2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)最新文献

Machine Learning Based Battery Anomaly Detection Using Empirical Data 利用经验数据进行基于机器学习的电池异常检测

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463489

Md Shahriar Nazim, Yeong Min Jang, ByungDeok Chung

引用次数: 0

Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory 通过博弈论分析物联网中参与式联盟学习的能量最小化问题

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463513

Alessandro Buratto, Elia Guerra, M. Miozzo, Paolo Dini, L. Badia

{"title":"Energy Minimization for Participatory Federated Learning in IoT Analyzed via Game Theory","authors":"Alessandro Buratto, Elia Guerra, M. Miozzo, Paolo Dini, L. Badia","doi":"10.1109/ICAIIC60209.2024.10463513","DOIUrl":"https://doi.org/10.1109/ICAIIC60209.2024.10463513","url":null,"abstract":"The Internet of Things requires intelligent decision making in many scenarios. To this end, resources available at the individual nodes for sensing or computing, or both, can be leveraged. This results in approaches known as participatory sensing and federated learning, respectively. We investigate the simultaneous implementation of both, through a distributed approach based on empowering local nodes with game theoretic decision making. A global objective of energy minimization is combined with the individual node's optimization of local expenditure for sensing and transmitting data over multiple learning rounds. We present extensive evaluations of this technique, based on both a theoretical framework and experiments in a simulated network scenario with real data. Such a distributed approach can reach a desired level of accuracy for federated learning without a centralized supervision of the data collector. However, depending on the weight attributed to the local costs of the single node, it may also result in a significantly high Price of Anarchy (from 1.28 onwards). Thus, we argue for the need of incentive mechanisms, possibly based on Age of Information of the single nodes.","PeriodicalId":518256,"journal":{"name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"35 12","pages":"249-254"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluation of XAI Algorithms in IoT Traffic Anomaly Detection 物联网流量异常检测中的 XAI 算法评估

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463357

Uyen Do, Laura Lahesoo, R. Carnier, Kensuke Fukuda

{"title":"Evaluation of XAI Algorithms in IoT Traffic Anomaly Detection","authors":"Uyen Do, Laura Lahesoo, R. Carnier, Kensuke Fukuda","doi":"10.1109/ICAIIC60209.2024.10463357","DOIUrl":"https://doi.org/10.1109/ICAIIC60209.2024.10463357","url":null,"abstract":"Anomaly detection in network traffic, both in general computer networks and specifically in Internet of Things (IoT) networks, plays a crucial role in ensuring computer network security. Over the years, numerous machine learning and deep learning-based anomaly detection tools have been proposed, exhibiting high accuracy in identifying anomalous behavior. However, a significant challenge arises with most machine learning and deep learning algorithms, as they are often considered black-box models that lack interpretability. Consequently, explaining the reasons behind certain network behaviors being labeled as anomalous becomes a difficult task. To overcome this issue, we evaluate the combination of anomaly detectors and eXplainable Artificial Intelligence (XAI) algorithms in IoT traffic anomaly detection. Our research results demonstrate that XAI algorithms can consistently identify the most impactful network features of security anomalies. More specifically, (1) SHAP algorithm is the most robust and reliable in the four tested XAI algorithms for four types of supervised/unsupervised anomaly detection models, independent of two datasets including different anomalies. (2) Image-based XAI algorithms are not suitable for explainability of network anomaly detection.","PeriodicalId":518256,"journal":{"name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"72 ","pages":"669-674"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design of Greenhouse Automated Carbon Cycling System for Carbon Savings 设计温室自动碳循环系统以实现碳节约

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463490

Kwang Ho Yang, H. Yoe, Mi Suk Kim, Meong-hun Lee

引用次数: 0

A Study on Solving the Data Imbalance Problem for Detecting Heunginjimun Roof Tilt Using Transfer Learning Algorithms 利用迁移学习算法解决兴仁寺门屋顶倾斜检测数据不平衡问题的研究

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463414

Sang-Yun Lee, Seok-Ju Kang

引用次数: 0

Bit-by-Bit: A Quantization-Aware Training Framework with XAI for Robust Metaverse Cybersecurity 逐位：利用 XAI 的量化感知训练框架实现强大的元宇宙网络安全

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463374

Ebuka Chinaechetam Nkoro, C. I. Nwakanma, Jae-Min Lee, Dong‐Seong Kim

{"title":"Bit-by-Bit: A Quantization-Aware Training Framework with XAI for Robust Metaverse Cybersecurity","authors":"Ebuka Chinaechetam Nkoro, C. I. Nwakanma, Jae-Min Lee, Dong‐Seong Kim","doi":"10.1109/ICAIIC60209.2024.10463374","DOIUrl":"https://doi.org/10.1109/ICAIIC60209.2024.10463374","url":null,"abstract":"In this work, a novel framework for detecting mali-cious networks in the IoT-enabled Metaverse networks to ensure that malicious network traffic is identified and integrated to suit optimal Metaverse cybersecurity is presented. First, the study raises a core security issue related to the cyberthreats in Metaverse networks and its privacy breaching risks. Second, to address the shortcomings of efficient and effective network intrusion detection (NIDS) of dark web traffic, this study employs a quantization-aware trained (QAT) 1D CNN followed by fully con-nected networks (ID CNNs-GRU-FCN) model, which addresses the issues of and memory contingencies in Metaverse NIDS models. The QAT model is made interpretable using eXplainable artificial intelligence (XAI) methods namely, SHapley additive exPlanations (SHAP) and local interpretable model-agnostic ex-planations (LIME), to provide trustworthy model transparency and interpretability. Overall, the proposed method contributes to storage benefits four times higher than the original model without quantization while attaining a high accuracy of 99.82 %.","PeriodicalId":518256,"journal":{"name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"35 31","pages":"832-837"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Securing Critical Infrastructure: A Denoising Data-Driven Approach for Intrusion Detection in ICS Network 保护关键基础设施：用于 ICS 网络入侵检测的去噪数据驱动方法

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463488

Urslla Uchechi Izuazu, Vivian Ukamaka Ihekoronye, Dong‐Seong Kim, Jae Min Lee

{"title":"Securing Critical Infrastructure: A Denoising Data-Driven Approach for Intrusion Detection in ICS Network","authors":"Urslla Uchechi Izuazu, Vivian Ukamaka Ihekoronye, Dong‐Seong Kim, Jae Min Lee","doi":"10.1109/ICAIIC60209.2024.10463488","DOIUrl":"https://doi.org/10.1109/ICAIIC60209.2024.10463488","url":null,"abstract":"The centralized and vulnerable nature of the industrial control system (ICS) communication network makes it an attractive target for malicious actors aiming to infiltrate and exploit vulnerabilities. These threat actors seek to cause disruptions, compromise sensitive data, and potentially sabotage critical industrial processes. Existing methods for threat detection assume an ideal scenario where there exists no noise/disturbance to threat detection and classification, neglecting to account for the inherent noise and complexity present in real-world industrial processing environments. In reality, the deployment of these models may introduce performance degradation leading to sub-optimal model performance. In response to the identified issue, this study presents a security framework that proactively addresses the challenges posed by noise and provides a robust mechanism for detecting malicious activities from routine industrial network operations. The proposed framework can be deployed at the supervision network segment of ICS to analyze incoming network traffic signals, to effectively distinguish an attack from normal operation amdist noise. Our proposed approach undergoes experimental simulations to validate its effectiveness, and is compared with state-of-the-art based on key performance metrics. Simulation results show that our approach is robust in reconstructing noisy traffic signals with a minimal mean square error of 0.12 and an overall accuracy of 99.6%, outperforming existing methods.","PeriodicalId":518256,"journal":{"name":"2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":"41 11","pages":"841-846"},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140527683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Defense Method Challenges Against Backdoor Attacks in Neural Networks 针对神经网络后门攻击的防御方法挑战

2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) Pub Date : 2024-02-19 DOI: 10.1109/ICAIIC60209.2024.10463411

Samaneh Shamshiri, Insoo Sohn

引用次数: 0