Given the increasing complexity of cyber threats, it is crucial to have strong anomaly detection systems in place for cybersecurity. This study investigates the utilization of machine learning techniques, particularly an ensemble method, to improve the ability to detect anomalies. The ensemble method synergistically incorporates the capabilities of three separate algorithms: Isolation Forest, Support Vector Machine (SVM), and Naive Bayes, resulting in a cybersecurity defense mechanism that is both resilient and precise. The study employs the CICIDS2017 dataset, which is a comprehensive and widely recognized benchmark in the field of cybersecurity. The dataset encompasses a wide range of network traffic scenarios, including both regular and malicious activities, making it an excellent resource for assessing the efficiency of anomaly detection algorithms. A rigorous preprocessing phase is utilized to guarantee the quality and pertinence of the data. The Isolation Forest algorithm, known for its efficient anomaly isolation capabilities, is incorporated into the ensemble to detect and capture the unique patterns displayed by malicious activities. The SVM classifier is utilized for its ability to define intricate decision boundaries, thereby improving the model’s ability to distinguish between normal and anomalous behavior. Naive Bayes, renowned for its simplicity and efficiency, enhances the ensemble by offering probabilistic insights into the probability of an instance being anomalous. The ensemble method is carefully designed to leverage the advantages of each individual algorithm, creating a collaborative and synergistic framework for detecting anomalies. The training process entails optimizing hyperparameters and fine-tuning model parameters to attain an optimal equilibrium between precision and recall. The experimental results showcase the effectiveness of the suggested ensemble technique, resulting in a remarkable accuracy of 99.21% on the CICIDS2017 dataset. The model demonstrates exceptional proficiency in identifying a diverse range of cyber threats while simultaneously maintaining a minimal rate of false positives. Comparative analyses using separate algorithms highlight the improved performance achieved by combining them into an ensemble. The research findings enhance anomaly detection methodologies in cybersecurity, providing a practical and efficient solution for protecting networks from emerging threats. The ensemble approach not only showcases exceptional precision but also underscores the capacity for collaborative models to tackle the complexities of contemporary cyber threats.