Intrusion detection systems (IDS) play a pivotal role in identifying and mitigating potential threats and vulnerabilities in IoT devices. IoT devices holds crucial data. Internet of Things (IoT) has rapidly evolved into a field that involves the interconnection and interaction of smart objects, which have embedded sensors, on-board data processing capability, and a means of communication, to provide automated services and applications. The proliferation of Internet of Things (IoT) devices usage has exponentially increased the attack surface, necessitating robust security mechanisms. This is because many IoT devices operate with limited computational resources, constrained memory, and low power capabilities, making them susceptible to security breaches. Additionally, the sheer diversity of device types, communication protocols, and deployment scenarios introduces a complex attack surface, providing malicious actors with numerous entry points to exploit. Traditional security measures have proved insufficient against sophisticated attacks targeting generally IoT ecosystems because of their high demand on resources. Therefore, this research explores on enhancing security for IoTs which are resource constraints with a model optimized for efficiency, minimizing computational overhead and energy consumption by employing an Intrusion Detection System (IDS). The model approach involves designing and developing of an IDS using machine learning algorithms for anomaly detection of the network traffic patterns to identify potential security breaches. The dataset was downloaded from Kaggle after creation of an account and cleaned to get relevant structured data for training and evaluating the model. The model training utilized pro deep neural network architecture and NSL-KDD dataset which was split into training (70%), validation (15%), and testing (15%) subsets using stratified sampling to maintain the class distribution across all subsets. The model's performance was assessed using multiple metrics like accuracy, precision, recall, F1-Score, FAR and Receiver Operating Characteristic Area Under the Curve) providing a comprehensive understanding of its capabilities. The model achieved Accuracy of 97.8%, Precision 0.976, false alarm rate of 1.54, 0.95, F1-score of 96.28% and an overall ROC AUC of 0.97. This results provided insights into the ability to correctly classify network traffic as either normal or malicious, as well as its effectiveness in detecting specific attack types in IoTs.