-CMCI), and Conv-Adapter. These components preserve local visual semantics, improve local cross-modal fusion, and align global visual features with textual information, enhancing pixel-level anomaly detection. KAnoCLIP achieves state-of-the-art performance in ZSAD across 12 industrial and medical datasets, demonstrating superior generalization compared to existing methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Multipedal - Multilayer Perceptron Demographic Targeting Algorithm

Multipedal - Multilayer Perceptron Demographic Targeting Alg...

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International Conference on Knowledge and Smart Technology (KST)

作者： Paul Connolly Kieran Flanagan Enda Fallon Circana Athlone Ireland Department of Computer and Software Engineering Technological University of the Shannon Athlone Ireland

ISBN: (数字)9798331520403

ISBN: (纸本)9798331520410

Modern data collection processes and applications provide the ability to gather detailed spending patterns of large numbers of consumers. Additionally, modern on-line marketing programs allow for highly specific targeting of advertising to demographic cohorts of interest. Advertisers know the sales performance characteristics of the consumers they wish to target, but they do not know the demographic characteristics of those consumers that exhibit the desired performance characteristics. This paper proposes a machine learning driven approach to determine, via feature-value selection, the specific demographics to target with online marketing when the aim is to focus that marketing spend on specific consumers of interest. In this experiment, a particular use case is examined to identify the key demographic features and values of high spenders in the grocery sector for a specific region. Specifically, a multilayer perceptron model and explainer is deployed to determine the feature values that distinguish high spenders in grocery retail that can subsequently be used to optimize online marketing spend. It is shown that this approach does show the key demographic feature values associated with the high spending cohort targeted. This can be the basis on which to further develop a generalized approach that can be used to determine the target demographic groups for other cohorts of interest.

关键词： Surveys Ethnicity Education Machine learning Dogs Predictive models Multilayer perceptrons Data collection Data models Advertising

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HTM-CDFK: An Online Industrial Control Anomaly Detection Algorithm based on Hierarchical Time Memory

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Liu, Jingwen Zhang, Yuanyuan Wang, Zilin Ren, Yi Chen, Liangyin Chen, Yanru Sichuan University College of Computer Science College of Software Engineering Institute for Industrial Internet Research Chengdu610065 China

With the rapid advancement of industrial structures, many factories are deploying anomaly detection systems. However, most existing anomaly detection algorithms are unsuitable for high-noise industrial control system (ICS) environments and constantly changing sensor patterns, resulting in low detection accuracy. To address these challenges, we propose a hierarchical temporal memory-based online anomaly detection algorithm for ICS, incorporating cumulative distribution functions and Gaussian convolution kernels. We enhanced the encoding process of hierarchical temporal memory, enabling it to quickly fit the physical processes of ICS and remember the system's previous operating states for anomaly recognition. Additionally, our anomaly calculation algorithm, based on cumulative distribution functions and Gaussian kernel convolution, effectively addresses the adaptability and accuracy challenges posed by high-noise ICS environments. Experimental results demonstrate that our method outperforms the best baseline algorithms. While maintaining good time stability, our method achieves a 9% improvement in detection accuracy, highlighting its significant advantage in balancing detection precision and time performance. © 2004-2012 IEEE.

关键词： Distribution functions

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TraceLens: Early Detection of software Anomalies Using Critical Path Analysis 25

TraceLens: Early Detection of Software Anomalies Using Criti...

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Companion of the 16th ACM/SPEC International Conference on Performance engineering

作者： Masoumeh Nourollahi Amir Haghshenas Michel Dagenais Department of Computer and Software Engineering École Polytechnique de Montréal Montreal QC Canada

ISBN: (纸本)9798400711305

Runtime smell detection in software systems, particularly through system call analysis, has garnered significant attention in recent years. Although various machine learning techniques have been employed to enhance detection accuracy and reduce false positives, limited focus has been given to their practical application in early real-time anomaly detection. To address this gap, we propose a deep learning-based approach, called TraceLens, designed for the early detection of performance-related issues in software systems. Unlike traditional methods that rely on system call data, our approach leverages critical path analysis, enabling more efficient and targeted anomaly detection. Experimental results demonstrate that this approach achieves detection performance comparable to methods that use system calls, while significantly improving data collection efficiency. In addition, the critical path dataset highlights software dependencies, both internal and external, providing deeper insight into the dynamic behavior of software systems.

关键词： anomaly detection

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Research and Development of Kubernetes Operator for Lightweight Multi-Access Edge Computing Platform Simulator

Research and Development of Kubernetes Operator for Lightwei...

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2025 Systems of Signals Generating and Processing in the Field of on Board Communications, SOSG 2025

作者： Filianin, Ivan Borisov, Nikita Bogatyrev, Vladimir Itmo University Faculty of Software Engineering and Computer Engineering St. Petersburg Russia Itmo University Faculty of Infocommunication Technologies St. Petersburg Russia Saint-Petersburg State University of Aerospace Instrumentation Department of Information Systems Security St. Petersburg Russia

ISBN: (纸本)9798331532635

This paper addresses the problem of deploying complex systems in Kubernetes clusters. It discusses using the OperatorSDK framework supported by RedHat as a basis for implementing the Kubernetes operator for Lightweight MultiAccess Edge Computing Platform Simulator (LWMECPS) deployment. Special attention is given to the operator Kubernetes architecture and how to use Operator Lifecycle Manager (OLM) to continuously deploy LWMECPS and machine learning models for it. © 2025 IEEE.

关键词： Edge computing

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A Neural Network Framework for Classifying Educational Content Aligned with Learning Outcomes 16

A Neural Network Framework for Classifying Educational Conte...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alkhalil, Adel Department of Software Engineering College of Computer Science and Engineering University of ha'Il Ha'il81481 Saudi Arabia

Rapid growth of digital educational content necessitates efficient and accurate methods for organizing and mapping resources to ensure well-alignment with targeted learning outcomes, academic standards, and competency frameworks. Traditional text classification approaches, including rule-based and classical machine learning techniques, often fail to address the semantic diversity and scalability demands of modern educational systems. This study investigates the application of neural networks for text classification to automate the mapping of educational content into predefined categories. Leveraging state-of-the-art architectures such as Long Short-Term Memory (LSTM) networks, and transformers like BERT, we present an architecture of a systematic Classification of educational materials. We discuss the implications of this work for adaptive learning environments, emphasizing the potential of neural networks to enhance the efficiency and scalability of content mapping. This study contributes to the growing body of research in artificial intelligence for education and sets the stage for further exploration into multilingual and domain-specific content classification methods. © 2025 Elsevier B.V.. All rights reserved.

关键词： Mapping

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A Smart Framework for Optimizing User Feedback Prioritization in Application Development

A Smart Framework for Optimizing User Feedback Prioritizatio...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Nuha Almoqren Mubarak Alrashoud Software Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

In mobile app development, user reviews are a significant source of requirements. Users frequently report bugs, request new features, or suggest enhancements. Mobile app vendors aim to maximize user satisfaction by addressing these continuous comments and requests as early as possible. Typically, they prioritize delivering the most promising features, extracted from user reviews, in early releases while deferring fewer promising ones to later stages. However, due to the massive volume of reviews, redundancy, and conflicts among them, manually extracting requirements is inefficient and often challenging, making requirement prioritization even more difficult. Therefore, automating this process is essential. This paper presents a conceptual framework for the requirements prioritization process's automation, continuity, and scalability. The proposed framework follows a hybrid approach that integrates multiple advanced techniques: generative artificial intelligence, active learning, ontologies, and optimization algorithms. Generative artificial intelligence enables the identification of important patterns and the automatic extraction of requirements and their properties, which aids in assessing properties to determine requirement priorities. The generative artificial intelligence framework integrates with an active learning system to improve annotation efficiency. Ontologies help comprehend relationships, properties, and dependencies among requirements, aligning them with domain-specific knowledge. Optimization methods playa crucial role in the requirements prioritization process by computing the weights of various properties to identify the most effective combination of requirements and determine the optimal order for implementation. Consequently, this research presents a smart theoretical framework for enhancing user-driven maintenance and development of mobile applications. Researchers are tackling several critical challenges that remain unresolved in the field, with

关键词： Technological innovation Reviews Generative AI Active learning Ontologies Feature extraction software Mobile applications software measurement software development management

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The 23andMe Data Breach: Analyzing Credential Stuffing Attacks, Security Vulnerabilities, and Mitigation Strategies

arXiv

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arXiv 2025年

作者： Holthouse, Ryan Owens, Serena Bhunia, Suman Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States

In October 2023, 23andMe, a prominent provider of personal genetic testing, ancestry, and health information services, suffered a significant data breach orchestrated by a cybercriminal known as "Golem." Initially, approximately 14,000 user accounts were compromised by a credential smear attack, exploiting reused usernames and passwords from previous data leaks. However, due to the interconnected nature of 23andMe’s DNA Relatives and Family Tree features, the breach expanded exponentially, exposing sensitive personal and genetic data of approximately 5.5 million users and 1.4 million additional profiles. The attack highlights the increasing threat of credential stuffing, exacerbated by poor password hygiene and the absence of robust security measures such as multi-factor authentication (MFA) and rate limiting. In response, 23andMe mandated password resets, implemented email-based two-step verification, and advised users to update passwords across other services. This paper critically analyzes the attack methodology, its impact on users and the company, and explores potential mitigation strategies, including enhanced authentication protocols, proactive breach detection, and improved cybersecurity practices. The findings underscore the necessity of stronger user authentication measures and corporate responsibility in safeguarding sensitive genetic and personal data. © 2025, CC BY.

关键词： Cybersecurity

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