The detection of zero-day exploits presents a significant challenge in modern cybersecurity, as these attacks exploit unknown vulnerabilities before they are recognized and patched. Ensemble learning models have emerged as a promising approach to address this challenge by combining multiple base learners to enhance prediction accuracy and robustness. The performance of these models heavily depends on the quality and relevance of the features used during training. This chapter explores advanced strategies for improving ensemble learning accuracy in the context of zero-day exploit detection through sophisticated feature engineering techniques. The integration of external data sources, such as Common Vulnerabilities and Exposures (CVEs) and Indicators of Compromise (IoCs), plays a crucial role in enriching the feature set and enabling the detection of previously unseen attack patterns. Additionally, the chapter delves into the importance of data preprocessing, including cleaning, scaling, and dimensionality reduction, to optimize the data for ensemble models. Sensitivity analysis is highlighted as a key method for evaluating feature contributions, allowing for a deeper understanding of the factors influencing attack detection. By emphasizing domain-specific feature engineering and the continuous refinement of detection systems, this chapter provides a comprehensive approach to enhancing the effectiveness of ensemble models for zero-day exploit detection. The insights presented are intended to guide future research and practical applications in securing systems against evolving cyber threats.
The detection of zero-day exploits has become a central concern in the realm of cybersecurity [1]. These attacks target vulnerabilities that are unknown to security vendors or the affected system developers, leaving the systems exposed to malicious threats until the vulnerabilities are discovered and patched [2]. Zero-day exploits are particularly dangerous because they are often difficult to detect using traditional security measures, which rely heavily on known attack signatures [3]. As cyber threats continue to evolve, there is a growing need for more effective detection mechanisms capable of identifying these unseen vulnerabilities in real time [4]. Ensemble learning, a machine learning technique that combines the outputs of multiple base models, has emerged as a promising solution for improving the accuracy and robustness of zero-day exploit detection systems [5]. By aggregating diverse learning algorithms, ensemble models can enhance the system’s ability to detect novel attack patterns and mitigate the risks associated with zero-day vulnerabilities [6].
The success of ensemble learning models in detecting zero-day exploits largely depends on the quality of the features used for training the models [7]. Feature engineering is a crucial step in the machine learning pipeline that involves selecting, modifying, and constructing relevant input data to improve the performance of the model [8]. In the case of zero-day exploit detection, feature engineering can be a complex and multifaceted process, as it requires extracting meaningful information from large and heterogeneous data sources [9]. Features such as network traffic patterns, system call sequences, and software configurations can provide valuable insights into the presence of vulnerabilities [10]. Domain-specific knowledge, such as information from Common Vulnerabilities and Exposures (CVEs) and Indicators of Compromise (IoCs), can further enhance the feature set, enabling models to identify patterns related to known vulnerabilities and previous attacks [11].ÂÂÂÂ
