A Cybersecurity Classification Model for Detecting Cyberattacks

The complexity of cybersecurity threats is constantly increasing and making it difficult for enterprises to protect themselves from cyberattack. This study offers an ensemble cybersecurity classification (ECC) model to improve cyberattack detection in response to the dynamic network threat landscape. The proposed model enhances the overall accuracy, robustness, and induction by combining the advantages of many machine learning (ML) algorithms, such as SVM and LR. Here, the training is implemented by using SVM. The ensemble model incorporates a variety of classifiers, such as autoencoders with a transfer learning approach, each with a specific focus on capturing various aspects of cyberthreats, including malware analysis, anomaly detection, and intrusion detection. By utilizing the complementary qualities of individual classifiers, the ensemble technique seeks to outperform standalone models. A large variety of cyberattack scenarios are included in the comprehensive and current dataset used to train the model. Here, a thorough testing is used to assess the ensemble’s efficacy against a range of cyberattacks, both existing and new. Evaluation parameters, including F1-score, precision, and recall, evaluate how well the model differentiates malicious and legitimate network activity. The findings show that the ensemble model may increase detection accuracy and decrease the false detection rate by strengthening the system’s overall security posture.