This study investigates the application of deep learning techniques for the detection of Denial of Service (DoS) attacks in network traffic using the NSL-KDD dataset. A Deep Neural Network (DNN) model is proposed and optimized for intrusion detection. The model consists of a 41-feature input layer, two fully connected hidden layers containing 128 and 64 neurons respectively and a SoftMax activated output layer for multiclass classification. The hidden layer used ReLU activation function and the model was optimized using Adam optimizer. The dataset was preprocessed using feature encoding, normalization and label transformation. The dataset was used with its standard predefined split: KDDTrain+ for training/validation and KDDTest+ for testing. The training data was further divided into 80% for training and 20% for validation. The effectiveness of the DNN was compared against traditional machine learning models, including Logistic Regression, LightGBM (LGBM), and CatBoost. Key evaluation metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each model in detecting network intrusions. The results demonstrate that the DNN model achieves an accuracy of 86% on the test dataset, consistently outperforming the traditional models across all key metrics. These findings highlight the advantages of deep learning for anomaly-based intrusion detection, particularly in handling complex network traffic patterns. This study contributes to advancing network security by leveraging the capabilities of DNNs for real-time DoS detection, scalability, and practical implementation in modern cybersecurity frameworks.