Hybrid approach towards intrusion detection enhancement for advancement of IoT security
Mohit Kumar, Sanjay Kumar Dubey · 2025
The escalating security threats within Internet of Things (IoT) ecosystems necessitate innovative approaches to intrusion detection. This paper proposes a novel strategy for bolstering IoT security through the fusion of metaheuristic algorithms and deep learning methodologies. An exhaustive review of existing literature highlights the limitations of conventional intrusion detection systems in dynamic IoT environments. Subsequently, the theoretical foundations of metaheuristics and deep learning relevant to intrusion detection are elucidated. Methodologically, the study outlines the integration of metaheuristics and deep learning tailored to IoT environments. Rigorous experimentation and evaluation demonstrate the effectiveness of this approach in accurately identifying intrusions while minimizing false positives. The implications of these findings for enhancing IoT security are discussed, along with potential avenues for further research. By combining metaheuristic and deep learning methodologies, this research aims to strengthen the resilience of IoT ecosystems against malicious activities.