The field of cybersecurity is continually evolving in response to increasingly complex and frequent cyber threats. To address these challenges, machine learning (ML) algorithms and intrusion detection systems (IDS) have been extensively utilised. However, the success of these technologies is heavily dependent on the quality and scope of the datasets used for their training and evaluation. Current datasets, such as KDD Cup 99, NSL-KDD, and CSE-CIC-IDS2018, have been pivotal in the development of IDS, but they often fall short in capturing the full spectrum of modern cyber threats. These datasets typically lack the depth and variety required to train models for detecting sophisticated and evolving attack vectors. This study addresses this deficiency by introducing a novel dataset that includes a comprehensive range of advanced features aimed at improving the detection capabilities of ML algorithms and IDS. The proposed dataset is designed to better reflect real-world scenarios and incorporate subtle malicious patterns, thereby enhancing the effectiveness of cybersecurity defences against emerging threats.