Abstract
Inspired by the biological immune system, many researchers apply artificial immune principles to intrusion detection in wireless sensor networks, such as negative selection algorithms, danger theory, and dendritic cell algorithms. When applying the negative selection algorithm to wireless sensor networks, the characteristics of wireless sensor networks, such as frequent changes in network topology and limited resources, are not considered too much, which makes the detection effect to need improvement. In this paper, a negative selection algorithm based on spatial partition is proposed and applied to hierarchical wireless sensor networks. The algorithm first analyzes the distribution of self-set in the real-valued space then divides the real-valued space, and several subspaces are obtained. Selves are filled into different subspaces. We implement the negative selection algorithm in the subspace. The randomly generated candidate detector only needs to be tolerated with selves in the subspace where the detector is located, not all the selves. This operation reduces the time cost of distance calculation. In the detection process of detectors, the antigen which is to be detected only needs to match the mature detectors in the subspace where the antigen is located, rather than all the detectors. This operation speeds up the antigen detection process. Theoretical analysis and experimental results show that the algorithm has better time efficiency and quality of detectors, saves sensor node resources and reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.
Highlights
With the progress and development of wireless communication, the microcomputer electrical system, microelectronics, signal processing, computer network and other technologies, and wireless sensor networks (WSN) with intelligent characteristics emerge [1]
Inspired by a negative selection algorithm in the biological immune system, this paper proposes a wireless sensor network intrusion detection model based on the spatial division negative selection algorithm (SD-real-valued negation selection algorithm (RNSA))
This paper proposes two methods based on support vector machines, namely, centered hyperellipsoidal support vector machine (CESVM) and quartersphere support vector machine (QSSVM)
Summary
With the progress and development of wireless communication, the microcomputer electrical system, microelectronics, signal processing, computer network and other technologies, and wireless sensor networks (WSN) with intelligent characteristics emerge [1]. We should select or design the appropriate algorithm according to requirements of the network (3) Wireless sensor networks have limited resources, including storage space, computing power, bandwidth, and energy [2,3,4,5]. The current wireless sensor network adopts the communication technology of low speed and low power consumption, and the node has the characteristic of having limited energy. Inspired by a negative selection algorithm in the biological immune system, this paper proposes a wireless sensor network intrusion detection model based on the spatial division negative selection algorithm (SD-RNSA). The performance of the model is analyzed in theory; experimental results show that the model has better time efficiency and detector quality, saves sensor node resources, reduces the energy consumption, and is an effective algorithm for wireless sensor network intrusion detection.
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