The widespread use of interconnectivity and interoperability of computing systems have become an indispensable necessity to enhance our daily activities. Simultaneously, it opens a path to exploitable vulnerabilities that go well beyond human control capability. The vulnerabilities deem cyber-security mechanisms essential to assume communication exchange. Secure communication requires security measures to combat the threats and needs advancements to security measures that counter evolving security threats. This paper proposes the use of deep learning architectures to develop an adaptive and resilient network intrusion detection system (IDS) to detect and classify network attacks. The emphasis is how deep learning or deep neural networks (DNNs) can facilitate flexible IDS with learning capability to detect recognized and new or zero-day network behavioral features, consequently ejecting the systems intruder and reducing the risk of compromise. To demonstrate the model's effectiveness, we used the UNSW-NB15 dataset, reflecting real modern network communication behavior with synthetically generated attack activities.