The Software-Defined Network (SDN) is a new network paradigm that promises more dynamic and efficiently manageable network architecture for new-generation networks. With its programmable central controller approach, network operators can easily manage and control the whole network. However, at the same time, due to its centralized structure, it is the target of many attack vectors. Distributed Denial of Service (DDoS) attacks are the most effective attack vector to the SDN. The purpose of this study is to classify the SDN traffic as normal or attack traffic using machine learning algorithms equipped with Neighbourhood Component Analysis (NCA). We handle a public “DDoS attack SDN Dataset” including a total of 23 features. The dataset consists of Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and Internet Control Message Protocol (ICMP) normal and attack traffics. The dataset, including more than 100 thousand recordings, has statistical features such as byte_count, duration_sec, packet rate, and packet per flow, except for features that define source and target machines. We use the NCA algorithm to reveal the most relevant features by feature selection and perform an effective classification. After preprocessing and feature selection stages, the obtained dataset was classified by k-Nearest Neighbor (kNN), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM) algorithms. The experimental results show that DT has a better accuracy rate than the other algorithms with 100% classification achievement.

This study aims to improve the automatic detection of unwanted emails using advanced machine learning and deep learning methods. By reviewing current research over the past five years, a comprehensive combined dataset structure was created containing a total of 81,586 email samples from seven different spam datasets. Class imbalance was addressed through the application of random oversampling and class-weighted loss, and the decision threshold was subsequently tuned for deployment. Among classical machine learning solutions, Random Forest (RF) emerged as the most successful method, while deep learning approaches, such as Transformer-based models like Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) and Robustly Optimized BERT Pretraining Approach (RoBERTa), demonstrated superior performance. The highest test score (99.62%) on a combined static dataset was achieved with a multimodal architecture that combines deep meaningful text representations from DistilBERT with structural text features. Beyond this static performance benchmark, the study investigates the critical challenge of concept drift by performing a temporal analysis on datasets from different eras. The results reveal a significant performance degradation in all models when tested on modern spam, highlighting a critical vulnerability of statically trained systems. Notably, the Transformer-based model demonstrated greater robustness against this temporal decay compared to traditional methods. This study offers not only an effective classification solution but also provides crucial empirical evidence on the necessity of adaptive, continually learning systems for robust spam detection.