scholarly journals Research on SSDF Attack Detection Algorithm in Cognitive Internet of Things

2021 ◽  
Author(s):  
Miao Liu ◽  
Di Yu ◽  
Zhuo-Miao Huo ◽  
Zhen-Xing Sun
Keyword(s):  
Internet Of Things ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Hidden Markov ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Iot Devices ◽  
Cognitive Internet Of Things

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.

Ward_p_HMM: A Shilling Attack Detection Technique Using Ward_p Method and Hidden Markov Model

2021 ◽  
Vol 25 (3) ◽  
Author(s):  
Keya Chowdhury ◽  
Abhishek Majumder ◽  
Joy Lal Sarkar ◽  
Sukanta Chakraborty ◽  
Sudipta Roy
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Attack Detection ◽  
Detection Technique ◽  
Shilling Attack ◽  
Shilling Attack Detection

scholarly journals A novel seizure detection algorithm informed by hidden Markov model event states

2016 ◽  
Vol 13 (3) ◽  
pp. 036011 ◽  
Author(s):  
Steven Baldassano ◽  
Drausin Wulsin ◽  
Hoameng Ung ◽  
Tyler Blevins ◽  
Mesha-Gay Brown ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Seizure Detection ◽  
Detection Algorithm

Combinatorial Detection Algorithm for Copy Number Variations Using High-throughput Sequencing Reads

2019 ◽  
Vol 33 (14) ◽  
pp. 1950022
Author(s):  
Hai Yang ◽  
Daming Zhu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
High Throughput ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Hidden Markov ◽  
Read Depth ◽  
Detection Algorithm ◽  
Gc Bias ◽  
Unmapped Reads

Copy number variation (CNV) is a prevalent kind of genetic structural variation which leads to an abnormal number of copies of large genomic regions, such as gain or loss of DNA segments larger than 1[Formula: see text]kb. CNV exists not only in human genome but also in plant genome. Current researches have testified that CNV is associated with many complex diseases. In this paper, guanine-cytosine (GC) bias, mappability and their effect on read depth signals in sequencing data are discussed first. Subsequently, a new correction method for GC bias and an improved combinatorial detection algorithm for CNV using high-throughput sequencing reads based on hidden Markov model (CNV-HMM) are proposed. The corrected read depth signals have lower correlation with GC content, mappability of reads and the width of analysis window. Then we create a hidden Markov model which maps the reads onto the reference genome and records the unmapped reads. The unmapped reads are counted and normalized. The CNV-HMM detects the abnormal signal of read count and gains the candidate CNVs using the expectation maximization (EM) algorithm. Finally, we filter the candidate CNVs using split reads to promote the performance of our algorithm. The experiment result indicates that the CNV-HMM algorithm has higher accuracy and sensitivity for CNVs detection than most current detection algorithms.

Sign in / Sign up

Export Citation Format

Share Document