Zero-day attacks and other new cyber threats are often hard to detect for traditional IDSs. They heavily depend on static signatures or known cyberattack patterns and since the vulnerabilities are unknown, the attacker might identify and exploit these unknown vulnerabilities and take advantage of them before a patch can be released. The advanced intrusion detection systems which have the capability to detect known and unexpected attacks are in demand as cyber threats are becoming increasingly sophisticated. To achieve an optimal balance between exploration and exploitation, the proposed approach employs Grasshopper and Whale Optimization Algorithm (GWOA) for efficient feature selection and classification, while Hierarchical Differential Evolution-based Artificial Immune System (HiDE-AIS) mimics the adaptive capabilities of the human immune system, enhancing the system's ability to recognise malicious and benign network traffic in dynamic environments. This paper puts forward a Bio-Inspired Network Intrusion Detection System (BINIDS) which integrates HiDE-AIS with the GWOA to overcome limitations of traditional intrusion detection systems. The results obtained prove the efficiency of hybrid bio-inspired optimization techniques in enhancing the model’s capability in detecting intrusions in networks.