## 5 Discussion
For instance, parallel processing can take advantage of the inherent parallelism in neural networks, accelerating training and inference tasks. Distributing the workload across multiple processing units, such as GPUs or TPUs, can significantly reduce the time needed for training and evaluating deep learning models. Distributed training is another technique to improve the scalability of deep learning-based intrusion detection systems. By partitioning the training data and model parameters across multiple devices or nodes, we can utilize the collective computational power of these resources to train large-scale models more effectively. Additionally, advanced algorithms like asynchronous stochastic gradient descent and distributed batch normalization can be incorporated to optimize synchronization and communication between the nodes. Model compression is a key consideration for deploying deep learning models in intrusion detection systems. Given that these models often contain many parameters, they can be resource-intensive, which may pose challenges in resource-constrained environments. Techniques such as weight pruning, quantization, and knowledge distillation can reduce the size and complexity of the models without significantly compromising their performance. By compressing the models, we can achieve faster inference times and lower memory usage, making them more suitable for real-world deployment. In addition to these optimization techniques, we also explored the impact of data preprocessing, feature engineering, and data balancing on the performance of intrusion detection models. Through correlation analysis, we identified and removed highly correlated features, enhancing the efficiency of the models. We also applied the synthetic minority over-sampling technique (SMOTE) to address the class imbalance in the dataset, improving performance for underrepresented classes. Thus, our research has demonstrated the potential of deep learning-based intrusion detection systems in effectively detecting and mitigating cyber threats in large-scale network environments. By leveraging advanced optimization techniques and data preprocessing strategies, we enhanced these models' scalability, efficiency, and performance, making them suitable for real-world deployment. As cyber threats evolve and become more sophisticated, deep learning-based intrusion detection systems will play an increasingly important role in safeguarding our networks and digital assets. Our study highlights the effectiveness of machine learning and deep learning models for network intrusion detection, which is critical as cyber threats continue to evolve and pose significant risks to organizations. By using advanced models like random forest and deep learning techniques such as MLP, CNN, and LSTM, we can improve the ability to detect complex attack patterns and adapt to new types of network threats. As cyberattacks grow in sophistication, our research suggests that leveraging deep learning approaches will be key to building more robust, adaptable IDS solutions capable of protecting critical infrastructure. While this study demonstrates the effectiveness of machine learning and deep learning models for intrusion detection, several limitations must be acknowledged. One key challenge is the dataset-specific biases present in CIC-IDS2017. Since the dataset is generated in a controlled environment, it may not fully capture the variability and evolving nature of real-world network traffic, potentially limiting the generalizability of the models. Additionally, certain attack categories in the dataset are underrepresented, which could impact model performance on rare attack types. Beyond SMOTE, additional strategies such as data augmentation, cost-sensitive learning, and advanced resampling techniques can be explored to handle imbalanced datasets more effectively. Another significant limitation is the need for real-time inference in practical deployment scenarios.