Distributed Denial of Service (DDoS) attacks pose significant threats to network security, disrupting critical services by overwhelming targeted systems with malicious traffic. In this study, a machine learning-based approach is proposed to classify DDoS attacks using multiple classification models, including Random Forest (RF), Naïve Bayes (NB), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Support Vector Machine (SVM). The DDoS-SDN dataset was used for training and evaluation, with feature selection via Backward Elimination (BE) and hyperparameter tuning using Grid Search with 5-fold Cross-Validation (CV = 5). Experimental results demonstrate a significant improvement in classification performance after feature selection and parameter optimization, with RF achieving the highest accuracy of 99.99%. In this study, we propose a machine learning-based classification framework enhanced by feature selection and hyperparameter optimization techniques through employing Recursive Feature Elimination (RFE) and Grid Search .Our model based on Random Forest (RF) achieved a remarkable accuracy of 99.99%, outperforming other baseline classifiers, including Naive Bayes (98.85%), K-Nearest Neighbors (97.90%), Linear Discriminant Analysis (97.10%), and Support Vector Machine (95.70%). In addition to accuracy, the RF model also demonstrated superior F1 score, recall, and precision, each reaching 99.99%. These results validate the effectiveness of our optimization strategy in improving classification performance. The study highlights the effectiveness of feature engineering and model optimization in enhancing DDoS detection accuracy, making machine learning a viable solution for real-time cybersecurity applications.

Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.

Rapid urbanization necessitates innovative traffic monitoring solutions. Traditional methods (fixed sensors/CCTV) face limitations in coverage, adaptability, and real-time processing. This review examines advancements (2015–2024) in vision-based unmanned aerial vehicle (UAV) traffic monitoring systems, evaluating their effectiveness in vehicle detection, traffic analysis, and congestion management. A systematic preferred reporting items for systematic reviews and meta-analyses (PRISMA)-guided analysis of 2,895 articles from IEEE Xplore, Scopus, Web of Science, and ACM Digital Library identified 49 eligible studies. Quantitative performance metrics (detection accuracy and latency) were standardized for cross-study comparison. Modern systems achieve 94% detection accuracy and 40 ms latency through edge computing and deep learning (e.g., you look only once (YOLO) and Faster region-based convolutional neural network (Faster R-CNN)). Multi-sensor fusion improves robustness by 35% in challenging conditions. However, battery life (reduced by 40% under processing load) and regulatory barriers remain critical constraints. Artificial intelligence (AI)-driven UAV systems enable real-time, high-accuracy traffic monitoring but require solutions for power efficiency and scalability. Future integration of 5G/6G and swarm intelligence holds promise for next-generation smart traffic management.