<p>This study investigates the optimal placement of security controllers in Software-Defined IoT (SD-IoT) networks to enhance intrusion detection and improve network resilience against cyber threats. In SD-IoT environments, the strategic positioning of security controllers plays a vital role in monitoring malicious activity and responding to attacks effectively. However, inefficient controller placement can increase detection time, heighten vulnerability, and degrade overall network performance. To address this challenge, we propose multi-objective optimization approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to determine the optimal number and locations of security controllers. The proposed method simultaneously considers network topology, resource constraints, and the characteristics of potential attack patterns to minimize intrusion detection delay, reduce the impact of threats on data transmission, and enhance load distribution. For evaluation, simulations were conducted using topologies from the Internet Topology Zoo. The proposed method was compared against other existing methods, such as Random Placement, Greedy Algorithm and Iterated Local Search strategies, in terms of controllers required, security, latency, load balancing, and deployment cost. Results demonstrate that our approach significantly outperforms other methods, for instance achieving approximately a 60% reduction in deployment cost in the large-scale Cogentco topology. These findings highlight the effectiveness of integrating NSGA-II for strategic controller placement to strengthen security and operational efficiency in SD-IoT networks.</p>