Internets of Things (IoT) are distinguished by different devices, which support the ability to provide innovative services in various applications. The main aspects of security which involves maintaining confidentiality and authentication of data, integrity within the IoT network, privacy and trust among IoT devices are important issues to be addressed. Conventional security policies cannot be used directly to IoT devices due to the limitation of memory and high power consumption factors. One of the security breaches in the intranet is lack of encryption due to the IoT devices infrastructure. The basic IoT devices are 8-bit, low-cost, limited memory and power consumption devices which limit the complex algorithm execution. The key distribution is another major challenge in IoT network.
This paper proposes a solution to transmitting messages by adopting Random Number generation and distribution of session key for every message without any difficulty. It gives better result to resist from the brute force attack in a network.

Although there are different techniques proposed for intrusion detection in the literature, most of them consider standalone misuse or anomaly intrusion detection systems. However, by taking the advantages of both systems a better hybrid intrusion detection system can be developed. In this paper, we present an effective hybrid layered intrusion detection system for detecting both previously known and zero-day attacks. In particular, a two layer system that combines misuse and anomaly intrusion detection systems is proposed. The first layer consists of misuse detector which can detect and block known attacks and the second layer comprises of anomaly detector which can efficiently detect and block previously unknown attacks. The misuse detector is modeled based on random forests classifier and the anomaly detector is built using bagging technique with ensemble of one-class support vector machine classifiers. Data pre-processing is done using automatic feature selection and data normalization. Experimental results show that the proposed intrusion detection system outperforms other well-known intrusion detection systems in detecting both previously known and zero-day attacks.