Intrusion Detection Model Based on Hierarchical Structure in Wireless Sensor Networks

Author(s):  
Lei Li ◽  
Yan-hui Li ◽  
Dong-yang Fu ◽  
Wan Ming
2018 ◽  
Vol 14 (09) ◽  
pp. 53
Author(s):  
Linlin Li ◽  
Liangxu Sun ◽  
Gang Wang

<strong>This paper, due to the intrusion detection problem in Wireless Sensor Networks, proposes an intrusion detection model based on the Danger Theory instead of the traditional Self-NonSelf theory. The intrusion detection model has two layers structure including danger perception and control decision, and it uses a multi-node cooperation mechanism. The perception node can realize the danger perception with Projection Pursuit Algorithm, and the decision node can detect the intrusion in detail with Extreme Learning Machine Algorithm. The logic process between their layers is consistent with the Danger Theory. The proposed model can realize the data trust between nodes with the Beta distribution trust evaluation method. By the simulations in the MATLAB, the proposed intrusion detection model on the whole is better than the SNS model at the aspects including classification training, danger perception, false negative rate, false positive rate and energy consumption.</strong>


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jeng-Shyang Pan ◽  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Hui-Qi Zhao ◽  
Gao-Yuan Liu

The wide application of wireless sensor networks (WSN) brings challenges to the maintenance of their security, integrity, and confidentiality. As an important active defense technology, intrusion detection plays an effective defense line for WSN. In view of the uniqueness of WSN, it is necessary to balance the tradeoff between reliable data transmission and limited sensor energy, as well as the conflict between the detection effect and the lack of network resources. This paper proposes a lightweight Intelligent Intrusion Detection Model for WSN. Combining k-nearest neighbor algorithm (kNN) and sine cosine algorithm (SCA) can significantly improve the classification accuracy and greatly reduce the false alarm rate, thereby intelligently detecting a variety of attacks including unknown attacks. In order to control the complexity of the model, the compact mechanism is applied to SCA (CSCA) to save the calculation time and space, and the polymorphic mutation (PM) strategy is used to compensate for the loss of optimization accuracy. The proposed PM-CSCA algorithm performs well in the benchmark functions test. In the simulation test based on NSL-KDD and UNSW-NB15 data sets, the designed intrusion detection algorithm achieved satisfactory results. In addition, the model can be deployed in an architecture based on cloud computing and fog computing to further improve the real-time, energy-saving, and efficiency of intrusion detection.


2018 ◽  
Vol 18 (5) ◽  
pp. 1971-1984 ◽  
Author(s):  
Ziwen Sun ◽  
Yimin Xu ◽  
Guangwei Liang ◽  
Zhiping Zhou

2010 ◽  
Vol 7 (2) ◽  
pp. 369-380
Author(s):  
M. Vasim Babu

Wireless Sensor Networks (WSNs) offer an excellent opportunity to monitor environments, and have a lot of interesting applications in warfare. Intrusion detection in Wireless Sensor Network (WSN) is of practical interest in many applications such as detecting intruder .The intrusion detection is defined as a mechanism for a WSN to detect the existence of inappropriate, incorrect, or anomalous moving attackers In this paper, I consider the cluster based architecture according to two WSN models: homogeneous and heterogeneous WSN. Furthermore, I derive the detection probability by considering two sensing models: single-sensing detection and multiple-sensing detection. In this Intrusion detection model we are going to track and detect Intrusion in a Homogenous and Heterogeneous Wireless Sensor Networks (WSN) using the intrusion distance and detection probability with various Tracking and Detection models.


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