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An Intelligent Multi-Stage GA–SVM Hybrid Optimization Framework for Feature Engineering and Intrusion Detection in Internet of Things Networks 认领 引用
1
作者 Isam Bahaa Aldallal Abdullahi Abdu Ibrahim Saadaldeen Rashid Ahmed 《Computers, Materials & Continua》 SCIE EI 2026年第4期985-1007,共23页
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n... The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches. 展开更多
关键词 Cybersecurity intrusion detection system(IDS) IoT support vector machines(SVM) genetic algorithms(GA) feature selection NSL-KDD dataset anomaly detection
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An Intelligent IoT-Enabled Real-Time Space Monitoring System for Urban Parking and Smart Manufacturing Logistics 认领 引用
2
作者 Isam Bahaa Aldallal Saadaldeen Rashid Ahmed +4 位作者 Abdullahi Abdu Ibrahim Oguz Bayat Abu Saleh Musa Miah Fahmid Al Farid Md.Hezerul Abdul Karim 《Computers, Materials & Continua》 SCIE EI 2026年第8期1819-1839,共21页
Urban parking problems worsen traffic jams,gas use,and pollution.Old parking systems often lack up-to-date space information,which annoys drivers and wastes their time.This research presents a smart IoT-enabled real-t... Urban parking problems worsen traffic jams,gas use,and pollution.Old parking systems often lack up-to-date space information,which annoys drivers and wastes their time.This research presents a smart IoT-enabled real-time space monitoring and booking system applicable to both urban parking management and Smart Manufacturing logistics environments,including loading bay coordination and Automated Guided Vehicle(AGV)docking station management.The system employs ultrasonic and IR sensors,managed by an Arduino UNO,to identify vehicles and track space availability.A servo-motor regulates entry.Slot data is presented on a Liquid Crystal Display screen and accessible through a mobile app.The tests suggest that the system is accurate(98.67%)and reduces entry and exit times to 1–2 s for gate actuation,and it can handle increased demand well.Proteus simulations support the system’s reliability.Real-time updates to slot availability improve the user experience and overall system efficiency in both urban and industrial deployment scenarios. 展开更多
关键词 IoT-based parking system Arduino UNO(ATmega328P) infrared(IR)photoelectric sensors real-time slot-occupancy detection intelligent transportation systems smart manufacturing logistics loading-bay monitoring Industry 4.0
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IntrusionNet:Deep Learning-Based Hybrid Model for Detection of Known and Zero-Day Attacks 认领 引用
3
作者 Sarmad Dheyaa Azeez Saadaldeen Rashid Ahmed +3 位作者 Muhammad Ilyas Abu Saleh Musa Miah Fahmid Al Farid Md.Hezerul Abdul Karim 《Computers, Materials & Continua》 SCIE EI 2026年第7期1220-1241,共22页
Traditional Intrusion Detection Systems(IDSs)that rely on fixed signatures or basic machine learning often struggle with sophisticated,multi-stage cyberattacks and previously unknown threats.To fix these problems,this... Traditional Intrusion Detection Systems(IDSs)that rely on fixed signatures or basic machine learning often struggle with sophisticated,multi-stage cyberattacks and previously unknown threats.To fix these problems,this paper introduces IntrusionNet,a mixed deep learning system that combines Convolutional Neural Networks(CNN),Recurrent Neural Networks(RNN),and Autoencoders in a two-part design.Differing from typical stacked models,IntrusionNet works on two levels at the same time.First,a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns.Second,an unsupervised Autoencoder process spots new anomalies by looking at reconstruction error limits.This approach allows the automatic learning of threat traits as they change,without needing someone to do it by hand.The system was tested on the UNSW-NB15 data set,picked because it realistically includes many kinds of attacks,like Fuzzers,Shellcode,and Worms.Tests show that IntrusionNet gets an accuracy of 98.80%and an F1-score of 0.985,doing better than other systems,especially with less common attack types.Also,tests using Precision-Recall(PR)analysis and False Positive Rate(FPR)measurements prove that the model handles class imbalance well,which is key for real-world security.The suggested system can be scaled up easily and performs calculations fast,making it a possible key part of real-time detection in Security Information and Event Management(SIEM)systems. 展开更多
关键词 Intrusion detection system(IDS) deep learning CNN-RNN hybrid anomaly detection UNSW-NB15 network security real-time detection IntrusionNet temporal modeling cybersecurity
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Graph Neural Networks with Multi-Head Attention and SHAP-Based Explainability for Robust, Interpretable, and High-Throughput Intrusion Detection in 5G-Enabled Software Defined Networks 认领 引用
4
作者 Sarmad Dheyaa Azeez Muhammad Ilyas Saadaldeen Rashid Ahmed 《Computers, Materials & Continua》 SCIE EI 2026年第6期1637-1665,共29页
The rapid evolution of 5G-enabled Software Defined Networks(SDNs)has transformed modern communication systems by enabling ultra-low latency,massive connectivity,and high throughput.However,the increased complexity of ... The rapid evolution of 5G-enabled Software Defined Networks(SDNs)has transformed modern communication systems by enabling ultra-low latency,massive connectivity,and high throughput.However,the increased complexity of traffic flows and the rise of sophisticated cyber-attacks such as Distributed Denial of Service(DDoS),Botnets,Fake Base Stations,and Zero-Day exploits have made intrusion detection a critical challenge.Traditional Intrusion Detection System(IDS)approaches often suffer from poor gen-eralization,high false positives,and lack of interpretability,making them unsuitable for dynamic 5G environments.This paper presents a novel Graph Neural Network(GNN)with Multi-Head Attention(MHA)and SHAP-based explainability for robust,interpretable,and high-throughput intrusion detection in 5G-SDN.The model is evaluated on the NGIDS-DS and 5G-NIDD datasets,along with a real-time 5G testbed,and achieves a detection accuracy of 98.67%and a detection rate of 99.20%,outperforming baseline IDS models(92.15%accuracy and 89.42%detection rate).Latency is reduced to 24.6 ms compared to 47.3 ms in existing methods,while throughput improves from 7420 flows/sec to 11,384 flows/sec,demonstrating scalability under 5G traffic loads.Furthermore,the integration of SHAP ensures an Interpretability Confidence Score(ICS)of 0.92,providing transparency in decision-making for security-critical applications.The proposed framework significantly enhances detection robustness,reduces overhead,and maintains compliance with 5G Ultra-Reliable Low-Latency Communication(URLLC)performance thresholds,making it a strong candidate for real-world 5G deployments. 展开更多
关键词 5G-SDN explainable AI graph neural networks(GNNs) intrusion detection system(IDS) multi-head attention(MHA) NGIDS-DS SHAP network security
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CycleGAN-RRW: Blind Reversible Image Watermarking via Cycle-Consistent Adversarial Feature Encoding for Secure Image Ownership Authentication 认领 引用
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作者 Mohammed Shamar Yadkar Sefer Kurnaz Saadaldeen Rashid Ahmed 《Computers, Materials & Continua》 SCIE EI 2026年第6期2297-2325,共29页
This advanced research describes CycleGAN-RRW,a new reversible watermarking system for secure image ownership authentication.It uses Cycle-Consistent Generative Adversarial Networks with adaptive feature encoding.In a... This advanced research describes CycleGAN-RRW,a new reversible watermarking system for secure image ownership authentication.It uses Cycle-Consistent Generative Adversarial Networks with adaptive feature encoding.In areas such as law,forensics,and telemedicine,digital images usually contain private info that may be changed or used without authorization.Existing watermarking methods may decrease image quality,may not be reversible,or need outside keys.To address these problems,our model embeds metadata into intermediate feature maps with Adaptive Instance Normalization(AdaIN),based on adversarial and perceptual loss.The dual-generator design permits two-way translation between original and watermarked images,with pixel-level reversibility and semantic integrity.Key aims include blind watermark verification,eliminating side-channel dependency,and resisting distortions such as compression and noise.We tested our approach on the DIV2K and USC-SIPI Miscellaneous datasets,which showed acceptable watermark fidelity and reconstruction accuracy.The model achieved a Peak Signal-to-Noise Ratio(PSNR)of over 42 dB,a Structural Similarity Index(SSIM)above 0.98,and a Bit Error Rate(BER)below 1.5%when subjected to typical attacks like JPEG compression(Q≥60)and Gaussian noise(σ=5).The system permits watermark recovery and tamper detection without outside keys,with an ownership verification accuracy of 98.63%.The CycleGAN-RRW method is a self-contained,blind,and legally defensible watermarking solution with real-time inference and may be applied to other fields like forensic imaging and tele-health. 展开更多
关键词 Reversible watermarking image ownership verification deep learning CycleGAN-RRW image processing
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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks 认领 引用 被引量：1
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作者 Haydar Abdulameer Marhoon Rafid Sagban +1 位作者 Atheer Y.Oudah Saadaldeen Rashid Ahmed 《Computers, Materials & Continua》 SCIE EI 2025年第3期4181-4218,共38页
In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations... In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning technique.Vital applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity.The proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic patterns.This approach not only improves the diversit.There are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning models.These are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble model.We implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline models.To train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor distributions.We used Monte Carlo simulations to extract these traits.Based on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models. 展开更多
关键词 Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model
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Deep Learning-Based Decision Support System for Predicting Pregnancy Risk Levels through Cardiotocograph(CTG)Imaging Analysis 认领 引用
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作者 Ali Hasan Dakheel Mohammed Raheem Mohammed +1 位作者 Zainab Ali Abd Alhuseen Wassan Adnan Hashim 《Intelligent Automation & Soft Computing》 2025年第1期195-220,共26页
The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short... The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded. 展开更多
关键词 Pregnancy risk prediction cardiotocography data analysis deep learning approach LSTM network maternal-fetal healthcare predictive modeling
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Enhancing Network Security:Leveraging Machine Learning for Integrated Protection and Intrusion Detection 认领 引用
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作者 NadaMohammedMurad Adnan Yousif Dawod +2 位作者 Saadaldeen Rashid Ahmed Ravi Sekhar Pritesh Shah 《Intelligent Automation & Soft Computing》 2025年第1期1-27,共27页
This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome ... This study introduces an innovative hybrid approach that integrates deep learning with blockchain technology to improve cybersecurity,focusing on network intrusion detection systems(NIDS).The main goal is to overcome the shortcomings of conventional intrusion detection techniques by developing amore flexible and robust security architecture.We use seven unique machine learning models to improve detection skills,emphasizing data quality,traceability,and transparency,facilitated by a blockchain layer that safeguards against datamodification and ensures auditability.Our technique employs the Synthetic Minority Oversampling Technique(SMOTE)to equilibrate the dataset,therefore mitigating prevalent class imbalance difficulties in intrusion detection.The model selection procedure determined that Random Forest was the most successful model,with a notable detection accuracy of 97%.This substantially surpasses conventional methods and enhances the system’s capacity to identify both established and novel threats with exceptional accuracy.To optimize feature selection and maximize performance,we use Extreme Gradient Boosting(XGBoost),which improves the significance of chosen features while reducing the danger of overfitting.Our study indicates that the integrated use of machine learning for pattern identification,multi-factor authentication(MFA)for access security,and blockchain for data validation constitutes a thorough and sustainable cybersecurity solution.This architecture not only increases security but also lowers the need for regular human monitoring,significantly cutting energy consumption connected with cybersecurity infrastructure.The research finds that this integrated strategy provides a realistic road for increasing network security,addressing real-world cyber threats,and promoting eco-friendly practices in IT security. 展开更多
关键词 Network security machine learning intrusion detection extreme gradient boosting(XGBoost) syntheticminority oversampling technique(SMOTE) IT security
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Enhanced Diagnostic Precision:Deep Learning for Tumors Lesion Classification in Dermatology 认领 引用
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作者 Rafid Sagban Haydar Abdulameer Marhoon Saadaldeen Rashid Ahmed 《Intelligent Automation & Soft Computing》 2024年第6期1035-1051,共17页
Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized... Skin cancer is a highly frequent kind of cancer.Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities.Melanoma,basal,and squamous cell carcinomas are well-recognized cutaneous malignancies.Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma.The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma.The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions.The present study investigates the feasibility of automated methods for detecting pigmented skin lesions with potential malignancy.To diagnose skin lesions,medical professionals employ a two-step approach.Before detecting malignant types with other deep learning(DL)models,a preliminary step involves using a DL model to identify the skin lesions as either pigmented or non-pigmented.The performance assessments accurately assessed four distinct DL models:Long short-term memory(LSTM),Visual Geometry Group(VGG19),Residual Blocks(ResNet50),and AlexNet.The LSTM model exhibited higher classification accuracy compared to the other models used.The accuracy of LSTM for pigmented and non-pigmented,pigmented tumours and benign classes,and melanomas and pigmented nevus classes was 0.9491,0.9531,and 0.949,respectively.Automated computerized skin cancer detection promises to enhance diagnostic efficiency and precision significantly. 展开更多
关键词 Pigmented lesions deep learning models skin cancer automated diagnosis basal cell carcinoma
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