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Optimizing Intrusion Detection for IoT: A Systematic Review of machine learning and deep learning Approaches With Feature Selection and Data Balancing

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WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY 2025年第2期15卷

作者： Mallidi, S. Kumar Reddy Ramisetty, Rajeswara Rao Jawaharlal Nehru Technol Univ Kakinada Univ Coll Engn Comp Sci & Engn Kakinada India Sri Vasavi Engn Coll Comp Sci & Engn Tadepalligudem India Jawaharlal Nehru Technol Univ Gurajada Vizianagara Comp Sci & Engn Vizianagaram India

As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) are fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims to guide future research by addressing six pivotal research questions that underscore the development of advanced IDS tailored for IoT environments. Specifically, the review concentrates on applying machine learning (ML) and deep learning (DL) technologies to enhance IDS capabilities. It explores various feature selection methodologies aimed at developing lightweight IDS solutions that are both effective and efficient for IoT scenarios. Additionally, the review assesses different datasets and balancing techniques, which are crucial for training IDS models to perform accurately and reliably. Through a comprehensive analysis of existing literature, this review highlights significant trends, identifies current research gaps, and suggests future studies to optimize IDS frameworks for the ever-evolving IoT landscape.

关键词： dataset balancing feature selection Intrusion Detection Systems (IDS) IoT security machine learning and deep learning

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A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using machine learning and deep learning Algorithms in Wireless Sensor Networks of Industry 4.0

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IEEE ACCESS 2024年 12卷 34800-34819页

作者： Al-Quayed, Fatima Ahmad, Zulfiqar Humayun, Mamoona Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Hazara Univ Dept Comp Sci & Informat Technol Mansehra 21300 Pakistan Jouf Univ Coll Comp & Informat Sci Dept Informat Syst Sakaka 72388 Saudi Arabia

Industry 4.0 is fundamentally based on networked systems. Real-time communication between machines, sensors, devices, and people makes it easier to transmit the data needed to make decisions. Informed decision-making is empowered by the comprehensive insights and analytics made possible by this connectedness in conjunction with information transparency. Industry 4.0-based wireless sensor networks (WSNs) are an integral part of modern industrial operations however, these networks face escalating cybersecurity threats. These networks are always vulnerable to cyber-attacks as they continuously collect data and optimize processes. Increased connections make people more susceptible to cyberattacks, necessitating the use of strong cybersecurity measures to protect sensitive data. This study proposes a predictive framework intended to intelligently prioritize and prevent cybersecurity intrusions on WSNs in Industry 4.0. The proposed framework enhances the cybersecurity of WSNs in Industry 4.0 using a multi-criteria approach. It implements machine-learning and deep-learning algorithms for cybersecurity intrusion detection in WSNs of Industry 4.0 and provides prevention by assigning priorities to the threats based on the situation and nature of the attacks. We implemented three models, i.e., Decision Tree, MLP, and Autoencoder, as proposed algorithms in the framework. For multidimensional classification and detection of cybersecurity intrusions, we implemented Decision Tree and MLP models. For binary classification and detection of cybersecurity intrusions in WSNs of Industry 4.0, we implemented Autoencoder model. Simulation results show that the Decision Tree model provides an accuracy of 99.48%, precision of 99.49%, recall of 99.48%, and F1 score of 99.49% in the detection and classification of cybersecurity intrusions. The MLP model provides an accuracy of 99.52%, precision of 99.5%, recall of 99.5%, and F1 score of 99.5% in the detection and classification of cybersecuri

关键词： Wireless sensor networks Computer security Fourth Industrial Revolution Sensors Intrusion detection Wireless communication Cyberattack Detection algorithms Predictive models machine learning deep learning Cybersecurity WSN detection prediction intrusions machine learning and deep learning

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A review of quantum communication and information networks with advanced cryptographic applications using machine learning, deep learning techniques

Franklin Open

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Franklin Open 2025年 10卷

作者： Ramya, R. Kumar, P. Dhanasekaran, D. Kumar, R. Satheesh Sharavan, S. Amithesh Department of ECE K.S.Rangasamy College of Technology Tamil Nadu India

Quantum communication and information networks offer unprecedented processing efficiency and security for data transfers. Technologies like quantum key distribution (QKD), quantum repeaters, quantum memory, and quantum entanglement sources are enabling technologies that ensure secure communication. QKD methods use quantum physics concepts to establish cryptographic keys, while quantum repeaters compensate for signal loss in fiber optic cables. Quantum memory enables storage and retrieval of quantum information, while quantum entanglement generators generate entangled photon pairs. Artificial intelligence and machine learning have significantly improved the efficacy and security of quantum communication. These methods can facilitate ultra-secure, reliable, large-scale communication and even a future quantum internet by analyzing quantum protocols and mitigating noise-induced errors. QKD protocols ensure secure communication channels, while quantum repeaters establish secure long-distance communication lines by mitigating signal loss. Traditional Internet technology is susceptible to surveillance due to Shannon's information theory and mathematics. The quantum internet (QI) uses satellite-based quantum communication and quantum cryptographic protocols for physical QI. Quantum entanglement sources fortify secure communication protocols, improving resistance to attacks. Quantum information networks (QINs) enhance computing, sensing, and security capabilities over extended distances. The development of reliable, robust communication systems depends on these technologies. Quantum-secured networks enhance urban security, smart management, and infrastructure protection by providing data privacy and facilitating secure data transmission, thereby facilitating robust infrastructure and technological development. © 2025

关键词： AI Industry, innovation, and infrastructure machine learning and deep learning Privacy Protocols Quantum communication and networking Security Sustainable cities and communities

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Assessing the Efficacy of machine learning and deep learning in the Field of Cyber Security

Assessing the Efficacy of Machine Learning and Deep Learning...

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2023 Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE 2023

作者： Eswarawaka, Rajesh Nijim, Mais Kanumuri, Viswas Albetaineh, Hisham Acm Engineering College Department of Machine Learning and Computer Science Karnataka Bangalore560076 India Texas A&m University-Kingsville Department of Electrical Engineering and Computer Science KingsvilleTX78363 United States

ISBN: (纸本)9798350327595

The use of machine learning has become widespread across various fields because of its superior performance compared to conventional rule-based algorithms. As a result, these models have also been integrated into cyber security systems, machine learning is being utilized to aid or possibly even supplant the role of human security analysts. However, it's important to evaluate the effectiveness of machine learning in cyber security with careful consideration, especially if complete automation of detection and analysis is being considered. This study provides an in-depth research focuses on machine learning techniques applied in intrusion, malware, and spam detection that are tailored towards security professionals. The primary objective of our study is to evaluate the degree of advancement or maturity of these techniques of ML-based cybersecurity solutions and to identify any limitations that could impede their effectiveness as detection mechanisms. To achieve this, we conducted a thorough literature review and performed experiments on enterprise systems and network traffic in real-world settings. Our goal is to gain understanding of the capabilities and limitations of ML solutions and provide actionable insights for their improvement. © 2023 IEEE.

关键词： cyber security machine learning and deep learning

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Soil profile analysis using interactive visualizations, machine learning, and deep learning

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2021年 191卷 106539-106539页

作者： Pham, Vung Weindorf, David C. Dang, Tommy Sam Houston State Univ Dept Comp Sci Huntsville TX 77340 USA Cent Michigan Univ Dept Earth & Atmospher Sci Mt Pleasant MI 48859 USA Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

Soil is an essential element of life, and soil properties are crucial in analyzing soil health. Recent developments of proximal sensor technologies, such as portable X-ray fluorescence (pXRF) spectroscopy or visible and near-infrared (Vis-NIR) spectroscopy, offer rapid and non-destructive alternatives for quantifying data from soil profiles. While the data collection time using these technologies decreases significantly, the subsequent analysis remains time-consuming, and current analysis solutions only provide basic visualizations. Furthermore, the use of collected data from proximal sensors to predict high-level soil properties has garnered worldwide attention in the past decade, owing to its convenience. Therefore, this paper discusses the objectives for software solutions in this area, consolidated from interviewing 102 stakeholders. Following these requirements, data visualizers work closely with soil scientists to propose a set of interactive visualizations for analyzing soil profiles using pXRF data. These interactive visualizations receive positive feedback from the domain experts. This project also explores various machine learning and deep learning approaches to predict soil properties from spectral data. This work then proposes a deep learning model called RDNet that achieves state-of-the-art results in predicting pH(H2O) and pH(KCl) from Vis-NIR spectra acquired from a set of globally distributed soil samples.

关键词： Chemical measurement data analysis Intelligent visual analytics pXRF Data visualization Soil property predictions Vis-NIR spectra machine learning and deep learning

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Artificial intelligence-enabled detection and assessment of Parkinson's disease using multimodal data: A survey

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INFORMATION FUSION 2025年 121卷

作者： Zhao, Aite Liu, Yongcan Yu, Xinglin Xing, Xinyue Zhou, Huiyu Qingdao Univ Coll Comp Sci & Technol Qingdao 266071 Peoples R China Univ Leicester Sch Comp & Math Sci Leicester LE17RH England

Highly adaptable and reusable AI models are revolutionizing the diagnosis and management of Parkinson's disease (PD). A wide range of AI algorithms, including machine learning and deep learning techniques, are now being employed for PD diagnosis and treatment. These algorithms leverage multimodal data, such as gait patterns, hand movements, and speech characteristics, to predict the likelihood of PD, evaluate symptom severity, facilitate early detection, and assess the effectiveness of treatments, demonstrating their advanced diagnostic potential. This paper provides a comprehensive review of machine learning and deep learning approaches for PD detection and assessment over the past decade, emphasizing their strengths, addressing their limitations, and exploring their potential to inspire new research directions. Additionally, it offers a curated collection of publicly available multimodal datasets focused on PD motor symptoms and validates the performance of these algorithms on privately collected datasets. Experimental results reveal that AI technologies for PD assessment have progressed from traditional methods to convolutional and sequential neural networks, and further to Transformer-based models, achieving consistently improving accuracy and establishing benchmarks for future advancements.

关键词： Parkinson's disease Artificial intelligence Symptoms Multimodal Diagnosis machine learning and deep learning

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A machine learning Framework for Major Depressive Disorder (MDD) Detection Using Non-invasive EEG Signals

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WIRELESS PERSONAL COMMUNICATIONS 2025年第1-2期140卷 39-61页

作者： Bashir, Nayab Narejo, Sanam Naz, Bushra Ismail, Fatima Anjum, Muhammad Rizwan Butt, Ayesha Anwar, Sadia Prasad, Ramjee Mehran Univ Engn & Technol Dept Biomed Engn Jamshoro Pakistan Mehran Univ Engn & Technol Dept Comp Syst Engn Jamshoro Pakistan Islamia Univ Bahawalpur Dept BBT Bahawalpur 63100 Pakistan Islamia Univ Bahawalpur Dept Elect Engn Bahawalpur 63100 Pakistan Aarhus Univ Dept Business Dev & Technol Aarhus Denmark

According to World Health Organization (WHO) report, every 40 seconds a person attempts suicide globally. Depression, one of the world's most prevailing diseases has become a reason behind these suicides. It is believed that early diagnosis of major depressive disorder (MDD) can reduce the adversity of this heinous deformity. For few years various machine learning and advanced neurocomputing techniques are being utilized in Electroencephalogram (EEG) based detection of multiple neurological diseases. In the proposed study, an EEG based screening of MDD is presented while using various machine learning and one deep learning approach. The majority of previous EEG based MDD decoding research has concentrated on a limited features. It was necessary to conduct in-depth comparisons of different approaches, besides more detailed feature-based EEG analysis. This research starts with the creation of a complete feature-based framework, which is then further compared against the state of the art end to end techniques. The K-nearest neighbors (KNN) model outperformed the other models and gained an accuracy of 87.5%. While long short term memory (LSTM) model acquired an accuracy of 83.3%. This study can further support in clinical diagnosis of multiple stages of MDD and can attempt to provide an early intervention.

关键词： Neurocomputing Major depressive disorder Feature based framework machine learning and deep learning

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XDQEDCNN: Design of an efficient explainable model using deep Q-Network and enhanced deep convolutional neural network for Distributed Denial-of-Service (DDoS) attack forensic analysis and investigation

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INFORMATION SECURITY JOURNAL 2025年

作者： Solanki, Meghana Chaudhari, Sangita DY Patil Deemed Univ Nerul Ramrao Adik Inst Technol Dept Comp Engn Navi Mumbai 400706 Maharashtra India

As Distributed Denial-of-Service (DDoS) attacks become more frequent and sophisticated, this paper addresses the limitations of existing network forensic analysis and investigation methods. It introduces an integrated model that merges network forensic analysis and investigation to enhance network security. The model functions across three phases: Attack Pattern Discovery, Analysis utilizing deep Q-Network (DQN) & Explainable AI (XAI) and Investigation employing Enhanced deep Convolutional Neural Network (EDCNN). Through empirical evaluation, the proposed model demonstrates notable enhancements in identifying and investigating forensic events. For forensic event identification, it demonstrates an 8.9% improvement in precision, 4.% in accuracy, 6.8% in recall, 6.7% in Area Under the Curve (AUC) and 5.9% in specificity. It also improves precision by 8.0%, accuracy by 5.7%, recall by 6.3%, AUC by 6.5% and specificity by 5.7% while studying forensic events. These results demonstrate how well the model works to offer a practical and efficient solution for network forensic analysis and investigation. This study represents important advancements in the field by highlighting the potential of fusing deep learning architectures with machine learning approaches to develop and improve network security solutions.

关键词： DDoS attacks machine learning and deep learning network forensic analysis network security

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LSTM and ResNet18 for optimized ambulance routing and traffic signal control in emergency situations

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SCIENTIFIC REPORTS 2025年第1期15卷 1-25页

作者： Alruwaili, Madallah Ali, Ali Almutairi, Mohammed Alsahyan, Abdulaziz Mohamed, Mahmood Jouf Univ Coll Comp & Informat Sci Dept Comp Engn & Networks Sakaka Aljouf Saudi Arabia Cairo Univ Fac Grad Studies Stat Res Dept Informat Syst & Technol Giza Egypt

Traffic congestion, particularly in rapidly expanding urban centers, significantly impacts the timely delivery of emergency medical services (EMS), where every minute can mean the difference between life and death. Traditional traffic signal control systems often lack real-time adaptability to prioritize emergency vehicles, resulting in delays caused by congestion around ambulances. To address this critical issue, this paper presents an AI-driven real-time traffic management system designed to reduce EMS response times. The proposed solution incorporates three core components: Raspberry Pi-based traffic signal prioritization, deep learning-enabled audio-visual ambulance detection, and an advanced intelligent traffic management framework. For audio detection, raw data is transformed into spectrograms using Mel Frequency Cepstral Coefficients (MFCCs) and classified using a Long Short-Term Memory (LSTM) network. Visual data is processed through a ResNet18 convolutional neural network, pre-trained on ImageNet using inductive transfer learning. The outputs from the auditory and visual streams are integrated using empirical risk minimization, enabling accurate ambulance detection through multimodal data fusion. Performance evaluation demonstrates the effectiveness of the proposed system, achieving 98.3% accuracy in audio classification, 98.1% accuracy in visual classification, and 99% accuracy with the fused model. Additional metrics, including precision, recall, F1-score, and a confusion matrix, confirm the model's reliability. This innovative system has the potential to transform urban traffic networks into intelligent, adaptive systems, reducing delays caused by traffic congestion, enhancing emergency medical care response times, and ultimately saving lives. The framework offers a scalable blueprint for future smart city traffic management solutions, meticulously designed to support urban growth and expansion.

关键词： Image recognition Sound recognition Hybrid intelligent systems Hybrid learning Real-time systems Microcontrollers Python machine learning and deep learning

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Harvesting Insights: Unrevealing Paddy Crop Yields Through Advanced Modeling Techniques 2nd

Harvesting Insights: Unrevealing Paddy Crop Yields Through A...

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2nd International Conference on Agriculture-Centric Computation

作者： Tummala, Mounika Aridoss, Manimaran Babu, Khadar S. K. VIT AP Univ Sch Adv Sci Amaravati 522237 India Vellore Inst Technol Dept Math Vellore 632014 Tamil Nadu India

ISBN: (纸本)9783031744396;9783031744402

This research examines on the effectiveness of various machine learning and deep learning models for predicting the yield of paddy crop in East Godavari District, Andhra Pradesh, India. Dataset consist of 91 entries representing 91-day paddy crop growth cycle from 2000 to 2009, this study forecast yield for the years 2017 and 2018 using machine learning models such as Decision Tree, Random Forest, Gradient Boosting, AdaBoost, and Support Vector machines (SVM) and deep learning models such as 1D Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Multilayer Perceptron's (MLP), Feedforward Neural Networks (FNN), and Gated Recurrent Units (GRU). Performance of the Model is evaluated by using R-square, Mean Square Error (MSE) and Mean Absolute Error (MAE).

关键词： Agriculture Yield machine learning and deep learning

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