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Intrusion Detection in NSL-KDD Dataset Using Hybrid Self-Organizing Map Model

computer Modeling in Engineering & Sciences 2025年第4期143卷 639-671页

作者： Noveela Iftikhar Mujeeb Ur Rehman Mumtaz Ali Shah Mohammed J.F.Alenazi Jehad Ali Knowledge Unit of Systems and Technology University of Management and TechnologySialkot51310Pakistan Department of Computer Science University ofWahWah Cantt47010Pakistan Department of Computer Engineering College of Computer and Information Sciences(CCIS)King Saud UniversityRiyadh11451Saudi Arabia Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea

Intrusion attempts against Internet of Things(IoT)devices have significantly increased in the last few *** devices are now easy targets for hackers because of their built-in security *** a Self-Organizing Map(SOM)hybrid anomaly detection system for dimensionality reduction with the inherited nature of clustering and Extreme Gradient Boosting(XGBoost)for multi-class classification can improve network traffic intrusion *** proposed model is evaluated on the NSL-KDD *** hybrid approach outperforms the baseline line models,Multilayer perceptron model,and SOM-KNN(k-nearest neighbors)model in precision,recall,and F1-score,highlighting the proposed approach’s scalability,potential,adaptability,and real-world ***,this paper proposes a highly efficient deployment strategy for resource-constrained network *** results reveal that Precision,Recall,and F1-scores rise 10%-30% for the benign,probing,and Denial of Service(DoS)*** particular,the DoS,probe,and benign classes improved their F1-scores by 7.91%,32.62%,and 12.45%,respectively.

关键词： Intrusion detection self-organizing map Internet of Things dimensionality reduction

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Double-branch fusion network with a parallel attention selection mechanism for camouflaged object detection

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Science China(Information Sciences) 2023年第6期66卷 258-266页

作者： Junjiang XIANG Qing PAN Zhengrong ZHANG Songnian FU Yuwen QIN Advanced Institute of Photonics Technology School of Information EngineeringGuangdong University of Technology Guangdong Provincial Key Laboratory of Photonics Information Technology Guangdong University of Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and InformationGuangxi University

To meet the challenge of camouflaged object detection （COD）,which has a high degree of intrinsic similarity between the object and background,this paper proposes a double-branch fusion network（DBFN）with a parallel attention selection mechanism （PASM）.In detail,a schismatic receptive field block（SRF）combined with an attention mechanism for low-level information is performed to learn texture features in one branch,and an integration of the SRF,a hybrid attention mechanism （HAM）,and a depth feature polymerization module （DFPM）is employed for high-level information to extract detection features in the other ***,both texture features and detection features are input into the PASM to acquire selective expression ***,the final result is obtained after further selective matrix optimization with atrous spatial pyramid pooling （ASPP）and a residual channel attention block （RCAB）being applied *** results on three public datasets verify that our method outperforms the state-of-the-art methods in terms of four evaluation metrics,i.e.,mean absolute error （MAE）,weighted F βmeasure （Fβω）,structural measure （Sα）,and E-measure （Eφ）

关键词： camouflaged object detection attention mechanism feature extraction feature aggregation texture information fuzzy boundary

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Remote Data Auditing for Cloud-Assisted WBANs with Pay-as-You-Go Business Model

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Chinese Journal of Electronics 2023年第2期32卷 248-261页

作者： LI Yumei ZHANG Futai School of Computer Science Hubei University of Technology Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal University

As an emerging technology, cloud-assisted wireless body area networks(WBANs) provide more convenient services to users. Recently, many remote data auditing protocols have been proposed to ensure the data integrity and authenticity when data owners outsourced their data to the cloud. However, most of them cannot check data integrity periodically according to the pay-as-you-go business model. These protocols also need high tag generation computation overhead, which brings a heavy burden for data owners. Therefore, we construct a lightweight remote data auditing protocol to overcome all above drawbacks. Our work can be deployed in the public environment without secret *** makes use of certificate-based cryptography which gets rid of certificate management problems, key escrow problems, and secret channels. The security analysis illustrates that the proposed protocol is secure. Moreover, the performance evaluation implies that our work is available in cutting down computation and communication overheads.

关键词： Wireless communication Performance evaluation Protocols Computational modeling Data integrity Data models Computational efficiency

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DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

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Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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A Malicious Domain Detection Method Based on DNS Logs 4

A Malicious Domain Detection Method Based on DNS Logs

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4th International Conference on Blockchain technology and Information Security, ICBCTIS 2024

作者： Ma, SiQi Pang, Tong Cui, Rui Yang, DeQuan School of Computer Science Beijing Institute of Technology Beijing China Beijing Institute of Technology Network Information Technology Center Beijing China

ISBN: (纸本)9798331528324

As the Internet continues to expand rapidly, the threat posed by malicious domain names to network security is on the rise. Cybercriminals exploit these domains to launch attacks such as malware infections, phishing scams, DDoS attacks, and botnets, posing a severe risk to both individuals and organizations. In light of this, research into effective methods for detecting malicious domains has become increasingly critical. However, traditional machine learning algorithms and blacklist-based approaches have proven ineffective against the sophisticated Domain Generation Algorithm (DGA) technology used by attackers. Recognizing the importance of DNS (Domain Name System) in detecting and mitigating malicious domains, research has focused on leveraging DNS information for detection. To overcome the limitation of robust feature engineering, This study presents a novel malicious domain detection method that fuses features using an attention mechanism. By integrating IP-domain name features and domain name character features through the attention mechanism, BiLSTM is employed to extract character features, while graph neural networks mine domain name-IP associations from DNS messages. Ultimately, the attention mechanism fuses these two types of features to enhance domain name detection, achieving an impressive accuracy rate of 92.38% on a subset of the CIC-Bell-DNS2021 dataset. © 2024 IEEE.

关键词： Phishing

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Towards Image-Based network Traffic Pattern Detection for DDoS Attacks in Cloud Computing Environments: A Comparative Study 14

Towards Image-Based Network Traffic Pattern Detection for DD...

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14th International Conference on Cloud Computing and Services Science, CLOSER 2024

作者： Abdullah, Azizol Bouke, Mohamed Aly Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia

ISBN: (纸本)9789897587016

With the increasing adoption of cloud computing and the emergence of Industry 4.0, the need for robust intrusion detection mechanisms to safeguard cloud-based systems against Distributed Denial of Services Attacks (DDoS) attacks has become more critical than ever. This study presents a comprehensive comparative analysis of traditional Machine Learning (ML) techniques and Deep Learning (DL) for DDoS attack detection in cloud environments. Utilizing the CIC-IDS 2017 dataset, transformed from tabular data into image-based formats for DL model compatibility, we evaluate 27,001 instances of normal traffic and 21,844 DDoS attack instances. We preprocess network traffic data and explore various DL architectures, including Convolutional Neural networks (CNN), Long Short-Term Memory (LSTM) networks, Bidirectional LSTM (BLSTM) networks, and Gated Recurrent Unit (GRU) networks. Additionally, we evaluate the performance of DL models against traditional ML algorithms, such as Random Forest (RF), Support Vector Machines (SVM), and Logistic Regression (LR), using standard evaluation metrics. Our results highlight the superior performance of DL models, particularly the BLSTM model, with an accuracy of 96.35%, precision of 97.42%, recall of 94.39%, F1 score of 95.88%, and ROC AUC score of 96.17%. Through in-depth analysis and discussion, we provide insights into the strengths and weaknesses of different intrusion detection mechanisms, such as the higher interpretability of ML models and the superior discriminatory power of DL models. These findings offer valuable guidance for practitioners and researchers in enhancing the security of cloud-based systems against DDoS attacks. © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Denial-of-service attack

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AI-enabled dental caries detection using transfer learning and gradient-based class activation mapping

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Journal of Ambient Intelligence and Humanized Computing 2024年第7期15卷 3009-3033页

作者： Inani, Hardik Mehta, Veerangi Bhavsar, Drashti Gupta, Rajeev Kumar Jain, Arti Akhtar, Zahid Department of Computer Science and Engineering Pandit Deendayal Energy University Gujarat Gandhinagar India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida India Department of Network and Computer Security State University of New York Polytechnic Institute NY United States

Dental caries detection holds the key to unlocking brighter smiles and healthier lives by identifying one of the most common oral health issues early on. This vital topic sheds light on innovative ways to combat tooth decay, empowering individuals to take control of their oral health and maintain radiant smiles. This research paper delves into the realm of transfer learning techniques, aiming to elevate the precision and efficacy of dental caries diagnosis. Utilizing Keras ImageDataGenerator, a rich and balanced dataset is crafted by augmenting teeth images from the Kaggle teeth dataset. Five cutting-edge pre-trained architectures are harnessed in the transfer learning approach: EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50, with each model, initialized using ImageNet weights and tailored top layers. A comprehensive set of evaluation metrics, encompassing accuracy, precision, recall, F1-score, and false negative rates are employed to gauge the performance of these architectures. The findings unveil the unique advantages and drawbacks of each model, illuminating the path to an optimal choice for dental caries detection using Grad-CAM (Gradient-weighted Class Activation Mapping). The testing accuracies achieved by EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models stand at 95.89%, 96.58%, 93.15%, 93.15%, and 94.18%, respectively. The Training accuracies stood at 100%, 99.91%, 100%, 100% and 100%, meanwhile on validation we achieved 97.63%, 96.68%, 98.82%, 96.68%, and 100% accuracies for EfficientNetV2B3, VGG19, InceptionResNetV2, Xception, and ResNet50 models respectively. Capitalizing on transfer learning and juxtaposing diverse pre-trained architectures, this research paper paves the way for substantial advancements in dental diagnostic capabilities, culminating in enhanced patient outcomes and superior oral health. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Chemical activation

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Named Entity Recognition in Electronic Medical Records Incorporating Pre-trained and Multi-Head Attention

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2024年第4期51卷 401-408页

作者： Yang, Haotian Wang, Li Yang, Yanpeng School of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China Network Information Centre University of Science and Technology Liaoning CO Anshan114051 China

Chinese Named Entity Recognition (NER) for Electronic Medical Records (EMR) is a fundamental task in building a digital hospital and is widely considered to be a sequence annotation problem in the Natural Language Processing domain. (NLP). However, existing deep learning sequence annotation models cannot fully use the large amount of unannotated data for Chinese EMRs that contain a vast number of professional unregistered words, named entities, and inter-of-entity relationships carrying rich professional knowledge. Moreover, the syntactic structure of EMR sentences is complex, and the text is long;the features of the EMR documents often cannot be captured deeply. Aiming at these two problems, this paper proposes a deep learning method that combines Multi-Head Attention with a pre-trained language model. (BERT-BiGRU-Att-CRF). The method uses the BERT pre-trained model to obtain dynamic word vectors combined with contextual information, extracts global semantic features through a Bidirectional Gated Recurrent Unit (BiGRU), obtains augmented semantic features by using the Multi-Head Attention. Finally, using Conditional Random Field (CRF) decoding, outputs the globally optimal label sequence with greatest probability. The model is trained using the CCKS2019 Chinese EMR dataset containing six types of entities: anatomical sites, surgeries, diseases and diagnoses, medicines, laboratory tests, and imaging tests, and good results are achieved with an F1 score of 86.97%. © (2024) International Association of Engineers.

关键词： Random processes

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Ground Penetrating Radar Image De-Noising Method Based on Multi-Noise and Self-Supervised Learning 5

Ground Penetrating Radar Image De-Noising Method Based on Mu...

引用

5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

作者： Huang, Yaoxin Zhou, Wenlong School of Computer and Network Security Chengdu University of Technology Chengdu China

ISBN: (纸本)9798350336030

The quality of ground penetrating radar (GPR) images is usually low due to unwanted noise interference such as antenna coupling, soil reflection, and industrial frequency interference, which often affects subsequent studies. Deep learning methods in the field of ground penetrating radar image denoising mainly include non-blind denoising studies based on a single additive Gaussian white noise, and blind denoising studies based on masks. Due to the presence of multiple composite noises in GPR images, methods based on such a single noise perform poorly in practical denoising work;and blind denoising deep learning methods based on image masking lose feature details of anomalous regions. To solve the above problems, this paper proposes a multi-noise self-supervised denoising model MNSSDN for GPR image denoising, including a noise generator, a denoising generation network and a label pre-denoising module;meanwhile a corresponding loss function is designed to train the whole network. Firstly, the GPR image is input to the noise generator to obtain the image with random horizontal noise, and then passed into the denoising generation network for denoising. Next, integrate them into one fusion image. Finally, the output of the label pre-denoising module is used as the label for backpropagation to constrain the evolution direction of the network. © 2023 IEEE.

关键词： Image denoising

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