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Deep Learning IoT Malware Analysis: Investigation and Understanding

Neural Computing and Applications 2025年 1-28页

作者： Abdullah, Muhammed Amin Yu, Yongbin Cai, Jingye Addo, Daniel Bankas, Edem K. Gu, Yeong Hyeon Alqahtani, Ali Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 South Korea Center for Artificial Intelligence and Computer Science Department King Khalid University Abha 61421 Saudi Arabia

This empirical study explores the ability of lightweight convolutional neural networks (CNNs) for malware analysis in Internet of Things (IoT) environments, emphasizing the impact of input dimensionality and size on feature extraction quality. By converting malware binaries into 1D and 2D grayscale images, we systematically evaluate how varying input resolutions (e.g., 64 × 64 for 2D, 4096 for 1D) influence model performance, resource efficiency, and interpretability. Experiments on the Microsoft Malware dataset (MMD) and IoT Malware dataset (IMD) reveal that 1D CNN models outperform their 2D counterparts in accuracy, parameter efficiency, and training time, with gated recurrent unit (GRU) classifiers demonstrating superior performance across both dimensions. Post hoc visual interpretability techniques, including saliency maps and activation heatmaps, uncover critical feature patterns driving classification decisions, addressing the "black box" limitations of deep learning in cybersecurity. Our findings challenge the assumption that 2D models inherently excel in image-based tasks and provide actionable insights for designing resource-efficient IoT malware detection systems. By bridging the gap between model complexity and deployment feasibility, this work advances the application of explainable AI to protect IoT ecosystems against evolving cyber threats. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional Neural Networks (CNN) Deep learning Feature selection Internet of things Interpretability saliency maps Malware analysis Malware Images

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Research and Application of Digital Twin in the Field of Hydropower Stations 9

Research and Application of Digital Twin in the Field of Hyd...

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9th Asia Conference on Power and Electrical engineering, ACPEE 2024

作者： Wei, Zhang Xie, Limiao Zhang, Wei Liu, Jiali Fan, Meijie Yi, Fengchao Inner Mongolia Electric Power Economy and Technology Research Institute Branch Hohhot China Inner Mongolia University School of Computer Science Hohhot China Inner Mongolia Intelligent Electronic Data Services Co. Ltd Hohhot China

ISBN: (纸本)9798350309638

The national '14th Five-Year Plan' on the construction of intelligent water conservancy clearly requires the construction of an intelligent water conservancy system to enhance the ability to measure and report water clarity and intelligent scheduling, and to make full use of the new generation of information technology such as the Internet of Things, big data and digital twins. The main task of smart water conservancy construction is to build a digital twin basin to achieve integrated development and safety. To this end, the digital twin platform in this paper covers the entire process of reservoir digital scene construction, simultaneous monitoring of dam safety and rainfall, real-time forecasting, automatic feedback analysis, online safety judgement, early warning, and dam safety decision support. The system collects the characteristics of dam settlement, inclination, water pressure and dam shape, and through the acquisition, collation and analysis of all kinds of information, it makes dam safety evaluation, controls the safe operation of dams, verifies the accuracy of calculation parameters, the practicability of the calculation method, and the correctness of the feedback construction method, and helps the management personnel to make accurate and fast disaster warning forecasts to protect the people's life and property safety. In addition, this paper also researches the 'four pre' functions required for the construction of digital twin watersheds, i.e., prediction, early warning, pre-control and planning, and puts forward suggestions for a perfect business application system and network security protection system. © 2024 IEEE.

关键词： Forecasting

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Comparative Study of Social Withdrawal Symptoms in Control and Ketum Dependent Subjects 8

Comparative Study of Social Withdrawal Symptoms in Control a...

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8th IET Smart Cities Symposium, SCS 2024

作者： Shafawi, Nur Hanani Adzha Vijean, Vikneswaran Palaniappan, Rajkumar Ravindran, Sindhu Chin, Lim Chee Abdullah, Rokiah Yogesh, C.K. Faculty of Electronic Engineering & Technology Universiti Malaysia Perlis Kampus Induk Pauh Putra Pauh Perlis Malaysia Department of Mechatronics Engineering University of Technology Bahrain Building 829 Road 1213 Block 712 Salmabad Bahrain Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus India

ISBN: (纸本)9781837242672

Derived from the Mitragyna speciosa tree, ketum is renowned for its addictive qualities and hallucinogenic effects. Using electroencephalogram (EEG) data, this study compares the social withdrawal symptoms—specifically, depression, irritability, and restlessness—between control and ketum-dependent people. After pre-processing the EEG data, the Dual-Tree Complex Wavelet Transform (DT-CWT) was used to extract features. The study employed statistical analyses, namely the two-sample t-tests, to determine significant differences between the groups and to develop indices for social withdrawal symptoms. To verify the distinction between social withdrawal symptoms, classification models such as Coarse Decision Tree, Logistic Regression, and Cubic K-Nearest Neighbor (KNN) were utilized. The EEG patterns of the control and ketum subjects differed significantly, according to the results, with Task 1 exhibiting more noticeable differences than Task 2. This study highlights the possibility of a trustworthy test for ketum-induced social withdrawal and advances the development of objective diagnostic tools for ketum-related social withdrawal. © The Institution of engineering & Technology 2024.

关键词： Logistic regression

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AI based Stock Market Analysis and Decision Making System using Design Thinking Approach 8

AI based Stock Market Analysis and Decision Making System us...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kumar, M.Venkatesh Umamaheswari, M. Bharathi, C. Hema Maruthaveni, R. Devi, M.Nirmala Prasanna, R. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India Dr. Sns Rajalakshmi College of Arts and Science Department of Business Administration Tamilnadu Coimbatore India Tamil Nadu Agricultural University Department of Physical Science and Information Technology Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Computer Science with Data Analytics Tamilnadu Coimbatore India Dr. Sns Rajalakshmi College of Arts and Science Department of Management Studies Tamilnadu Coimbatore India

ISBN: (纸本)9798350386578

Stock market is a dynamic and ever-changing environment that can be both exciting and challenging for investors. Equities, primarily referred to as stocks, are traded on stock exchanges around the world and reflect ownership in a business. The stock market is an essential tool for companies to raise capital, and investors use it to grow their wealth. Investing in stocks requires a thorough understanding of the market and the individual companies in which you are interested. The financial health of a company must be taken into account and can be analyzed using financial statements and other data. Another critical factor is the overall economic climate, as market conditions can significantly impact stock prices. It's crucial to have a long-term outlook and avoid being influenced by momentary market changes when making equity investments. A random walk is a mathematical model used to describe a sequence of steps or movements where each step is determined by chance. It is often used in various fields, including physics, finance, and statistics. Random walk theory has important implications in various fields, including the efficient market hypothesis in finance and the modeling of diffusion processes in physics. To effectively predict the future stock price using conventional and modern machine learning models. Compare the performance of different models and identify a higher accuracy prediction model using the selected important factors from disparate data sources. © 2024 IEEE.

关键词： Decentralized finance

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Artificial Intelligence and data science for Social Good: The Case for the SDGs

Artificial Intelligence and Data Science for Social Good: Th...

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2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Maria Fasli Business and Local Government Data Research Centre School of Computer Science and Electronic Engineering Institute for Analytics and Data Science University of Essex Colchester United Kingdom

Technologies have always been reshaping our economic, social and cultural environments but more recently we have seen the speed of change and impact of ICT technologies in particular rapidly increasing. These phenomenal changes are fuelled by the proliferation of data and the advances in computational methods. Though Artificial Intelligence as a (sub)discipline has been around for over 60 years, it is only in the last decade or so that we have seen very significant improvements in techniques such as machine learning. The potential of Artificial Intelligence to transform multiple aspects of our lives is huge, but there is an equally huge challenge in ensuring that the benefits of these technologies reach all.

关键词： Economics Transforms Machine learning data science Intelligent agents Cultural differences

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A Rationale-Centric Framework for Human-in-the-loop Machine Learning 60

A Rationale-Centric Framework for Human-in-the-loop Machine ...

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60th Annual Meeting of the Association for Computational Linguistics, ACL 2022

作者： Lu, Jinghui Yang, Linyi Namee, Brian Mac Zhang, Yue The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland School of Engineering Westlake University China Institute of Advanced Technology Westlake Institute for Advanced Study China SenseTime Research

ISBN: (纸本)9781955917216

We present a novel rationale-centric framework with human-in-the-loop - Rationales-centric Double-robustness Learning (RDL) - to boost model out-of-distribution performance in few-shot learning scenarios. By using static semi-factual generation and dynamic human-intervened correction, RDL exploits rationales (i.e. phrases that cause the prediction), human interventions and semi-factual augmentations to decouple spurious associations and bias models towards generally applicable underlying distributions, which enables fast and accurate generalisation. Experimental results show that RDL leads to significant prediction benefits on both in-distribution and out-of-distribution tests compared to many state-of-the-art benchmarks-especially for few-shot learning scenarios. We also perform extensive ablation studies to support in-depth analyses of each component in our framework. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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A Dense Retrieval System and Evaluation dataset for Scientific Computational Notebooks 19

A Dense Retrieval System and Evaluation Dataset for Scientif...

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19th IEEE International Conference on e-science, e-science 2023

作者： Li, Na Zhang, Yangjun Zhao, Zhiming Multiscale Networked System Informatics Institute University of Amsterdam Netherlands School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Amsterdam Netherlands

ISBN: (纸本)9798350322231

The discovery and reutilization of scientific codes are crucial in many research activities. Computational notebooks have emerged as a particularly effective medium for sharing and reusing scientific codes. Nevertheless, effectively locating relevant computational notebooks is a significant challenge. First, computational notebooks encompass multi-modal data comprising unstructured text, source code, and other media, posing complexities in representing such data for retrieval purposes. Second, the absence of evaluation datasets for the computational notebook search task hampers fair performance assessments within the research community. Prior studies have either treated computational notebook search as a code-snippet search problem or focused solely on content-based approaches for searching computational notebooks. To address the aforementioned difficulties, we present DeCNR, tackling the information needs of researchers in seeking computational notebooks. Our approach leverages a fused sparse-dense retrieval model to represent computational notebooks effectively. Additionally, we construct an evaluation dataset including actual scientific queries, computational notebooks, and relevance judgments for fair and objective performance assessment. Experimental results demonstrate that the proposed method surpasses baseline approaches in terms of F1@5 and NDCG@5. The proposed system has been implemented as a web service shipped with REST APIs, allowing seamless integration with other applications and web services. © 2023 IEEE.

关键词： Web services

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Cluster Head Selection Algorithm using Machine Learning 4

Cluster Head Selection Algorithm using Machine Learning

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Nidhya, M.S. Gurumoorthi, E. Babu, M. Charumathi, A.C. Naved, Mohd Maram, Balajee School of Computer Applications Dayananda Sagar University Bangalore India School of Engineering Malla Reddy University India Chennai Institute of Technology College Dept of Ai & Ds Chennai India Sona College of Technology Dept of Information Technology India Jaipuria Institute of Management Dept of Business Analytics Noida India School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Warangal India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies. © 2025 IEEE.

关键词： Self-supervised learning

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Big data analytics in Healthcare——A Systematic Literature Review and Roadmap for Practical Implementation

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IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 1-22页

作者： Sohail Imran Tariq Mahmood Ahsan Morshed Timos Sellis the Faculty of Computer Science Karachi Institute of Economics and TechnologyKarachi 75190Pakistan the Faculty of Computer Science Institute of Business AdministrationKarachi 75270Pakistan the School of Engineering and Technology CQ UniversityMelbourne 3000Australia the Data Science Research Institute Swinburne University of TechnologyHawthorn 3122Australia

The advent of healthcare information management systems(HIMSs)continues to produce large volumes of healthcare data for patient care and compliance and regulatory requirements at a global *** of this big data allows for boundless potential outcomes for discovering *** data analytics(BDA)in healthcare can,for instance,help determine causes of diseases,generate effective diagnoses,enhance Qo S guarantees by increasing efficiency of the healthcare delivery and effectiveness and viability of treatments,generate accurate predictions of readmissions,enhance clinical care,and pinpoint opportunities for cost ***,BDA implementations in any domain are generally complicated and resource-intensive with a high failure rate and no roadmap or success strategies to guide the *** this paper,we present a comprehensive roadmap to derive insights from BDA in the healthcare(patient care)domain,based on the results of a systematic literature *** initially determine big data characteristics for healthcare and then review BDA applications to healthcare in academic research focusing particularly on No SQL *** also identify the limitations and challenges of these applications and justify the potential of No SQL databases to address these challenges and further enhance BDA healthcare *** then propose and describe a state-of-the-art BDA architecture called Med-BDA for healthcare domain which solves all current BDA challenges and is based on the latest zeta big data *** also present success strategies to ensure the working of Med-BDA along with outlining the major benefits of BDA applications to ***,we compare our work with other related literature reviews across twelve hallmark features to justify the novelty and importance of our *** aforementioned contributions of our work are collectively unique and clearly present a roadmap for clinical administrators,practitioners and professionals to successf

关键词： Big data analytics(BDA) big data architecture healthcare NoSQL data stores patient care roadmap systematic literature review

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Beyond Scaleup: Knowledge-aware Parsimony Learning from Deep Networks

arXiv

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arXiv 2024年

作者： Yao, Quanming Zhang, Yongqi Wang, Yaqing Yin, Nan Kwok, James Yang, Qiang Department of Electronic Engineering the Tsinghua University China Department of Data Science and Analytics Thrust the Hong Kong University of Science and Technology Guangzhou China Beijing Institute of Mathematical Sciences and Applications China Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong

The brute-force scaleup of training datasets, learnable parameters and computation power, has become a prevalent strategy for developing more robust learning models. However, due to bottlenecks in data, computation, and trust, the sustainability of this strategy is a serious concern. In this paper, we attempt to address this issue in a parsimonious manner (i.e., achieving greater potential with simpler models). The key is to drive models using domain-specific knowledge, such as symbols, logic, and formulas, instead of purely relying on scaleup. This approach allows us to build a framework that uses this knowledge as "building blocks" to achieve parsimony in model design, training, and interpretation. Empirical results show that our methods surpass those that typically follow the scaling law. We also demonstrate our framework in AI for science, specifically in the problem of drug-drug interaction prediction. We hope our research can foster more diverse technical roadmaps in the era of foundation models. © 2024, CC BY.

关键词： Contrastive Learning

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