检索结果-内蒙古大学图书馆

Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review

引用

Discover Internet of Things 2025年第1期5卷 1-33页

作者： Mallidi, S. Kumar Reddy Ramisetty, Rajeswara Rao Computer Science and Engineering Jawaharlal Nehru Technological University AP Kakinada533003 India Computer Science and Engineering Sri Vasavi Engineering College AP Tadepalligudem534101 India Computer Science and Engineering Jawaharlal Nehru Technological University Gurajada AP Vizianagaram535003 India

As the Internet of Things (IoT) grows, ensuring robust security is crucial. Intrusion Detection Systems (IDS) protect IoT networks from various cyber threats. This systematic literature review (SLR) explores the advancements in training and deployment strategies of Artificial Intelligence (AI) based IDS within IoT environments. The study begins by outlining prevalent IoT attacks and developing an updated taxonomy to enhance understanding of these threats. It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. Significant emphasis is placed on analyzing different IDS training paradigms-centralized, distributed, and federated learning-and deployment strategies, including in cloud, fog, and edge layers. Their effectiveness within IoT contexts is evaluated comprehensively. Moreover, the review assesses the datasets commonly used for training and validating IDS and discusses key performance metrics and validation measures critical for assessing IDS effectiveness. The study concludes by synthesizing the major challenges facing current IDS implementations in IoT and suggesting future research directions aimed at overcoming these hurdles. This review highlights the technological advancements in IDS and sets the stage for future enhancements in IoT security, emphasizing the integration of innovative training and deployment strategies. © The Author(s) 2025.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

ANovel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

引用

computer Modeling in engineering & sciences 2023年第7期136卷 87-106页

作者： Bejoy Abraham Jesna Mohan Linu Shine Sivakumar Ramachandran Department of Computer Science and Engineering College of Engineering MuttatharaThiruvananthapuramKerala695008India Department of Computer Science and Engineering Mar Baselios College of Engineering and TechnologyThiruvananthapuramKerala695015India Department of Electronics and Communication Engineering College of Engineering TrivandrumKerala695016India

Tear film,the outermost layer of the eye,is a complex and dynamic structure responsible for tear *** tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular *** eye syndrome(DES)is a symptomatic disease caused by reduced tear production,poor tear quality,or excessive *** diagnosis is a difficult task due to its multifactorial *** of several clinical tests available,the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES *** instrument known as Tearscope Plus allows the rapid assessment of the lipid layer.A grading scale composed of five categories is used to classify lipid layer *** reported work proposes the design of an automatic system employing light weight convolutional neural networks(CNN)and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope *** designed framework achieves promising results compared with the existing state-of-the-art techniques.

关键词： Dry-eye syndrome tearscope plus tear film deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

引用

computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

来源：评论

学校读者我要写书评

暂无评论

Augmented FCN: rethinking context modeling for semantic segmentation

引用

science China(Information sciences) 2023年第4期66卷 193-211页

作者： Dong ZHANG Liyan ZHANG Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggregating content-and position-based object contexts for semantic ***, motivated because each deep feature map is a global, class-wise representation of the input,we first propose an augmented nonlocal interaction(AugNI) to aggregate the global content-based contexts through all feature map interactions. Compared to classical position-wise approaches, AugNI is more efficient. Moreover, to eliminate permutation equivariance and maintain translation equivariance, a learnable,relative position embedding branch is then supportably installed in AugNI to capture the global positionbased contexts. AugFCN is built on a fully convolutional network as the backbone by deploying AugNI before the segmentation head network. Experimental results on two challenging benchmarks verify that AugFCN can achieve a competitive 45.38% mIoU(standard mean intersection over union) and 81.9% mIoU on the ADE20K val set and Cityscapes test set, respectively, with little computational overhead. Additionally, the results of the joint implementation of AugNI and existing context modeling schemes show that AugFCN leads to continuous segmentation improvements in state-of-the-art context modeling. We finally achieve a top performance of 45.43% mIoU on the ADE20K val set and 83.0% mIoU on the Cityscapes test set.

关键词： semantic segmentation context modeling long-range dependencies attention mechanism

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach for Network Vulnerability Analysis in IIoT

引用

computer Systems science & engineering 2023年第4期45卷 263-277页

作者： K.Sudhakar S.Senthilkumar Department of Computer Science and Engineering University College of EngineeringPattukkottaiRajamadam614701India

Industrial Internet of Things(IIoT)offers efficient communication among business partners and *** an enlargement of IoT tools connected through the internet,the ability of web traffic gets *** to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in *** proposed MPDQDJREBC technique includes feature selection and ***,the network traffic features are collected from the *** applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time *** the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost *** Rocchio Emphasis Boost Classification technique combines the weak learner result into strong *** Rocchio classification technique is considered as the weak learners to identify the normal and ***,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic *** assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time *** assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection *** observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

关键词： Industrial internet of things(iiot) attack detection features selection maximum posterior dichotomous quadratic discriminant analysis jaccardized rocchio emphasis boost classification

来源：评论

学校读者我要写书评

暂无评论

A supervised deep learning-based sentiment analysis by the implementation of Word2Vec and GloVe Embedding techniques

引用

Multimedia Tools and Applications 2025年第2期84卷 979-1012页

作者： Rakshit, Pranati Sarkar, Avik Computer Science & Engineering JIS College of Engineering Block A Phase III West Bengal Kalyani741235 India

Sentiment analysis provides valuable insights into people’s opinions, emotions, and attitudes, enabling businesses to make more informed decisions, improve customer satisfaction, and stay competitive in today’s market. Now-a-days due to the accessibility of social networking platforms like Twitter, Instagram, Facebook, WeChat, etc a bulk of data is being generated within a very small span of time. These data inherit highly potential information hidden in them. Consequently, these data may be analyzed using powerful tools of deep learning techniques to achieve tremendous societal benefits. Keeping these views in mind, the authors have planned an extensive experimental study to undertake the task of sentiment analysis. In this present work, thirty-six different Word2Vec and GloVe Embedded deep learning models have been developed on the deep learning based architecture namely Multilayer Perceptron (MLP), Convolutional Neural Network(CNN), Long-and-Short-Term Memory Network (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). All the developed models are compared based on their accuracies, F1-scores, and other evaluation parameters. Several promising results have emerged from this study. It has been observed that the Word2Vec embedded Bi-directional GRU model yields the best result with an average F1-score of 0.84 in the case of a train-test ratio of 80:20. Finally this type of elaborated comparative and comprehensive study may be considered unique in its nature and supposed to be used to develop expert sentiment analyser. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

DDSS: Driver decision support system based on the driver behaviour prediction to avoid accidents in intelligent transport system

引用

International Journal of Cognitive Computing in engineering 2024年第1期5卷 1-13页

作者： S, Balasubramani D, John Aravindhar Renjith, P.N. Ramesh, K. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially catastrophic accidents. In order to anticipate and handle unusual driving behavior in Intelligent Transportation Systems (ITS), this research presents a unique Driver Decision Support System (DDSS). A reliable driving behavior prediction system is used by the suggested DDSS to categorize drivers as displaying normal or abnormal behavior. In order to prevent accidents in ITS scenarios, the system reliably detects anomalous driving patterns and advises nearby vehicles to change lanes or alter speed. The driver behavior prediction algorithm efficiently groups drivers into behavior categories using the K-Means clustering method. In order to evaluate the algorithm's efficacy, a comparative analysis is conducted by comparing its outcomes against those of Support Vector Machines (SVMs), Decision Trees, K-Nearest Neighbours (KNN), Logistic Regression, and Naïve Bayes. The integration of the Driver Decision Support System into the Intelligent Transportation System infrastructure serves to augment endeavours in accident prevention. Monitoring and analysis of driver behavior enable timely interventions, promoting safer driving practices and reducing accident risks. This research helps to create a more effective transportation system by reducing the number of accidents brought on by reckless driving. Because of its novel method to anticipating and controlling driver behavior, the proposed DDSS has promise for improving road safety and preventing accidents. The efficacy and the dependability of the driver behavior prediction algorithm are confirmed by the experimental assessment. © 2023

关键词： K-means clustering

来源：评论

学校读者我要写书评

暂无评论

Classification of autism severity levels using facial features and eye gaze patterns

引用

Multimedia Tools and Applications 2025年第16期84卷 15853-15866页

作者： Jiji, G. Wiselin Department of Computer Science and Engineering Dr. Sivanthi Aditanar College of Engineering Tamil Nadu Thiruchendur India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with varying degrees of severity. Early diagnosis and classification of autism severity are crucial for personalized intervention and support. This study proposes a novel approach using image processing techniques to analyze facial features and eye gaze patterns to differentiate between mild, moderate, and severe autism cases. A comprehensive dataset comprising facial videos and images of individuals aged 2 to 12 years with ASD was collected, along with normally developing children. Features such as facial asymmetry is calculated using SIFT feature, eye size, inter-eye distance, and eye openness were extracted for both groups using canny and adaptive thresholding techniques. Support Vector Machine (SVM) classifiers were employed to classify autism cases into three severity levels. Results revealed that eye gaze patterns were significantly lower for autism cases and higher for severe cases. Facial asymmetry was higher for autism cases, showing greater deviations from mild to severe cases. Severe autism cases exhibited extreme stiffness in facial muscle control, leading to the absence of facial expressions. Additionally, inter-eye distance increased, and eye openness decreased for severe autism cases. The proposed method demonstrates promising discrimination performance, as evidenced by high accuracy of 98.70 and sensitivity and specificity of 100 and 97.30. This research contributes valuable insights into the potential use of image processing techniques for early autism diagnosis and effective severity classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Building a Robust and Efficient Defensive System Using Hybrid Adversarial Attack

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4470-4478页

作者： Dhanaraj, Rachel Selva Sridevi, M. Goa College of Engineering Computer Engineering Department Goa 403401 India National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Adversarial attack is a method used to deceive machine learning models, which offers a technique to test the robustness of the given model, and it is vital to balance robustness with accuracy. Artificial intelligence (AI) researchers are constantly trying to find a better balance to develop new techniques and approaches to minimize loss of accuracy and increase robustness. To address these gaps, this article proposes a hybrid adversarial attack strategy by utilizing the Fast Gradient Sign Method and Projected Gradient Descent effectively to compute the perturbations that deceive deep neural networks, thus quantifying robustness without compromising its accuracy. Three distinct datasets - CelebA, CIFAR-10, and MNIST - were used in the extensive experiment, and six analyses were carried out to assess how well the suggested technique performed against attacks and defense mechanisms. The proposed model yielded confidence values of 99.99% for the MNIST dataset, 99.93% for the CelebA dataset, and 99.99% for the CIFAR-10 dataset. Defense study revealed that the proposed model outperformed previous models with a robust accuracy of 75.33% for the CelebA dataset, 55.4% for the CIFAR-10 dataset, and 98.65% for the MNIST dataset. The results of the experiment demonstrate that the proposed model is better than the other existing methods in computing the adversarial test and improvising the robustness of the system, thereby minimizing the accuracy loss. © 2020 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

引用

Tsinghua science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy Mohammed Yousuf Uddin Sultan Ahmad Department of Information Systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：