Computer network users experience persistent attacks due to vulnerabilities in the systems and network. An intrusion detection system (IDS) is a system that checks the flow of data in the network and alerts or detects abnormal traffic. Even though there is no perfect system anywhere on earth, there may be a cause for breakdown/downtime now and then. More so, the intrusion detection system may run into errors in a bid to detect malware in incoming traffic on a network. Hence, the need to develop a system that can detect intrusion in the network and do that with minimal error. This proposed paper is an improved deep sparse autoencoder-driven network intrusion detection system (IDSAE NIDS) that addresses the issue of interpretability of the L2 regularization technique employed in other works. The proposed IDSAE NIDS model was trained using a mini-batch gradient descent algorithm, L1 regularization technique, and ReLU activation function to achieve a better model performance. Experimental results based on the NSL-KDD dataset show that our approach provides significant performance and improvements over other deep sparse autoencoder NIDSs.