This paper presents a bidirectional generative adversarial network (BiGAN) for detecting cyberattacks using the IoT23 dataset. The proposed BiGAN model efficiently detects various types of attacks, achieving an accuracy of 98.8% and an F-score of 98.2%. The study addresses the challenge of data imbalance in intrusion detection systems (IDS), which hinders the performance of AI-based models. The BiGAN model is trained to generate synthetic data for network traffic, improving the detection of rare attack types. The model is evaluated using a 10-fold cross-validation approach, and the results show that the proposed method outperforms traditional machine learning models such as random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). The BiGAN model demonstrates superior performance in terms of accuracy, precision, and F-score compared to other approaches. The study also highlights the advantages of using BiGANs for cyberattack detection, including improved accuracy, reduced false positives, adaptability to dynamic environments, data augmentation, real-time detection, enhanced security posture, privacy preservation, and complementarity with existing solutions. The results indicate that the proposed BiGAN model is a valuable tool for detecting and mitigating advanced cyber threats. The study concludes that the BiGAN model is an effective solution for enhancing network threat detection and improving the performance of intrusion detection systems.This paper presents a bidirectional generative adversarial network (BiGAN) for detecting cyberattacks using the IoT23 dataset. The proposed BiGAN model efficiently detects various types of attacks, achieving an accuracy of 98.8% and an F-score of 98.2%. The study addresses the challenge of data imbalance in intrusion detection systems (IDS), which hinders the performance of AI-based models. The BiGAN model is trained to generate synthetic data for network traffic, improving the detection of rare attack types. The model is evaluated using a 10-fold cross-validation approach, and the results show that the proposed method outperforms traditional machine learning models such as random forest (RF), support vector machine (SVM), and long short-term memory (LSTM). The BiGAN model demonstrates superior performance in terms of accuracy, precision, and F-score compared to other approaches. The study also highlights the advantages of using BiGANs for cyberattack detection, including improved accuracy, reduced false positives, adaptability to dynamic environments, data augmentation, real-time detection, enhanced security posture, privacy preservation, and complementarity with existing solutions. The results indicate that the proposed BiGAN model is a valuable tool for detecting and mitigating advanced cyber threats. The study concludes that the BiGAN model is an effective solution for enhancing network threat detection and improving the performance of intrusion detection systems.