A Dynamic Programming Model for Internal Attack Detection in Wireless Sensor Networks

Shi, Qi; Q, Li; Song, Lipeng; Zhang, Rongping; Jia, Yanfeng

doi:10.1155/2017/5743801

Cited by 9 publications

(3 citation statements)

References 35 publications

(33 reference statements)

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“…Younis have proposed a novel security attack known as snooze attack on cluster-based routing in WSN. Recently Qiong shi applied an the internal attack detection model as an epidemiological model (32). However most of these models ignore the game process between attackers and the IDS.…”

Section: Intrusion Detection Model Based On Game Theorymentioning

confidence: 99%

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Han

Zhou

Jia

et al. 2019

Information Sciences

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A Wireless Sensor Network (WSN) security strategy is imperative to counteract security threats, and energy consumption directly affects the network life-time of a wireless sensor; thus an attempt to exploit a low-consumption Intrusion Detection System (IDS) to detect malicious attacks makes a lot of sense. Current Intrusion Detection Systems can detect just some specific attacks and the network life time is reduced due to their high energy consumption. For the purpose of energy limitation and high efficiency, this paper proposes an intrusion detection model based on game theory and an autoregressive model. The paper not only improves the autoregressive theory model to a non-cooperative complete information static game model, but makes a prediction about attack pattern based on the reliable model. The proposed approach improves on previous approaches in two main ways: it takes energy consumption of the intrusion detection process into account, and it obtains the optimal defense strategy, balancing detection efficiency and the system's energy consumption by analyzing the model's mixed Nash equilibrium solution. In the simulation experiment, the running time of the process is regarded as the main indicator of energy consumption of the system. The simulation results show that the IDS proposed not only effectively predicts the attack time and the next targeted cluster based on the game theory, but also reduces energy consumption.

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Section: Intrusion Detection Model Based On Game Theorymentioning

confidence: 99%

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Han

Zhou

Jia

et al. 2019

Information Sciences

View full text Add to dashboard Cite

show abstract

“…When determining the attacker nodes, base station broadcasts blocked messages to the network. Shi et al [22] proposed a state transition model based on the continuous time Markov chain (CTMC), studied the sensor's behavior under internal attacks, and positioned attacks. The model linked the detection model for internal attack with the epidemiological model and considered the current state, viability, availability, and energy consumption of WSN to balance network features and security.…”

Section: Research On Abnormal Detectionmentioning

confidence: 99%

Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

Xiao

2019

Journal of Sensors

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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.

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“…The model elaborates the biological differentiation mechanism of dendritic cells [7]. Shi proposed a state transition model based on the continuous time Markov chain to study the behaviors of the sensors in a WSN under internal attack [8]. Yang started from the main idea of changes leading to danger,and established an adaptive danger signal extraction model based on finding changes [9].…”

Section: Introductionmentioning

confidence: 99%

An Intrusion Detection Model Based On Danger Theory for Wireless Sensor Networks

Sun²,

Wang

2018

Int. J. Onl. Eng.

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<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>

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A Dynamic Programming Model for Internal Attack Detection in Wireless Sensor Networks

Cited by 9 publications

References 35 publications

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Intrusion detection model of wireless sensor networks based on game theory and an autoregressive model

Intrusion Detection in Wireless Sensor Networks with an Improved NSA Based on Space Division

An Intrusion Detection Model Based On Danger Theory for Wireless Sensor Networks

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