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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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Beyond the network-based multi-omics data integration in COPD: A pathway-centric analysis

Beyond the network-based multi-omics data integration in COP...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Sibilio, Pasquale Conte, Federica Paci, Paola Sapienza University of Rome Department of Computer Control and Management Engineering Rome Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy Karolinska Institutet Stockholm17177 Sweden

ISBN: (纸本)9798350386226

Chronic obstructive pulmonary disease (COPD) is a lung disease causing hundred thousand of death each year worldwide and defined as a respiratory and airflow impairment majorly due to large and small airways dysfunctions. COPD could be considered as a syndrome that includes disease axes of variable pathological and clinical conditions. Despite recent advances, the comprehension of the molecular mechanisms responsible for COPD disease spectrum is far to be reached. Moreover, we should also consider continuous smoking exposure, which could lead to variability in the disease mechanisms and progression. Network-based multi-omics data integration can help to study the association between molecular determinants on diverse biomolecular layers in a disease context. In a previous study, we leveraged lung RNA-seq and DNA-methylation data of a COPD-control cohort to build a correlation-based integrated network (called coupled network), that helped to unveil genes involved in immune and inflammatory modulation of COPD. Therefore, in our previous study we highlighted the most important genes of the coupled network and inspected their single contribution to disease-related pathways. In this study we aim to overcome this limitation by performing a pathway activity analysis by considering the expression and DNA methylation profiles of coupled network genes. Moreover, we exploit this analysis to study the possible contribution of coupled network genes to the differential disease progression between current and former smokers patients. © 2024 IEEE.

关键词： Lung cancer

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End-to-end Optimized Lossy Compression for Neural-morphic Spiking Camera Captured Data

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6074-6086页

作者： Feng, Kexiang Jia, Chuanmin Pan, Jingshan Ma, Siwei Gao, Wen Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Peking University National Engineering Research Center of Visual Technology Beijing100871 China Peking University Wangxuan Institute of Computer Technology Beijing100080 China Shandong Computer Science Center National Supercomputer in Jinan Jinan250014 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China

Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we propose a novel Reconstruction-based Contextual Spike Compression (RCSC) framework, which contains scene reconstruction, contextual image compression and spike generation. To our knowledge, it is the first learning-based model for efficient and robust spike stream compression with informative fidelity. Extensive experimental results show that our model outperforms the state-of-the-art conventional codec VVC intra by 6.14% and surpasses the state-of-the-art learned codec by 2.53% in BD-rate reduction, establishing a strong baseline for spike compression. © 1991-2012 IEEE.

关键词： Image compression

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Attention-Guided Integration of CLIP and SAM for Precise Object Masking in Robotic Manipulation

Attention-Guided Integration of CLIP and SAM for Precise Obj...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Muttaqien, Muhammad A. Motoda, Tomohiro Hanai, Ryo Yukiyasu, Domae Automation Research Team National Institute of AIST Tokyo Japan University of Tsukuba Department of Computer Science Tsukuba Japan

ISBN: (纸本)9798331531614

This paper introduces a novel pipeline to enhance the precision of object masking for robotic manipulation within the specific domain of masking products in convenience stores. The approach integrates two advanced AI models, CLIP and SAM, focusing on their synergistic combination and the effective use of multimodal data (image and text). Emphasis is placed on utilizing gradient-based attention mechanisms and customized datasets to fine-tune performance. While CLIP, SAM, and Grad-CAM are established components, their integration within this structured pipeline represents a significant contribution to the field. The resulting segmented masks, generated through this combined approach, can be effectively utilized as inputs for robotic systems, enabling more precise and adaptive object manipulation in the context of convenience store products. © 2025 IEEE.

关键词： Robotics

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Comparative Analysis of a Resource Loss System with the Finite Buffer and Different Service Disciplines 1

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26th International Conference on Distributed computer and Communication Networks: control, Computation, Communications, DCCN 2023

作者： Stepanov, V.A. Daraseliya, A.V. Sopin, E.S. Shorgin, S.Ya. Moscow Russia Institute of Informatics Problems Federal Research Center Computer Science and Control of Russian Academy of Sciences Moscow Russia

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

Fifth-generation (5G) networks are expected to revolutionize wireless communication by enabling faster and more reliable data transfer rates. However, the use of millimeter-wave frequencies in 5G networks introduces unique challenges in data transmission due to phenomena such as dynamic signal blockage, which can result in sudden and intense resource demands for customers. This paper presents a mathematical model of a 5G base station using a resource allocation system with waiting and non-homogeneous resource requirements for customers. The study aims to determine the most efficient way to select customers from the waiting buffer to increase system performance and minimize the probability of blocking data customers and reduce waiting time in the waiting buffer. As a result of the research, it was found, that the service discipline that prioritized customers with the highest resource requirements and took them from the waiting buffer until the system reached its limit, demonstrates the best system performance under certain parameters. In particular, in this paper, an analytical model was obtained for one of the service disciplines with the calculation of a system of equilibrium equations and performance measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： 5G mobile communication systems

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Data Distribution Valuation 38

Data Distribution Valuation

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xu, Xinyi Wang, Shuaiqi Foo, Chuan-Sheng Low, Bryan Kian Hsiang Fanti, Giulia Department of Computer Science National University of Singapore Singapore Department of Electrical and Computer Engineering Carnegie Mellon University United States Institute for Infocomm Research Agency for Science Technology and Research Singapore

Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces. Existing data valuation methods define a value for a discrete dataset. However, in many use cases, users are interested in not only the value of the dataset, but that of the distribution from which the dataset was sampled. For example, consider a buyer trying to evaluate whether to purchase data from different vendors. The buyer may observe (and compare) only a small preview sample from each vendor, to decide which vendor's data distribution is most useful to the buyer and purchase. The core question is how should we compare the values of data distributions from their samples? Under a Huber characterization of the data heterogeneity across vendors, we propose a maximum mean discrepancy (MMD)-based valuation method which enables theoretically principled and actionable policies for comparing data distributions from samples. We empirically demonstrate that our method is sample-efficient and effective in identifying valuable data distributions against several existing baselines, on multiple real-world datasets (e.g., network intrusion detection, credit card fraud detection) and downstream applications (classification, regression). © 2024 Neural information processing systems foundation. All rights reserved.

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Angle-uniform parallel coordinates

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Computational Visual Media 2023年第3期9卷 495-512页

作者： Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China National Institute of Health Data Science Peking UniversityBeijing 100191China Visualization Research Center(VISUS) University of Stuttgart70569 StuttgartGermany

We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates *** being a common method for visualizing multidimensional data,parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable *** address this issue,we introduce a transformation that bounds all points horizontally using an angleuniform mapping and shrinks them vertically in a structure-preserving fashion;polygonal lines become smooth curves and a symmetric representation of data correlations is *** further propose a combined subsampling and density visualization approach to reduce visual clutter caused by *** method enables accurate visual pattern interpretation of data correlations,and its data-independent nature makes it applicable to all multidimensional *** usefulness of our method is demonstrated using examples of synthetic and real-world datasets.

关键词： parallel coordinates multidimensional data deformation correlations

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A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mismatch Error Shaping

A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mis...

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2024 IEEE International Conference on Integrated Circuits, Technologies and Applications, ICTA 2024

作者： Yi, Tiange Li, Zhenbing Du, Bing Yang, Shiheng Liu, Jiaxin University of Electronic Science and Technology of China China National Computer System Engineering Research Institute of China China

ISBN: (纸本)9798331530709

The resolution of SAR ADCs is primarily limited by sampling noise, DAC mismatch, and comparator noise. This work presents a noise-shaping SAR ADC design that addresses all these error sources. Sampling noise is mitigated using a kT/C noise cancellation technique combined with resampling. DAC mismatch is managed through data-weighted averaging (DWA) and mismatch error shaping (MES), while comparator noise is reduced using a second-order passive noise-shaping (NS) technique. Designed in a 28nm CMOS process, the ADC achieves 86.8 dB SNDR across a 2.8 MHz bandwidth with a power consumption of 3.8 mW. © 2024 IEEE.

关键词： Comparators (optical)

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Multi-objective optimization of lithium-ion battery designs considering the dilemma between energy density and rate capability

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Energy and AI 2024年第4期18卷 53-63页

作者： Xiao-Ying Ma Wen-Ke Zhang Ying Yin Kailong Liu Xiao-Guang Yang National Engineering Research Center of Electric Vehicles School of Mechanical EngineeringBeijing Institute of TechnologyBeijing100081China Shenzhen Automotive Research Institute Beijing Institute of TechnologyShenzhen518118GuangdongChina School of Control Science and Engineering Shandong UniversityJinan250100China

Electrified transportation requires batteries with high energy density and high-rate capability for both charging and ***-ion batteries(LiBs)face a dilemma:increasing areal capacity and reducing electrode porosity to boost energy density often reduces rate capability due to a longer and more tortuous ion transfer *** cell design parameters to balance these metrics is essential but ***,we present a multi-objective optimization framework targeting energy density,fast charging,high-rate discharging,and lifespan *** cell parameters-cathode areal capacity,N-P ratio,cathode porosity,and anode poros-ity-along with operating temperature,are selected as design variables.A physics-based pseudo-2D model,validated against experimental data,generates data to train the surrogate model,which is combined with the NSGA-Ⅱalgorithm for rapid *** different objective calculation methods are compared to identify the maximum sum of energy densities,lowest polarization,and most balanced performance,*** design parameters are optimized at different temperatures using the most balanced optimization *** demonstrate that elevating cell operating temperature achieves high-rate capability while maintaining high energy density,mitigating the energy-power trade-off and broadening battery design parameter ranges.

关键词： Lithium-ion batteries Multi-objective optimization Battery design Fast charging Lithium plating

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Cybersecurity Landscape on Remote State Estimation:A Comprehensive Review

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 851-865页

作者： Jing Zhou Jun Shang Tongwen Chen the Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 1H9Canada the Department of Control Science and Engineering and Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

Cyber-physical systems(CPSs)have emerged as an essential area of research in the last decade,providing a new paradigm for the integration of computational and physical units in modern control *** state estimation(RSE)is an indispensable functional module of ***,it has been demonstrated that malicious agents can manipulate data packets transmitted through unreliable channels of RSE,leading to severe estimation performance *** paper aims to present an overview of recent advances in cyber-attacks and defensive countermeasures,with a specific focus on integrity attacks against ***,two representative frameworks for the synthesis of optimal deception attacks with various performance metrics and stealthiness constraints are discussed,which provide a deeper insight into the vulnerabilities of ***,a detailed review of typical attack detection and resilient estimation algorithms is included,illustrating the latest defensive measures safeguarding RSE from ***,some prevalent attacks impairing the confidentiality and data availability of RSE are examined from both attackers'and defenders'***,several challenges and open problems are presented to inspire further exploration and future research in this field.

关键词： Cyber-attacks Kalman filtering remote state estimation unreliable transmission channels

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