scholarly journals Intrusion Detection Analysis of Internet of Things considering Practical Byzantine Fault Tolerance (PBFT) Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Leixia Li ◽  
Yong Chen ◽  
Baojun Lin
Keyword(s):  
Fault Tolerance ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection Method ◽  
Detection Methods ◽  
The Internet ◽  
Byzantine Fault Tolerance ◽  
Byzantine Fault ◽  
Operation Process ◽  
The Internet Of Things

In order to improve the security performance and accuracy of the Internet of things in the use process, it is necessary to use the Internet of things intrusion detection method. At present, the problem of inconsistency between the accuracy of detection results and nodes is more prominent when the Internet of things intrusion detection methods are running. This paper proposes a practical Byzantine fault-tolerant intrusion detection method for the use process of the Internet of things. This method introduces the intrusion detection method and the operation function of foreign attackers on the basis of practical Byzantine fault tolerance; using the expected utility function to the corresponding benefit function of practical Byzantine fault tolerance, the results of Internet of things intrusion detection model can be effectively calculated. Finally, the experimental results show that compared with the existing intrusion detection methods, the proposed method can effectively reduce the energy consumption of the Internet of things in the operation process, can effectively reduce 14.3% and 7.8%, and can effectively reduce the energy consumption of the Internet of things in the operation process.

Research on Data Security Detection Algorithm in IoT Based on K-means

2021 ◽  
Vol 14 ◽  
Author(s):  
Jianxing Zhu ◽  
Lina Huo ◽  
Mohd Dilshad Ansari ◽  
Mohammad Asif Ikbal
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Data Security ◽  
Detection Method ◽  
Detection Algorithm ◽  
The Internet ◽  
Human Beings ◽  
Network Attacks ◽  
Hybrid Detection ◽  
The Internet Of Things

Background: The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Method: Proposed method Aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks, the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed Results: This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. Conclusions: This paper proposes an efficient intrusion detection method for applications with Internet of Things

scholarly journals Research on Data Security Detection Algorithm in IoT Based on K-means

2021 ◽  
Vol 22 (2) ◽  
Author(s):  
Jianxing Zhu ◽  
Lina Huo ◽  
Mohd Dilshad Ansari ◽  
Mohammad Asif Ikbal
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Data Security ◽  
Detection Method ◽  
Detection Algorithm ◽  
The Internet ◽  
Human Beings ◽  
Network Attacks ◽  
Hybrid Detection ◽  
The Internet Of Things

The development of the Internet of Things has prominently expanded the perception of human beings, but ensuing security issues have attracted people's attention. From the perspective of the relatively weak sensor network in the Internet of Things. Proposed method is aiming at the characteristics of diversification and heterogeneity of collected data in sensor networks; the data set is clustered and analyzed from the aspects of network delay and data flow to extract data characteristics. Then, according to the characteristics of different types of network attacks, a hybrid detection method for network attacks is established. An efficient data intrusion detection algorithm based on K-means clustering is proposed. This paper proposes a network node control method based on traffic constraints to improve the security level of the network. Simulation experiments show that compared with traditional password-based intrusion detection methods; the proposed method has a higher detection level and is suitable for data security protection in the Internet of Things. This paper proposes an efficient intrusion detection method for applications with Internet of Things.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

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