## 1 Introduction  
The idea of the Internet of Things (IoT) depends upon the incorporation of recognizable, varied physical substances (instant cameras, humans, sensors, animals, vehicles, etc.) and the cyber world with the capability of transferring data in a network deprived of human-to-computer or human-to-human interfaces [1]. IoT applications may range from modest appliances for a smart home to complicated devices in a smart grid. The IoT offers a marvelous chance for humanity across the globe [2]. With various objectives, conflicting IoTapplications take a joint set of features. Generally, an IoT primary node can execute three different activities: data processing and utilization, data collection, and data transmission. Many approaches to enhancing data access, authentication, and confidentiality are stated in the literature; however, even with such systems, IoT networks are vulnerable to many assaults pointed at network disturbance [3]. The diversity, growth, ubiquity, and complexity of the IoT expands the possible assault surfaces. Thus, intrusion-preventing apparatuses and signature-related intrusion detection (ID) techniques are needed for the potential assaults in the IoT network [4]. An intrusion detection system (IDS) is a device or software application that monitors a network for abnormal activities or policy violations. Any intrusion activity or violation is normally reported either to an administrator, or gathered centrally through the use of security information and an event management system. A defense system is needed for noticing new and effective intrusions. An IDS depends upon anomaly detection (i.e., statistically related) to achieve its purpose. Anomaly recognition has no need for previous recognition of assault signs [5]. Since IoT services cover an extensive range, there are varied transmission technologies with diverse values, which increases the threats to end-wise security. The assault surface size in the IoT is raised, due to the expansions in diversity and the complexity of the IoT [6]. Defense systems such as signature-related ID techniques do not function correctly for the adapted assaults, and, therefore, an IDS is necessary for new intrusions. The anomaly-related ID method drives this discovery structure, as there is no need for previous data regarding the assault signs [7]. Definite features of IoT systems were provided by several scholars in the facets of evolving an IDS. The devices or nodes in the IoT are source-limited and work with less influence. It is impossible to host an orthodox IDS that needs higher computational abilities and a higher level of power [8]. The protocols utilized in the IoT system are not similar to the protocols utilized in traditional systems [9]. The protocols utilized in such networks, namely, IPv6 with Constrained Application Protocol (CoAP) and low-power, wireless personal area networks (6 LoWPAN) make the network varied, leading to novel dimness and making it challenging to apply the IDS in the networks. Taking the aforementioned features into account, it becomes essential to advance a lightweight IDS that executes its purpose professionally in network security [10]. The word lightweight indicates that the scheme must operate on controlled sources obtainable in the IoT network nodes, and does not mean that the system is simple. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies the DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process.