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Vehicular Named Data Networking (VNDN) inherits the broadcast-oriented forwarding of NDN, which exposes safety messages to position-falsification attacks. Existing detectors rely either on static fuzzy thresholds, which drift as traffic patterns change, or on opaque deep models, which are accurate but uninterpretable to safety auditors. We propose a two-stage detector that combines an Adaptive Fuzzy Membership Tuning (AFMT) pre-filter with an attention-augmented bidirectional LSTM. AFMT is a Mamdani fuzzy classifier whose triangular membership-function parameters are updated online by gradient descent on a prediction-error feedback signal from the downstream BiLSTM, replacing offline-fixed thresholds. The BiLSTM consumes the fuzzy suspicion score as an extra feature and produces interpretable per-time-step attention weights aligned with attack onsets. On a simulator-synthesized VNDN benchmark following the five canonical VeReMi attack types, the detector attains F1-scores between 0.955 and 0.979 (macro-average 0.964), ties the strongest baselines on the hardest Random-Offset attack while achieving the highest ROC-AUC of all models (0.984), and runs in 0.44 ms per sample on a CPU. On a live OMNeT++/Veins/SUMO testbed running the five attacks on the LuST scenario, the detector attains an F1 value of 0.986. A leave-one-feature-out study shows that detection does not hinge on the Kalman plausibility feature, and on the real public VeReMi v1.0 dataset the architecture transfers to four of the five attack types at an F1 near 1.0, while the Constant Offset stays invisible to kinematics-only features, and this quantifies the value of the named-data-plane features. Every number reported here is measured from the running detector.

Taxi service quality is traditionally assessed using objective metrics like waiting time, cost, and comfort. However, contemporary research confirms that subjective psychological factors significantly influence service perception. This study develops an interpretable Mamdani fuzzy inference system (TEC model) that maps three passenger psycho-emotional factors—Trust, Empathy, and Control—into an interaction level (0–100) and a corresponding 1–5 star rating for taxi trips. The model is parameterized using a questionnaire survey in Ukraine (n = 118) and Poland (n = 101) and examined through a pilot case study of 10 real trips in Kharkiv and Gdansk. In the pilot, the model-based star ratings were largely consistent with app ratings; the relative error was 6.67% and is reported as a descriptive indicator only. The framework is intended for offline diagnostic assessment and scenario-based improvement planning rather than real-time prediction. Further validation on larger and more diverse samples is required to quantify generalizability. The novelty of this research lies in translating a passenger’s subjective psycho-emotional perceptions into quantitative indicators, which increases the reliability of taxi service quality assessment. The results are significant for taxi services and the logistics of their operations, providing a deeper understanding of the psychological factors that influence ratings. Furthermore, this approach can be used by digital taxi platforms for more effective service management. The work demonstrates the potential of fuzzy logic to improve predictive accuracy, develop personalized strategies, and create a foundation for intelligent decision support systems to enhance taxi services in European cities.

The unified power quality conditioner (UPQC) makes use of a fuzzy logic sliding mode controller (FLSMC) in order to enhance the reliability and quality of the distribution power system. This is accomplished by addressing dynamic performance and power quality issues such as current disturbances, voltage sag, swell, and total harmonic distortion (THD) under nonlinear loads. When compared to traditional controllers, FLSMC-based UPQCs perform better in terms of dynamic performance and power quality. In order to reliably extract reference current and voltage signals for UPQC, the FSMPWM architecture makes use of sliding surface implementation. Similarly, control principles are derived for shunt converters and series converters. The Mamdani fuzzy rule basis for switching pulse generation was meant to be implemented at the sliding surface. Chattering is eliminated, and a fixed switching pulse is produced for shunt and series converters through the utilization of the method that has been provided. The results indicate that when a FLSMC controller is utilized, the total harmonic distortion (THD) values of the source voltage are 0.38%, and the THD values of the source current are 2.01%. Additional evidence demonstrates that the compensation utilized by this controller is successful for all parameters. As a result, the FLSMC controller is the most effective of the four options that have been offered. The FLSMC is superior to the FLC, ANFIS, and FOFLC in terms of its ability to increase dynamic performance and power quality. MATLAB/Simulink is used to actually implement the controller-based system architecture for the FOFLC and FLSMC applications.MATLAB/Simulink, Power Quality, and Fractional Order FLC, as well as Fuzzy Sliding Mode Controller, are some of the keywords that are associated with this topic.

In this paper, the energy management strategy between the internal combustion engine and the electric motor in parallel hybrid vehicles is investigated using an improved and innovative online approach. The proposed method is based on fuzzy systems which can provide online strategy, specifically Takagi-Sugeno fuzzy models, which offer several advantages due to their quasi-linear nature. These advantages include lower computational complexity compared to Mamdani fuzzy systems, simplicity in the design phase, lower implementation costs, and high accuracy in simulation results. The key innovation in this study is the reduction of the number of inputs to the fuzzy system. Instead of using both speed and torque as inputs for energy management, only power is utilized as the primary input. This approach ensures that the internal combustion engine operates at maximum efficiency while satisfying constraints such as driving cycle requirements. The results of the simulation provide evidence of improved vehicle performance and reduced fuel consumption, which are presented in detail in the following sections.

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep-fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO2 and 95.7% for PM2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments.

Poultry products are important global protein sources but are vulnerable to contamination by toxic metals such as copper, cadmium, lead, and arsenic. Excessive intake of these metals poses health risks, necessitating reliable yet accessible detection methods. This study developed a fuzzy logic framework using the Mamdani inference system and triangular membership functions in MATLAB R2021b to estimate heavy metal concentrations in poultry products including eggs, meat, and liver. Three fuzzy logic models were constructed, and multiple rule sets of 25, 50, and 81 rules were tested. Results showed that the 50-rule model achieved accurate classifications while minimizing complexity, correctly identifying safe and unsafe products in line with FAO/WHO permissible limits. Validation using published laboratory data confirmed that the model classified samples exceeding lead values above 0.1 ppm and cadmium values above 0.05 ppm as unsafe, whereas those within safe ranges such as egg samples containing 0.243 ppm copper, 0.033 ppm lead, 0.002 ppm cadmium, and 0.003 ppm arsenic were correctly identified. The key novelty of this study lies in the integration of a fuzzy logic-based heavy metal safety prediction model with an accessible graphical user interface (GUI), enabling non-expert users to perform rapid and interpretable food safety assessments without requiring programming or toxicological expertise. The proposed system provides a rapid, cost-effective, and user-friendly alternative to laboratory testing, supporting food safety monitoring and public health protection.

Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based motion capture systems. The latter, while highly accurate, require specialised laboratory infrastructure, are costly, and are unsuitable for home-based use. There is growing interest in markerless, camera-based alternatives that leverage artificial intelligence (AI) for joint angle estimation. This study presents a preliminary proof-of-concept evaluation of a hybrid AI vision system, combining the MediaPipe Pose framework (version 0.8.9.1, Full model variant) with a Mamdani-type fuzzy inference system, for joint angle estimation and exercise repetition counting using a single consumer-grade camera. Fourteen healthy adult rehabilitation staff members performed three exercises (elbow flexion, knee extension, and hip external rotation) simultaneously recorded by the proposed AI system and by a reference optical motion capture system (C-Motion Visual3D™, 14 calibrated infrared cameras, 53 retro-reflective markers). Reliability was assessed using two-way mixed-effects intraclass correlation coefficients (ICC[Formula: see text]) with absolute agreement. Individual-measure ICC values ranged from poor to good across joints and conditions (range: 0.005-0.68). The highest reliability was observed for the right hip external rotation ROM (individual ICC [Formula: see text]; average ICC [Formula: see text], 95% CI: 0.63-0.89). Several measures showed poor reliability, including the right elbow flexion ROM (individual ICC [Formula: see text]) and the minimum angle of the left hip external rotation (individual ICC [Formula: see text]). Confidence intervals were wide throughout. This proof of concept demonstrates that a single-camera AI system can capture general trends in joint angles during simple rehabilitation exercises in healthy adults under controlled conditions. However, reliability is inconsistent and frequently poor, and the system has not been evaluated in patient populations with pathological movement patterns. These findings do not support clinical deployment at this stage. Substantial further development and validation in patient cohorts are required before clinical or home-based use can be considered.

There is increasing interest in the use of artificial intelligence (AI) to assist with respiratory diagnosis and risk prediction. Fuzzy logic is a form of AI that has the advantage of being transparent and interpretable, compared to alternatives such as deep neural networks. We systematically reviewed applications of fuzzy logic for outcome prediction in respiratory medicine. We searched PubMed and IEEE Xplore from inception to November 2024 for studies which applied fuzzy logic to respiratory outcome prediction and diagnosis. Three reviewers independently screened titles and abstracts, then all five reviewers assessed full texts for eligibility. Risk of bias was assessed using PROBAST by three reviewers. We performed a narrative synthesis following the SWiM guidelines due to heterogeneity. From 982 records, 29 studies (1998-2024) met the inclusion criteria. Studies addressed asthma (n = 5), obstructive sleep apnoea (n = 8), lung cancer (n = 4) and a variety of other conditions. Mamdani-type systems were the most frequently used (69%). Performance varied dramatically, with sensitivity/specificity ranging from 69 to 100% and 19-100%, respectively. The studies which displayed the highest accuracy (>95%) incorporated well-defined clinical variables, particularly for asthma and tuberculosis. However, 69% of studies displayed high risk of bias, frequently due to inadequate validation. Fuzzy logic systems show potential as a transparent alternative to neural network-based machine learning for outcome prediction and diagnosis in respiratory medicine. However, clinical implementation is limited by frequent methodological limitations. Future research requires prospective validation studies and standardised reporting before fuzzy logic can enhance respiratory medicine.

Elite athletes operate in high-stress, feedback-rich environments where mental health risk is shaped by motivation and training climate. This study models the joint effects of intrinsic motivation, psychological safety, and mental well-being on anxiety, depression, athlete-specific strain, and burnout using an interpretable fuzzy-logic framework alongside conventional regression. A sample of 247 athletes completed validated measures (SMS-6 Intrinsic Motivation, Psychological Safety, SWEMWBS, GAD-7, PHQ-9, APSQ, and BMS). After pre-processing and standard analyses, we constructed a Mamdani-type Fuzzy Inference System. To accurately represent the non-linear transitions between psychological states, the model specifies trapezoidal membership functions for boundary linguistic variables and triangular membership functions for intermediate categories. The system utilises a transparent rule base (primary risk from low IM; protection from PS/MWB; synergistic protection when both are high), min-max aggregation, and centroid defuzzification. Rule weights and breakpoints were calibrated against observed score distributions to minimise mean absolute error (MAE). Multiple regressions indicated that PS and MWB independently predicted lower risk across all outcomes; however, IM emerged as a significant positive predictor of depression and anxiety (p < 0.05). Further diagnostics confirmed this as a suppression effect, with all VIF values&#x2009;<&#x2009;1.5. Visual and quantitative analyses confirmed three regularities: (i) a primary risk gradient when IM, PS, and MWB are low; (ii) buffering as PS or MWB increase; and (iii) a low-risk "basin" when PS and MWB are jointly high. Comparative metrics (MAE/RMSE) showed that the FIS model offers superior predictive accuracy and interpretability compared to standard linear approaches.

This research focuses on the challenge of assessing the health status and fault-type diagnosis in rolling element bearings (REBs). A key obstacle in this field pertains to feature extraction to exhibit generalizability across a wide array of machines and operating conditions. To address this challenge, new dimensionless features extracted from wavelet transform (WT) and fast Fourier transform (FFT) are proposed in this study. Mamdani fuzzy inference systems are developed based on the authors' extensive years of expertise in condition monitoring. One fuzzy system is dedicated to REB health status detection, while three other fuzzy systems are designed for fault-type diagnosis. The generalizability of this approach is validated through testing on four distinct REB vibration datasets. These datasets include two laboratory datasets, XJTU and PRONOSTIA, a dataset from the authors' university, and an industrial data collected from three industrial sites. The health status detector demonstrates strong performance across all datasets, achieving an average accuracy of 96.8% when an acceptable discrepancy of 0.2 is considered, and 90.4% with a stricter discrepancy of 0.1. Meanwhile, the fault-type diagnosis system is assessed on datasets with available fault-type labels, achieving an average accuracy of 98.7%. Results underscore the robust generalization of the proposed dimensionless features which are insensitive to operating conditions.

Cognitive Radio Networks (CRNs) form the basis of an interesting approach to ease the ongoing shortage of spectral resources. Through allowing Secondary Users (SUs) to opportunistically use underutilized frequency bands, including the integrity of Primary Users (PUs), CRNs represent a complex solution to the old spectrum quandary. This research paper outlines an independent, vibrant channel assignment model based on a 27-rule Mamdani fuzzy logic decision model. This framework cleverly coordinates channel selection after wise disposition of simulated network characteristics including Signal-to-Interference-plus-Noise Ratio (SINR), transmission power and the necessary channel capacity. In spectrum sensing, energy-detection approach is implemented to detect idle channels and assign a high-priority SUs to a stable interweave channel, and a low-priority SUs to a hybrid interweave-underlay approach reducing transmission power when the PU is active. The proposed system demonstrates adaptive responsiveness to varying conditions in the network by having a distributed decision making at the individual SU units. The empirical findings, in both simulation cases of various SU arrival conditions using MATLAB, indicate that the fuzzy-based allocation system can make significant improvements in throughput, reduce the service delay and drop rate, and increase the spectrum utilization compared to the traditional CRN paradigms. Cognitive intelligence combined with fuzzy decision-making, therefore, is envisaged to autonomies, scale, and provide effective spectrum management in heterogeneous Internet of Things (IoT) communications.

Indoor Environmental Quality (IEQ) critically impacts human health, comfort, and productivity, yet existing air purification systems often rely on single-sensor data, manual monitoring, or cloud-based processing, resulting in limited intelligence, increased latency, and reduced suitability for resource-constrained environments such as Uganda. To address these challenges, this paper presents the design, mathematical modelling, and simulation of a novel multi-parameter indoor air purification control system. The proposed framework integrates six environmental sensors (dust, smoke, gas, volatile organic compounds (VOC), temperature, and humidity) with a hybrid Fuzzy Logic-Proportional-Integral-Derivative (PID) controller whose parameters are optimally tuned using a Genetic Algorithm (GA). A first-principles mathematical model of the DC motor-driven purifier actuator is developed to describe the relationship between the input voltage and volumetric airflow. To enhance the physical realism of the simulations, the system model incorporates practical operating factors, including fan inertia, room-air mixing, pollutant transport, filter dynamics, and environmental sensor noise. The complete system is implemented in the MATLAB/Simulink environment using a Mamdani-type fuzzy inference system with 49 rules and a GA-based multi-objective fitness function to minimise overshoot, settling time, and steady-state error. Simulation results demonstrate that the proposed GA-tuned Fuzzy-PID controller achieves a stable and well-damped response with a rise time of 53.93 s, a settling time of 119.99 s, and a maximum overshoot of 2.14%, reflecting the expected dynamics of practical indoor air purification systems. The proposed framework provides a scalable, low-latency, and network-independent intelligent control solution that establishes a realistic foundation for future prototype development and experimental validation in low-resource environments.

The present study investigates the effectiveness of carbon nanotube (CNT)-reinforced soybean oil as a nano-lubricant under minimum quantity lubrication (MQL) for machining Monel 400 alloy. CNTs were dispersed in soybean oil at varying concentrations (0-0.8 vol%), and their thermo-physical properties were evaluated through wettability (contact angle) and rheological (viscosity) analyses. The results identified 0.5 vol% CNT as the optimal concentration, exhibiting improved wettability and enhanced viscosity, which are favorable for stable lubricating film formation. Machining experiments were conducted under different lubrication environments, namely dry, compressed air, pure soybean oil, and CNT-enriched soybean oil. The results demonstrated that the CNT-based nano-lubricant significantly improved machining performance. Relative to dry machining conditions, substantial improvements were observed across all performance metrics. Surface roughness decreased by 73.7%, while cutting force and cutting temperature were reduced by 56.1% and 59.0%, respectively. In addition, tool wear exhibited a significant decline of 82.1%, indicating enhanced machining efficiency and tool longevity. These improvements are attributed to the superior tribological characteristics of CNTs, including tribo-film formation, enhanced thermal conductivity, reduced interfacial shear resistance, and improved load-bearing capacity. Furthermore, a Taguchi L16 experimental design integrated with a Fuzzy Mamdani inference system was employed to determine the optimal machining conditions. The best-performing combination of machining conditions-cutting speed of 70&#xa0;m/min, feed rate of 0.05&#xa0;mm/tooth, and depth of cut of 0.15&#xa0;mm under CNT-MQL lubrication-resulted in the maximum multi-performance characteristics index (MPCI), achieving a value of 0.78. Overall, CNT-reinforced soybean oil demonstrates strong potential as an effective nano-lubricant for improving machining performance of nickel-based alloys under MQL conditions. The findings of this research demonstrate that nano-lubrication incorporating carbon nanotubes (CNTs) serves as a promising strategy to significantly improve machining performance, prolong tool durability, and achieve superior surface finish when processing hard-to-machine materials.

Ensuring functional stability of critical infrastructure facilities (CIFs) under conditions of uncertainty and dynamic threats remains a critical challenge. Existing approaches insufficiently integrate technical, cybersecurity, and human-related factors. This study proposes an information-cognitive approach based on a hybrid model combining Bayesian Trust Networks and fuzzy logic. The model incorporates expert knowledge and evaluates the mutual influence of information security, cybersecurity, human factors, and vulnerability indicators. The Mamdani algorithm is used for probabilistic estimation under uncertainty. Numerical experiments conducted in the GeNIe environment demonstrate that the proposed model effectively supports decision-making. Scenario analysis shows that adjusting key cybersecurity and vulnerability factors increases the probability of achieving sufficient functional stability above the critical threshold. The proposed hybrid framework improves interpretability and adaptability of functional stability assessment. It enables flexible reasoning under uncertainty and supports real-time decision-making for critical infrastructure management. The approach can be applied across different categories of CIFs and extended with additional data-driven components.

To develop and validate a fuzzy logic model based on expert consensus to elucidate distress dynamics in cancer patients, examining the non-linear interactions between psychological, social, and medical factors. A two-round Delphi process with 23 psychosocial oncology experts was conducted to generate an interaction matrix of 18 distress-related variables. Using the skfuzzy Python library, a Mamdani fuzzy inference system was constructed, focusing on Negative Psychological Factors, Symptoms, and Positive Psychological Factors as primary drivers. Model validation included network analysis, time-series simulations, and sensitivity analyses, compared against a traditional crisp system dynamics model. The fuzzy model confirmed a self-reinforcing "vicious cycle" of distress driven by Negative Psychological Factors (weight&#x2009;=&#x2009;2.00) and Symptoms (weight&#x2009;=&#x2009;1.50). Simulations demonstrated that positive psychological interventions could reduce overall distress levels by up to 25%. Network analysis identified distress as a central system hub, while the fuzzy model produced smoother, more clinically realistic trajectories than the crisp model. This study provides a robust mathematical explanation for the success of the validated DIC-2 clinical tool. The results underscore the necessity of early, multidisciplinary interventions to disrupt distress cycles, supporting the clinical shift toward treating distress as the "sixth vital sign".

In critically injured trauma patients, tools that stratify injury severity and estimate mortality are essential. Fuzzy logic (FL) enables the creation of accurate, interpretable models but requires decision rules, which can be generated using machine learning (ML) techniques like classification trees (CT). Our objective was to develop a hybrid model combining fuzzy logic and classification trees to estimate ICU mortality risk using only prehospital variables. We conducted a retrospective study using data from the Spanish Trauma ICU registry (RETRAUCI) from 2015 to 2022. Patients were randomly divided into derivation (DS) and validation sets (VS) (70:30). Candidate variables were those available in the prehospital phase. A hybrid model (HFL) was developed using a Fuzzy Inference System built with the 'FuzzyR' library in RStudio (v 2024.04.2) and the Mamdani method. CHAID (Chi-squared Automatic Interaction Detection) classification trees were used to derive the rules. The HFL's discrimination and calibration were compared with other scores: Revised Trauma Score (RTS); Glasgow Coma Scale, Age, and Arterial Pressure (GAP); Mechanism, Glasgow Coma Scale, Age, and Arterial Pressure (MGAP); Reverse shock index multiplied by Glasgow Coma Scale score (rSIG); and Trauma Rating Index in Age, Glasgow Coma Scale, Respiratory Rate, and Systolic Blood Pressure (TRIAGE). The study included 11,030 records, with 7,728 in the DS and 3,302 in the VS, and an overall mortality of 11.1%. Five variables were selected, ordered by importance: GCS, age, systolic blood pressure, respiratory rate, and heart rate. A total of 32 classification rules were generated. The HFL model achieved the highest accuracy in DS and VS, with AUROC of 0.87 (0.86-0.88) and 0.86 (0.83-0.88), and acceptable calibration with intercepts of -0.11 (-0.18 to -0.04) and -&#x2009;0.19 (-0.32 to -0.06) and slopes of 0.99 (0.94-1.05) and 0.96 (0.83-0.88). Our hybrid model achieves accuracy comparable to commonly used models and provides clear clinical interpretation, with GCS and age as key variables.

Safety-critical Industrial Internet of Things (IIoT) sensor networks deployed in disaster scenarios require intelligent routing mechanisms that prioritize mission-critical packets without relying on centralized coordination. Federated learning on resource-constrained edge nodes presents three primary challenges: the absence of an interpretable supervisory signal, the inability to act conservatively based on per-inference confidence, and vulnerability to partial node availability. The proposed FedCARE framework addresses these issues by employing a Mamdani Fuzzy Inference System to generate traceable criticality labels from multi-modal sensor telemetry, a dropout-aware aggregation protocol that normalizes over only reachable nodes, and a confidence-gated resolver that defers to symbolic fuzzy classification when model confidence is insufficient, otherwise applying an auditable maximization rule to prevent under-prioritization of safety-critical data. Evaluation on 50-, 100-, and 200-node Watts-Strogatz topologies under fault rates up to 50%, using the Edge-IIoTset and WUSTL-IIoT-2021 benchmarks, demonstrates 99.00% critical recall and up to 1.8&#xd7; higher overall-packet delivery compared to RPL-RP under severe fault conditions. Routing improvements are primarily attributed to fuzzy criticality labeling and multi-path replication. These findings indicate that fuzzy-supervised federated inference offers a practical and interpretable solution for safety-critical IIoT routing, with an observed energy overhead of 7.8% per delivered packet.

Wireless Sensor Networks (WSNs) are widely used in applications such as environmental monitoring, smart agriculture, healthcare, industrial automation, and military surveillance. However, their performance and lifetime are often limited by energy constraints, inefficient clustering, insecure routing, and vulnerability to network attacks. Existing approaches frequently suffer from high energy consumption, increased routing overhead, poor scalability, and limited accuracy in detecting intrusions. To address these issues, the proposed approach employs a Mamdani-type Fuzzy Inference System (FIS) for adaptive cluster formation, ensuring balanced and stable clustering. Cluster Head (CH) selection is carried out using Dynamic Adaptive Fig Tree-Wasp Symbiotic Coevolutionary Optimization (DA-FTWSCO) to improve energy efficiency and extend network lifetime. For secure communication, the Adaptive Trust-Synchronized Packet Control Protocol (ATSPCP) is used to compute trust values, followed by optimal path selection using the Improved Grizzly Bear Fat Increase Optimizer (IGBFIO). Additionally, an Enhanced Multi-scale Dilated MobileNet with Attention Mechanism (EMSD-MobileNet-AM) is utilized for effective intrusion detection, enabling accurate identification of denial-of-service (DoS) and zero-day attacks. Simulation outcomes demonstrate that the proposed method achieves low energy consumption (1.02&#xa0;J), high throughput (0.93 Mbps), low end-to-end delay (4.0 ms), and a high attack detection rate (98%). The results demonstrate that the proposed framework outperforms several existing methods in terms of efficiency, security, and scalability.