Pub Date : 2026-02-09DOI: 10.1109/tac.2026.3662563
Alexandre Didier, Melanie N. Zeilinger
{"title":"Approximate Predictive Control Barrier Function for Discrete-Time Systems","authors":"Alexandre Didier, Melanie N. Zeilinger","doi":"10.1109/tac.2026.3662563","DOIUrl":"https://doi.org/10.1109/tac.2026.3662563","url":null,"abstract":"","PeriodicalId":13201,"journal":{"name":"IEEE Transactions on Automatic Control","volume":"133 1","pages":""},"PeriodicalIF":6.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1016/j.jnca.2026.104438
Zainab Alwaisi, Tanesh Kumar, Simone Soderi
Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.
{"title":"Robust and energy-aware detection of Mirai botnet for future 6G-enabled IoT networks","authors":"Zainab Alwaisi, Tanesh Kumar, Simone Soderi","doi":"10.1016/j.jnca.2026.104438","DOIUrl":"https://doi.org/10.1016/j.jnca.2026.104438","url":null,"abstract":"Next-generation IoT wireless communication systems emphasise the importance and urgent need for energy-efficient security measures, thus requiring a balanced approach to address growing security vulnerabilities and fulfil energy demands in advanced wireless communication networks. However, the evolution of 6G networks and their integration with advanced technologies will revolutionise the IoT ecosystem while simultaneously introducing new security threats such as the Mirai malware, which targets IoT devices, infects multiple nodes, and depletes computational and energy resources. This study introduces a novel security algorithm designed to minimise energy consumption while effectively detecting botnet attacks at the smart device level. This research examines four distinct types of Mirai botnet attacks: scan, UDP, TCP, and ACK flooding.The experimental evaluation was conducted using real IoT device data collected from a Raspberry Pi setup combined with network traffic traces simulating the four Mirai attack scenarios to ensure realistic and reproducible results. Two ML algorithms, SVM and KNN, are employed to detect these botnet attacks, with each algorithm’s detection accuracy and energy efficiency thoroughly assessed. Results indicate that the proposed approach significantly enhances smart device security while minimising energy use. Findings show that the KNN algorithm outperforms SVM in terms of accuracy and energy efficiency for detecting Mirai botnet attacks, achieving detection rates above 99% across various attack types. This study highlights the importance of selecting suitable security techniques for IoT networks to address the evolving threats and energy demands of 6G-enabled wireless communication systems, providing valuable insights for future research.","PeriodicalId":54784,"journal":{"name":"Journal of Network and Computer Applications","volume":"1 1","pages":""},"PeriodicalIF":8.7,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/tnnls.2026.3657138
Basit Alawode, Iyyakutti Iyappan Ganapathi, Sajid Javed, Mohammed Bennamoun, Arif Mahmood
{"title":"AquaticCLIP: A Vision-Language Foundation Model and Dataset for Underwater Scene Analysis","authors":"Basit Alawode, Iyyakutti Iyappan Ganapathi, Sajid Javed, Mohammed Bennamoun, Arif Mahmood","doi":"10.1109/tnnls.2026.3657138","DOIUrl":"https://doi.org/10.1109/tnnls.2026.3657138","url":null,"abstract":"","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"161 1","pages":""},"PeriodicalIF":10.4,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1109/jiot.2026.3662758
Haowen Zhang, Juan Li, Qing Yao
{"title":"RACER: Fast and Accurate Time Series Clustering with Random Convolutional Kernels and Ensemble Methods","authors":"Haowen Zhang, Juan Li, Qing Yao","doi":"10.1109/jiot.2026.3662758","DOIUrl":"https://doi.org/10.1109/jiot.2026.3662758","url":null,"abstract":"","PeriodicalId":54347,"journal":{"name":"IEEE Internet of Things Journal","volume":"314 1","pages":""},"PeriodicalIF":10.6,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-09DOI: 10.1007/s10796-026-10698-3
Mustafa Cavus, Jan N. van Rijn, Przemysław Biecek
Trustworthiness of AI systems is a core objective of Human-Centered Explainable AI, and relies, among other things, on explainability and understandability of the outcome. While automated machine learning tools automate model training, they often generate not only a single “best” model but also a set of near-equivalent alternatives, known as the Rashomon set. This set provides a unique opportunity for human-centered explainability: by exposing variability among similarly performing models, we can offer users richer and more informative explanations. In this paper, we introduce Rashomon partial dependence profiles , a model-agnostic technique that aggregates feature effect estimates across the Rashomon set. Unlike traditional explanations derived from a single model, Rashomon partial dependence profiles explicitly quantify uncertainty and visualize variability, further enabling user trust and understanding model behavior to make informed decisions. Additionally, under high-noise conditions, the Rashomon partial dependence profiles more accurately recover ground-truth feature relationships than a single-model partial dependence profile. Experiments on synthetic and real-world datasets demonstrate that Rashomon partial dependence profiles reduce average deviation from the ground truth by up to 38%, and their confidence intervals reliably capture true feature effects. These results highlight how leveraging the Rashomon set can enhance technical rigor while centering explanations on user trust and understanding aligned with Human-centered explainable AI principles.
{"title":"Quantifying Model Uncertainty with AutoML and Rashomon Partial Dependence Profiles: Enabling Trustworthy and Human-centered XAI","authors":"Mustafa Cavus, Jan N. van Rijn, Przemysław Biecek","doi":"10.1007/s10796-026-10698-3","DOIUrl":"https://doi.org/10.1007/s10796-026-10698-3","url":null,"abstract":"Trustworthiness of AI systems is a core objective of Human-Centered Explainable AI, and relies, among other things, on explainability and understandability of the outcome. While automated machine learning tools automate model training, they often generate not only a single “best” model but also a set of near-equivalent alternatives, known as the Rashomon set. This set provides a unique opportunity for human-centered explainability: by exposing variability among similarly performing models, we can offer users richer and more informative explanations. In this paper, we introduce <jats:italic>Rashomon partial dependence profiles</jats:italic> , a model-agnostic technique that aggregates feature effect estimates across the Rashomon set. Unlike traditional explanations derived from a single model, Rashomon partial dependence profiles explicitly quantify uncertainty and visualize variability, further enabling user trust and understanding model behavior to make informed decisions. Additionally, under high-noise conditions, the Rashomon partial dependence profiles more accurately recover ground-truth feature relationships than a single-model partial dependence profile. Experiments on synthetic and real-world datasets demonstrate that Rashomon partial dependence profiles reduce average deviation from the ground truth by up to 38%, and their confidence intervals reliably capture true feature effects. These results highlight how leveraging the Rashomon set can enhance technical rigor while centering explanations on user trust and understanding aligned with Human-centered explainable AI principles.","PeriodicalId":13610,"journal":{"name":"Information Systems Frontiers","volume":"45 1","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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