IEEE Transactions on Machine Learning in Communications and Networking最新文献

{"title":"Machine Learning Aided Resilient Spectrum Surveillance for Cognitive Tactical Wireless Networks: Design and Proof-of-Concept","authors":"Eli Garlick;Nourhan Hesham;MD. Zoheb Hassan;Imtiaz Ahmed;Anas Chaaban;MD. Jahangir Hossain","doi":"10.1109/TMLCN.2025.3585849","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3585849","url":null,"abstract":"Cognitive tactical wireless networks (TWNs) require spectrum awareness to avoid interference and jamming in the communication channel and assure quality-of-service in data transmission. Conventional supervised machine learning (ML) algorithm’s capability to provide spectrum awareness is confronted by the requirement of labeled interference signals. Due to the vast nature of interference signals in the frequency bands used by cognitive TWNs, it is non-trivial to acquire manually labeled data sets of all interference signals. Detecting the presence of an unknown and remote interference source in a frequency band from the transmitter end is also challenging, especially when the received interference power remains at or below the noise floor. To address these issues, this paper proposes an automated interference detection framework, entitled <inline-formula> <tex-math>$textsf {MARSS}$ </tex-math></inline-formula> (Machine Learning Aided Resilient Spectrum Surveillance). <inline-formula> <tex-math>$textsf {MARSS}$ </tex-math></inline-formula> is a fully unsupervised method, which first extracts the low-dimensional representative features from spectrograms by suppressing noise and background information and employing convolutional neural network (CNN) with novel loss function, and subsequently, distinguishes signals with and without interference by applying an isolation forest model on the extracted features. The uniqueness of <inline-formula> <tex-math>$textsf {MARSS}$ </tex-math></inline-formula> is its ability to detect hidden and unknown interference signals in multiple frequency bands without using any prior labels, thanks to its superior feature extraction capability. The capability of <inline-formula> <tex-math>$textsf {MARSS}$ </tex-math></inline-formula> is further extended to infer the level of interference by designing a multi-level interference classification framework. Using extensive simulations in GNURadio, the superiority of <inline-formula> <tex-math>$textsf {MARSS}$ </tex-math></inline-formula> in detecting interference over existing ML methods is demonstrated. The effectiveness <inline-formula> <tex-math>$textsf {MARSS}$ </tex-math></inline-formula> is also validated by extensive over-the-air (OTA) experiments using software-defined radios.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"814-834"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11068948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Blockchain-Enabled Federated Learning Scheme for IoT Anomaly Detection","authors":"Van-Doan Nguyen;Abebe Diro;Naveen Chilamkurti;Will Heyne;Khoa Tran Phan","doi":"10.1109/TMLCN.2025.3585842","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3585842","url":null,"abstract":"In this research, we proposed a novel anomaly detection system (ADS) that integrates federated learning (FL) with blockchain for resource-constrained IoT. The proposed system allows IoT devices to exchange machine learning (ML) models through a permissioned blockchain, enabling trustworthy collaborative learning through model sharing. To avoid single-point failure, any device can be a centre of the FL process. To deal with the issue of resource constraints in IoT devices and the model poisoning problem in FL, we introduced a novel method to use commitment coefficients and ML model discrepancies when selecting particular devices to join the FL process. We also proposed an efficient heuristic method to aggregate a federated model from a list of ML models trained locally on the selected devices, which helps to improve the federated model’s anomaly detection ability. The experiment results with the popular N-BaIoT dataset for IoT botnet attack detection show that the proposed system is more effective in detecting anomalies and resisting poisoning attacks than the two baselines (FedProx and FedAvg).","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"798-813"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11070312","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-Driven AI Model Generalization for Workload Forecasting in the Compute Continuum","authors":"Berend J. D. Gort;Godfrey M. Kibalya;Angelos Antonopoulos","doi":"10.1109/TMLCN.2025.3584009","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3584009","url":null,"abstract":"Effective resource management in edge-cloud networks demands precise forecasting of diverse workload resource usage. Due to the fluctuating nature of user demands, prediction models must have strong generalization abilities, ensuring high performance amidst sudden traffic changes or unfamiliar patterns. Existing approaches often struggle with handling long-term dependencies and the diversity of temporal patterns. This paper introduces OmniFORE (Framework for Optimization of Resource forecasts in Edge-cloud networks), which integrates attention-based time-series models with temporal clustering to enhance generalization and predict diverse workloads efficiently in volatile settings. By training on carefully selected subsets from extensive datasets, OmniFORE captures both short-term stability and long-term shifts in resource usage patterns. Experiments show that OmniFORE outperforms state-of-the-art methods in prediction accuracy, inference speed, and generalization to unseen data, particularly in scenarios with dynamic workload changes and varying trace variance. These improvements enable more efficient resource management in the compute continuum.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"779-797"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11053768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144597748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating Energy-Efficient Federated Learning in Cell-Free Networks With Adaptive Quantization","authors":"Afsaneh Mahmoudi;Ming Xiao;Emil Björnson","doi":"10.1109/TMLCN.2025.3583659","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3583659","url":null,"abstract":"Federated Learning (FL) enables clients to share model parameters instead of raw data, reducing communication overhead. Traditional wireless networks, however, suffer from latency issues when supporting FL. Cell-Free Massive MIMO (CFmMIMO) offers a promising alternative, as it can serve multiple clients simultaneously on shared resources, enhancing spectral efficiency and reducing latency in large-scale FL. Still, communication resource constraints at the client side can impede the completion of FL training. To tackle this issue, we propose a low-latency, energy-efficient FL framework with optimized uplink power allocation for efficient uplink communication. Our approach integrates an adaptive quantization strategy that dynamically adjusts bit allocation for local gradient updates, significantly lowering communication cost. We formulate a joint optimization problem involving FL model updates, local iterations, and power allocation. This problem is solved using sequential quadratic programming (SQP) to balance energy consumption and latency. Moreover, for local model training, clients employ the AdaDelta optimizer, which improves convergence compared to standard SGD, Adam, and RMSProp. We also provide a theoretical analysis of FL convergence under AdaDelta. Numerical results demonstrate that, under equal energy and latency budgets, our power allocation strategy improves test accuracy by up to 7% and 19% compared to Dinkelbach and max-sum rate approaches. Furthermore, across all power allocation methods, our quantization scheme outperforms AQUILA and LAQ, increasing test accuracy by up to 36% and 35%, respectively.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"761-778"},"PeriodicalIF":0.0,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11052837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SET: A Shared-Encoder Transformer Scheme for Multi-Sensor, Multi-Class Fault Classification in Industrial IoT","authors":"Kamran Sattar Awaisi;Qiang Ye;Srinivas Sampalli","doi":"10.1109/TMLCN.2025.3579750","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3579750","url":null,"abstract":"The Industrial Internet of Things (IIoT) has revolutionized the industrial sector by integrating sensors to monitor equipment health and optimize production processes. These sensors collect real-time data and are prone to a variety of different faults, such as bias, drift, noise, gain, spike, and constant faults. Such faults can lead to significant operational problems, including false results, incorrect predictions, and misleading maintenance decisions. Therefore, classifying sensor data appropriately is essential for ensuring the reliability and efficiency of IIoT systems. In this paper, we propose the Shared-Encoder Transformer (SET) scheme for multi-sensor, multi-class fault classification in IIoT systems. Leveraging the transformer architecture, the SET uses a shared encoder with positional encoding and multi-head self-attention mechanisms to capture complex temporal patterns in sensor data. Consequently, it can accurately detect the health status of sensor data, and if the sensor data is faulty, it can specifically identify the fault type. Additionally, we introduce a comprehensive fault injection strategy to address the problem of fault data scarcity, enabling the validation of the robust performance of SET even with limited fault samples in both ideal and realistic scenarios. In our research, we conducted extensive experiments using the Commercial Modular Aeropropulsion System Simulation (C-MAPSS) and Skoltech Anomaly Benchmark (SKAB) datasets to study the performance of the SET. Our experimental results indicate that SET consistently outperforms baseline methods, including Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN)-LSTM, and Multilayer Perceptron (MLP), as well as the proposed comparative variant of SET, Multi-Encoder Transformer (MET), in terms of accuracy, precision, recall, and F1-score across different fault intensities. The shared-kmencoder architecture improves fault detection accuracy and ensures parameter efficiency/robustness, making it suitable for deployment in memory-constrained industrial environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"744-760"},"PeriodicalIF":0.0,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11037229","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144367023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal Collaborative Perception for Dynamic Channel Prediction in 6G V2X Networks","authors":"Ghazi Gharsallah;Georges Kaddoum","doi":"10.1109/TMLCN.2025.3578577","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3578577","url":null,"abstract":"The evolution toward sixth-generation (6G) wireless communications introduces unprecedented demands for ultra-reliable low-latency communication (URLLC) in vehicle-to-everything (V2X) networks, where fast-moving vehicles and the use of high-frequency bands make it challenging to acquire the channel state information to maintain high-quality connectivity. Traditional methods for estimating channel coefficients rely on pilot symbols transmitted during each coherence interval; however, the combination of high mobility and high frequencies significantly reduces the coherence times, necessitating substantial bandwidth for pilot transmission. Consequently, these conventional approaches are becoming inadequate, potentially causing inefficient channel estimation and degraded throughput in such dynamic environments. This paper presents a novel multimodal collaborative perception framework for dynamic channel prediction in 6G V2X networks, integrating LiDAR data to enhance the accuracy and robustness of channel predictions. Our approach synergizes information from connected agents and infrastructure, enabling a more comprehensive understanding of the dynamic vehicular environment. A key innovation in our framework is the prediction horizon optimization (PHO) component, which dynamically adjusts the prediction interval based on real-time evaluations of channel conditions, ensuring that predictions remain relevant and accurate. Extensive simulations using the MVX (Multimodal V2X) high-fidelity co-simulation framework demonstrate the effectiveness of our solution. Compared to baseline methods—namely, a classical LS-LMMSE approach and a wireless-based model that solely relies on channel measurements—our framework achieves up to a 30.82% reduction in mean squared error (MSE) and a 32.76% increase in goodput. These gains underscore the efficiency of the PHO component in reducing prediction errors, maintaining low bit error rates, and meeting the stringent requirements of 6G V2X communications. Consequently, our framework establishes a new benchmark for AI-driven channel prediction in next-generation wireless networks, particularly in challenging urban and rural scenarios.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"725-743"},"PeriodicalIF":0.0,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11030661","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144323233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual Self-Attention is What You Need for Model Drift Detection in 6G Networks","authors":"Mazene Ameur;Bouziane Brik;Adlen Ksentini","doi":"10.1109/TMLCN.2025.3576727","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3576727","url":null,"abstract":"The advent of 6G networks heralds a transformative shift in communication technology, with Artificial Intelligence (AI) and Machine Learning (ML) forming the backbone of its architecture and operations. However, the dynamic nature of 6G environments renders these models vulnerable to performance degradation due to model drift. Existing drift detection approaches, despite advancements, often fail to address the diverse and complex types of drift encountered in telecommunications, particularly in time-series data. To bridge this gap, we propose, for the first time, a novel drift detection framework featuring a Dual Self-Attention AutoEncoder (DSA-AE) designed to handle all major manifestations of drift in 6G networks, including data, label, and concept drift. This architectural design leverages the autoencoder’s reconstruction capabilities to monitor both input features and target variables, effectively detecting data and label drift. Additionally, its dual self-attention mechanisms comprising feature and temporal attention blocks capture spatiotemporal fluctuations, addressing concept drift. Extensive evaluations across three diverse telecommunications datasets (two time-series and one non-time-series) demonstrate that our framework achieves substantial advancements over state-of-the-art methods, delivering over a 13.6% improvement in drift detection accuracy and a remarkable 94.7% reduction in detection latency. By balancing higher accuracy with lower latency, this approach offers a robust and efficient solution for model drift detection in the dynamic and complex landscape of 6G networks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"690-709"},"PeriodicalIF":0.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11024186","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Powered System for an Efficient and Effective Cyber Incidents Detection and Response in Cloud Environments","authors":"Mohammed Ashfaaq M. Farzaan;Mohamed Chahine Ghanem;Ayman El-Hajjar;Deepthi N. Ratnayake","doi":"10.1109/TMLCN.2025.3564912","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3564912","url":null,"abstract":"The growing complexity and frequency of cyber threats in cloud environments call for innovative and automated solutions to maintain effective and efficient incident response. This study tackles this urgent issue by introducing a cutting-edge AI-driven cyber incident response system specifically designed for cloud platforms. Unlike conventional methods, our system employs advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques to provide accurate, scalable, and seamless integration with platforms like Google Cloud and Microsoft Azure. Key features include an automated pipeline that integrates Network Traffic Classification, Web Intrusion Detection, and Post-Incident Malware Analysis into a cohesive framework implemented via a Flask application. To validate the effectiveness of the system, we tested it using three prominent datasets: NSL-KDD, UNSW-NB15, and CIC-IDS-2017. The Random Forest model achieved accuracies of 90%, 75%, and 99%, respectively, for the classification of network traffic, while it attained 96% precision for malware analysis. Furthermore, a neural network-based malware analysis model set a new benchmark with an impressive accuracy rate of 99%. By incorporating deep learning models with cloud-based GPUs and TPUs, we demonstrate how to meet high computational demands without compromising efficiency. Furthermore, containerisation ensures that the system is both scalable and portable across a wide range of cloud environments. By reducing incident response times, lowering operational risks, and offering cost-effective deployment, our system equips organizations with a robust tool to proactively safeguard their cloud infrastructure. This innovative integration of AI and containerised architecture not only sets a new benchmark in threat detection but also significantly advances the state-of-the-art in cybersecurity, promising transformative benefits for critical industries. This research makes a significant contribution to the field of AI-powered cybersecurity by showcasing the powerful combination of AI models and cloud infrastructure to fill critical gaps in cyber incident response. Our findings emphasise the superior performance of Random Forest and deep learning models in accurately identifying and classifying cyber threats, setting a new standard for real-world deployment in cloud environments.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"623-643"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979487","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission","authors":"Mehdi Letafati;Seyyed Amirhossein Ameli Kalkhoran;Ecenaz Erdemir;Babak Hossein Khalaj;Hamid Behroozi;Deniz Gündüz","doi":"10.1109/TMLCN.2025.3564907","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3564907","url":null,"abstract":"Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdroppers. Both scenarios of colluding and non-colluding eavesdroppers are considered. Unlike prior works that assume perfectly known and independent identically distributed (i.i.d.) source and channel statistics, the proposed scheme operates under unknown and non-i.i.d. conditions, making it more applicable to real-world scenarios. The goal is to transmit images with minimum distortion, while simultaneously preventing eavesdroppers from inferring certain private attributes of images. Simultaneously generalizing the ideas of privacy funnel and wiretap coding, a multi-objective optimization framework is expressed that characterizes the trade-off between image reconstruction quality and information leakage to eavesdroppers, taking into account the structural similarity index (SSIM) for improving the perceptual quality of image reconstruction. Extensive experiments on the CIFAR-10 and CelebA, along with ablation studies, demonstrate significant performance improvements in terms of SSIM, adversarial accuracy, and the mutual information leakage compared to benchmarks. Experiments show that the proposed scheme restrains the adversarially-trained eavesdroppers from intercepting privatized data for both cases of eavesdropping a common secret, as well as the case in which eavesdroppers are interested in different secrets. Furthermore, useful insights on the privacy-utility trade-off are also provided.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"568-584"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10979442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evolving ML-Based Intrusion Detection: Cyber Threat Intelligence for Dynamic Model Updates","authors":"Ying-Dar Lin;Yi-Hsin Lu;Ren-Hung Hwang;Yuan-Cheng Lai;Didik Sudyana;Wei-Bin Lee","doi":"10.1109/TMLCN.2025.3564587","DOIUrl":"https://doi.org/10.1109/TMLCN.2025.3564587","url":null,"abstract":"Existing Intrusion Detection System (IDS) relies on pre-trained models that struggle to keep pace with the evolving nature of network threats, as they cannot detect new types of network attacks until updated. Cyber Threat Intelligence (CTI) is analyzed by professional teams and shared among organizations for collective defense. However, due to its diverse forms, existing research often only analyzes reports and extracts Indicators of Compromise (IoC) to create an IoC Database for configuring blocklists, a method that attackers can easily circumvent. Our study introduces a unified solution named Dynamic IDS with CTI Integrated (DICI), which focuses on enhancing IDS capabilities by integrating continuously updated CTI. This approach involves two key AI models: the first serves as the IDS Model, detecting network traffic, while the second, the CTI Transfer Model, analyzes and transforms CTI into actionable training data. The CTI Transfer Model continuously converts CTI information into training data for IDS, enabling dynamic model updates that improve and adapt to emerging threats dynamically. Our experimental results show that DICI significantly enhances detection capabilities. Integrating the IDS Model with CTI in DICI improved the F1 score by 9.29% compared to the system without CTI, allowing for more effective detection of complex threats such as port obfuscation and port hopping attacks. Furthermore, within the CTI Transfer Model, involving the ML method led to a 30.92% F1 score improvement over heuristic methods. These results confirm that continuously integrating CTI within DICI substantially boosts its ability to detect and respond to new types of cyber attacks.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"605-622"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10978877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}