The increased application of internet of things escalates the security concerns on these devices, when not handled properly would leave the system vulnerable to external threats. The current security mechanism requires high computation power and storage, they use technologies such as artificial intelligence, machine learning, and deep learning, moreover they cannot be implemented easily in all the environments. To facilitate this issue, we have proposed a network-based intrusion detection system on the internet of things device itself, that is simple to implement in any environment and requires less computation power and storage needs and has the ability to detect zero-day threats along with small number of false positive rate. The proposed system is developed using techniques such as deep packet inspection, packet sniffing, and regex string parsing along with the capability to block unauthorized client by dropping the incoming packet from that client. The proposed system uses a baseline profile which consists of valid operation of the internet of things device and will compare all the incoming traffic with this profile; moreover, it will determine whether to process the traffic normally or block it.