Rademics Research Institute

Abstract

Anomaly detection in network systems was a critical task for ensuring security and stability in the face of ever-evolving threats. Hybrid models that combine clustering and classification techniques have emerged as effective solutions for identifying anomalies with higher accuracy and efficiency. This chapter explores the integration of supervised and unsupervised learning methods to address the complexities of detecting network anomalies. The synergy between clustering algorithms, such as K-means and DBSCAN, and classification models, including support vector machines (SVM) and random forests, enhances the model’s capability to detect both known and novel threats. Key challenges, such as handling data imbalance, optimizing model parameters, and feature engineering, are discussed in the context of hybrid models. Additionally, the chapter examines the use of statistical and domain-specific features to improve detection accuracy and reduce false positives. Practical case studies highlight the application of hybrid models in real-world network environments, demonstrating their effectiveness in scenarios such as intrusion detection and DDoS attack identification. The balance between detection accuracy and computational efficiency is also critically evaluated, providing insight into the practical deployment of hybrid models in large-scale network systems. This chapter offers a comprehensive framework for researchers and practitioners aiming to develop robust, scalable, and efficient hybrid models for network anomaly detection. 

Introduction

Anomaly detection was a crucial element in modern network security, as it helps identify suspicious activities that deviate from normal behavior, which could indicate potential threats such as cyberattacks, intrusions, or fraud [1]. With the increasing complexity of network systems and the diversity of attacks targeting them, traditional detection methods often fall short in accurately identifying unknown or evolving threats [2]. This limitation has driven the development of advanced techniques that combine both supervised and unsupervised learning methods, known as hybrid models [3]. These models offer a promising solution by enhancing the capabilities of individual algorithms through synergy, thereby improving the detection of network anomalies with higher precision and efficiency [4].

The fusion of clustering and classification techniques is particularly effective in hybrid models [5]. Clustering algorithms like K-means and DBSCAN excel in grouping data based on inherent similarities, allowing the identification of previously unknown anomalies that might not align with predefined attack signatures [6]. On the other hand, classification models, such as support vector machines (SVM) or decision trees, are well-suited for distinguishing between normal and anomalous data, based on labeled training data [7]. The integration of these methods allows for a multi-faceted approach, where clustering can uncover potential outliers, and classification helps refine the decision-making process to ensure accurate anomaly detection [8]. This complementary relationship between clustering and classification provides a more robust and adaptable solution for anomaly detection in dynamic network environments [9].