Machine learning-based hybrid technique to enhance cyber-attack perspective

The rapid proliferation of interconnected devices and Internet of Things (IoT) applications has intensified cybersecurity risks for individuals, organizations, and critical infrastructures. Despite advances in encryption and authentication protocols, networks remain vulnerable to sophisticated cyber threats, particularly Distributed Denial of Service (DDoS) attacks. This study investigates the effectiveness of hybrid machine learning (ML) models in distinguishing between benign traffic and two DDoS variants—DDOS-ACK and DDOS-PSH-ACK. Building on recent advances in intrusion detection, we integrate Fuzzy C-Means clustering with classifiers such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), and AdaBoost (ADB). Experiments conducted on benchmark datasets evaluate models using accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). Results show that the hybrid models outperform standalone classifiers, particularly in handling imbalanced datasets and overlapping class boundaries. These findings underscore the practical viability of clustering-enhanced ML for real-time, attack-specific intrusion detection systems in IoT and cloud-enabled environments.