In today's interconnected world, the threat of intrusion activities continues to rise, making it imperative to deploy effective security measures such as Intrusion Detection Systems (IDS). These systems play a vital role in monitoring network and system activities to identify unauthorised or malicious behaviour. The focus of this research is on evaluating the efficiency of different IDS in detecting anomalies in network traffic, specifically targeting Denial of Service (DDoS) attacks that exploit server vulnerabilities using IP addresses. The study utilises the CIC-DDoS 2019 dataset to analyse the performance of various IDS, particularly Network Intrusion Detection Systems (NIDSs), in predicting DDoS attacks accurately. To combat the diverse range of DDoS threats, a collective classifier is introduced, which combines four top-performing algorithms to enhance detection capabilities. By transforming the problem into a multilabel classification issue, the researchers aim to address the complexity of DDoS attacks effectively. Several machine learning (ML) and artificial intelligence (AI) algorithms are employed in the study, including Random Forest Classifier, Decision Tree Classifier, Support Vector Machine (SVM), Naïve Bayes, Multi-Layer Perceptron, Long Short-Term Memory (LSTM), and XGBoost Classifier. Evaluating the performance and computational efficiency of these algorithms is crucial to determining the most effective approach to detecting DDoS attacks. The results of the research highlight the effectiveness of the Random Forest Classifier and Multi-Layer Perceptron in accurately detecting DDoS attacks, as evidenced by their high accuracy rates on the test dataset. These findings underscore the importance of leveraging advanced ML algorithms to enhance the security of networks and systems against evolving cybersecurity threats. In conclusion, the study emphasises the significance of deploying robust IDS equipped with sophisticated ML algorithms to safeguard against intrusion activities like DDoS attacks. By continuously evaluating and improving the performance of these systems, organisations can enhance their cybersecurity posture and mitigate the risks posed by malicious actors in the digital landscape.