Hidden Markov Model for Internet of Things Data Analysis

Author(s):  
Vladimir Tanasiev ◽  
Anatoli Paul Ulmeanu ◽  
Adrian Badea
Nanoscale ◽  
2017 ◽  
Vol 9 (10) ◽  
pp. 3458-3465 ◽  
Author(s):  
Jianhua Zhang ◽  
Xiuling Liu ◽  
Yi-Lun Ying ◽  
Zhen Gu ◽  
Fu-Na Meng ◽  
...  

2012 ◽  
Vol 263-266 ◽  
pp. 2949-2952
Author(s):  
Xiu Mei Wei ◽  
Xue Song Jiang ◽  
Xin Gang Wang

Along with the development of Internet of Things (IOT), there are a lot of increasingly serious security problems. The traditional intrusion detection method cannot adapt to the requirement of IOT. In this paper we advance a new intrusion detection method which can adapt to IOT. It is based on Hidden Markov Model (HMM), which is named as Hidden Markov state time delay sequence embedding (HMMSTdse) method.


2021 ◽  
Author(s):  
Miao Liu ◽  
Di Yu ◽  
Zhuo-Miao Huo ◽  
Zhen-Xing Sun

Abstract The Internet of Things (IoT) is a new paradigm for connecting various heterogeneous networks.cognitive radio (CR) adopts cooperative spectrum sensing (CSS) to realize the secondary utilization of idle spectrum by unauthorized IoT devices,so that IoT objects can effectively use spectrum resources.However, the abnormal IoT devices in the cognitive Internet of Things will disrupt the CSS process. For this attack, we propose a spectrum sensing strategy based on the weighted combining of the Hidden Markov Model. In this method, Hidden Markov Model is used to detect the probability of malicious attack of each node and report it to the fusion center (FC). FC allocates a reasonable weight value according to the evaluation of the submitted observation results to improve the accuracy of the sensing results.Simulation results show that the detection performance of spectrum sensing data forgery(SSDF) attack in cognitive Internet of Things is better than that of K rank criterion in hard combining.


2021 ◽  
Vol 1873 (1) ◽  
pp. 012084
Author(s):  
Mingwei Tang ◽  
Xiaoliang Chen ◽  
Hongyun Mao ◽  
Tian Yang ◽  
Jianhua Xie ◽  
...  

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