The central promise of the Internet of Things (IoT) is to accelerate the interaction with surroundings. Appliances such as smartwatches, smart bulbs, thermostats, fitness trackers, next-generation vehicles, and so on have gained the ability to communicate and accept instructions from outside with the help of various embedded devices. While doing so, they almost always operate with little to no human interaction. These embedded devices, alongside a lot of benefits, also bring forth several security challenges. From the manufacturers' point of view, performance and production remain the top priority leaving security a less addressed problem. This practice has manifested itself in the form of various attacks including the Distributed Denial of Service (DDoS) where malware prepared with the aid of malicious nodes in IoT devices are used to carry out the attack. It is, therefore, essential to comprehend the nature of these attacks and swiftly identify infected devices to combat this situation. Machine Learning, or more particularly a branch of this technique commonly known as Deep Learning, has already demonstrated its outstanding potentials while administering with the heterogeneous data of diverse sizes. Using the Artificial Neural Network (ANN), this work suggests a method of detecting malicious nodes in IoT environments. The contribution of the paper is in two-fold. First, it classifies the usual and malicious patterns of IoT devices in the network, and second, describes a scheme for successfully detecting malicious nodes with 77.51% accuracy.