Data-driven network layer security detection model and simulation for the Internet of Things based on an artificial immune system

2020 ◽  
Author(s):  
Bo Yang ◽  
Meifang Yang
Keyword(s):  
Immune System ◽  
Internet Of Things ◽  
Artificial Immune System ◽  
Data Driven ◽  
The Internet ◽  
Artificial Immune ◽  
Network Layer ◽  
Detection Model ◽  
Model And Simulation ◽  
The Internet Of Things

A Security Situation Sense Model Based on Artificial Immune System in the Internet of Things

2011 ◽  
Vol 403-408 ◽  
pp. 2457-2460 ◽  
Author(s):  
Run Chen ◽  
Cai Ming Liu ◽  
Lu Xin Xiao
Keyword(s):  
Immune System ◽  
Internet Of Things ◽  
Artificial Immune System ◽  
Theory And Practice ◽  
The Internet ◽  
Security Threat ◽  
Artificial Immune ◽  
Model Based ◽  
The Internet Of Things ◽  
Sense Model

Grasping security situation of the Internet of Things (IoT) is useful to work out a scientific and reasonable strategy to defend the IoT security. In the interest of resolving the problems of the security situation sense technology for IoT, a security situation sense model based on artificial immune system for IoT is proposed in this paper. Security threat sense sub-model, formulation mechanism for security threat intensity and security situation assessment sub-model are established. The security threats in the IoT environment are surveyed effectively. Quantitative and accurate assessment for the Real-Time security situation is realized. Theoretical analysis shows that the proposed model is significative of theory and practice.

An Artificial Immune-Based Distributed Intrusion Detection Model for the Internet of Things

2011 ◽  
Vol 366 ◽  
pp. 165-168 ◽  
Author(s):  
Run Chen ◽  
Cai Ming Liu ◽  
Chao Chen
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Theory Model ◽  
The Internet ◽  
Artificial Immune ◽  
Detection Model ◽  
Detection Technology ◽  
Proposed Model ◽  
Distributed Intrusion Detection ◽  
The Internet Of Things

Traditional detection technology for network attacks is difficult to adapt the complicated and changeful environment of the Internet of Things (IoT). In the interest of resolving the distributed intrusion detection problem of IoT, this paper proposes an artificial immune-based theory model for distributed intrusion detection in IoT. Artificial immune principles are used to solve the problem of IoT intrusion detection. Antigen, self, non-self and detector in the IoT environment are defined. Good immune mechanisms are simulated. Detector is evolved dynamically to make the proposed model have self-learning and self-adaptation. The outstanding detectors which have accepted training are shared in the whole IoT to adapt the local IoT environment and improve the ability of global intrusion detection in IoT. The proposed model is expected to realize detecting intrusion of IoT in distribution and parallelity.

Immune System for the Internet of Things Using Edge Technologies

2019 ◽  
Vol 6 (3) ◽  
pp. 4774-4781 ◽  
Author(s):  
Rodrigo Roman ◽  
Ruben Rios ◽  
Jose A. Onieva ◽  
Javier Lopez
Keyword(s):  
Immune System ◽  
Internet Of Things ◽  
The Internet ◽  
The Internet Of Things

Applications of Cloud-Based Internet of Things

2021 ◽  
pp. 65-84
Author(s):  
Nipun R. Navadia ◽  
Gurleen Kaur ◽  
Harshit Bhardwaj ◽  
Taranjeet Singh ◽  
Aditi Sakalle ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Business Model ◽  
Data Driven ◽  
The Internet ◽  
Cloud Infrastructure ◽  
Business Decisions ◽  
Goods And Services ◽  
Cloud Network ◽  
The Internet Of Things

Cloud storage is a great way for companies to fulfill more of their data-driven needs and excellent technology that allows the company to evolve and grow at a faster pace, accelerating growth and providing a flexible forum for developers to build useful apps for better devices to be developed over the internet. The integration of cloud computing and the internet of things creates a scalable, maintainable, end-to-end internet of things solution on the cloud network. By applying the infrastructure to the real universe, it generates sources of insight. Cloud computing and IoT are separate technology but are closely associated and are termed as ‘cloud-based IoT' as IoT has the ability to create intelligent goods and services, gather data that can affect business decisions and probably change the business model to boost success and expansion, and cloud infrastructure can be at the heart of all IoT has to deliver.

scholarly journals Internet of things - A Survey

2021 ◽  
pp. 417-438
Author(s):  
Pranjal Upadhyay ◽  
Prof. Deepak Upadhyay
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Detailed Knowledge ◽  
Network Layer ◽  
Survey Paper ◽  
The Internet Of Things

In the survey paper we defined all the topics related to the Internet of Things. All the components related to the internet of things in Details. You will get detailed knowledge about the Internet of things ecosystem, Internet of things Elements, Internet of things Architecture. Also, we will cover all the internet of things protocols and brief about protocols. In this we will provide the details of attack based on Protocols and at the end we justify why RPL is useful over 6Low-PAN in the internet on things network layer.

scholarly journals System Log Detection Model Based on Conformal Prediction

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 232 ◽  
Author(s):  
Yitong Ren ◽  
Zhaojun Gu ◽  
Zhi Wang ◽  
Zhihong Tian ◽  
Chunbo Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
The Internet ◽  
Learning Method ◽  
Log Data ◽  
Detection Model ◽  
Statistical Learning Method ◽  
The Internet Of Things

With the rapid development of the Internet of Things, the combination of the Internet of Things with machine learning, Hadoop and other fields are current development trends. Hadoop Distributed File System (HDFS) is one of the core components of Hadoop, which is used to process files that are divided into data blocks distributed in the cluster. Once the distributed log data are abnormal, it will cause serious losses. When using machine learning algorithms for system log anomaly detection, the output of threshold-based classification models are only normal or abnormal simple predictions. This paper used the statistical learning method of conformity measure to calculate the similarity between test data and past experience. Compared with detection methods based on static threshold, the statistical learning method of the conformity measure can dynamically adapt to the changing log data. By adjusting the maximum fault tolerance, a system administrator can better manage and monitor the system logs. In addition, the computational efficiency of the statistical learning method for conformity measurement was improved. This paper implemented an intranet anomaly detection model based on log analysis, and conducted trial detection on HDFS data sets quickly and efficiently.

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