以下是本文的主要贡献：• 本文描述了一种在认知物联网中发现和检测恶意物

The following are the main contributions of <br>this article : • This article describes a method for discovering and detecting malicious IoT devices in the cognitive IoT. <br>• Proposed a weighted fusion scheme based on hidden Markov model to discover malicious attack devices in CRIoT, so that the final decision made by FC has high accuracy. <br>• Use Hidden Markov Model to determine the size of the weight, and it has a higher detection rate when there are more malicious devices. <br>• The weighted fusion scheme greatly reduces the inaccuracy of a single node's perception. <br>• The improvement of the detection rate of MIDs improves the overall spectrum utilization efficiency. <br>The rest of this article is organized as follows: Section 2 introduces related work. Section 3 describes the system model. Section 4 describes the model of resisting SSDF attacks based on the hidden Markov model. In Section 5, experimental simulations are carried out and the results are analyzed. Section 6 summarizes the paper.

The main contributions of this paper are as follows<br>This paper describes a method to discover and detect malicious Internet of things devices in cognitive Internet of things.<br>A weighted fusion scheme based on Hidden Markov model is proposed to detect malicious attack devices in criot, which makes the final FC decision with high accuracy.<br>Using hidden Markov model to determine the weight of the size, in the case of more malicious devices also has a high detection rate.<br>The weighted fusion scheme greatly reduces the sensing inaccuracy of a single node.<br>The improvement of mid detection rate improves the overall spectrum efficiency.<br>The rest of this paper is organized as follows: Section 2 introduces the related work. Section 3 describes the system model. Section 4 describes the model of resisting SSDF attack based on Hidden Markov model. In Section 5, the experimental simulation is carried out and the results are analyzed. Section 6 summarizes the thesis.<br>

The following are the main contributions of this paper:This paper describes a method for discovering and detecting malicious IoT devices in cognitive IoT.A weighted fusion scheme based on hidden Markov model is proposed, which is used to find malicious attack devices in CRIoT, and makes the final decision made by FC highly accurate.Using hidden Markov model to determine the weight, it has a higher detection rate even when there are many malicious devices.The weighted fusion scheme greatly reduces the inaccuracy of single node perception.The improvement of MIDs detection rate improves the overall spectrum utilization efficiency.The rest of this paper is organized as follows: Section 2 introduces the related work. Section 3 describes the system model. Section 4 describes the model of resisting SSDF attack based on hidden Markov model. In Section 5, the experimental simulation is carried out and the results are analyzed. Section 6 summarizes the thesis.