Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining

Huang, Jyun-Yao; Liao, I‐En; Chung, Yu-Fang; Chen, Kuen-Tzung

doi:10.1016/j.ins.2011.03.014

Cited by 48 publications

(21 citation statements)

References 13 publications

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“…The proposed IHIDS can achieve a high detection rate and low false positive rate and it also learns about new attacks on the IDS using ART [39]. Based on incorporating the learning mechanism in IDS, [31] has also proposed a new model that uses Markovian IDS to protect sensor nodes from attack. It integrates Anomaly based, Misuse based and game theory to prevent malicious attacks.…”

Section: Fig 3 Hybrid Based Idsmentioning

confidence: 99%

“…In the hybrid based IDS, a different scenario exists as the machine learning algorithms are the widely preferred methods, which includes ART, Q-learning, SVM, SLIPPER, CSOACN, etc. The proposed models incorporating machine learning methods are [26,28,29,31] and [33]. In papers [26,33], SVM is used for detecting intrusions and in [33], CSOACN is used along with SVM to provide a dual layer of intrusion detection.…”

Section: Comparisonmentioning

confidence: 99%

“…A combination of all the 3 techniques is used in [31] which comprises of statistical machine learning and optimization algorithms. In the hybrid based IDS, purely statistical based algorithms such as MLE and Neyman Pearson test are applied by [30].…”

Section: Comparisonmentioning

confidence: 99%

See 2 more Smart Citations

Intrusion Detection System in Wireless Sensor Networks: A Review

Ananthakumar¹,

Ganediwal²,

Kunte³

2015

ijacsa

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Abstract-The security of wireless sensor networks is a topic that has been studied extensively in the literature. The intrusion detection system is used to detect various attacks occurring on sensor nodes of Wireless Sensor Networks that are placed in various hostile environments. As many innovative and efficient models have emerged in the last decade in this area, we mainly focus our work on Intrusion detection Systems. This paper reviews various intrusion detection systems which can be broadly classified based on certain traditional techniques, namely signature based, anomaly based and hybrid based. The models proposed by various researchers have been critically examined based on certain classification parameters, such as detection rate, false alarm, algorithms used, etc. This work contains a summarization study of various intrusion detection systems used particularly in Wireless Sensor Networks, and also highlights their distinct features.

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Section: Fig 3 Hybrid Based Idsmentioning

confidence: 99%

Section: Comparisonmentioning

confidence: 99%

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Intrusion Detection System in Wireless Sensor Networks: A Review

Ananthakumar¹,

Ganediwal²,

Kunte³

2015

ijacsa

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“…The security of WSN for those crucial fields has been an important demand [4]. Since wireless sensor nodes usually are limited by power supply, computation capability and communication range [5], traditional encryption/decryption techniques that require an uninterrupted power supply to retain frequently key management and access control are unrealistic to be applied to WSN [6]. Thus, establishing an intrusion detection system (IDS) to meet the security requirements in WSN is essential.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Intrusion Detection Method for Wireless Sensor Networks

Qu¹,

Qiu²,

Tang³

et al. 2017

ijacsa

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Abstract-Current intrusion detection systems for WirelessSensor Networks (WSNs) which are usually designed to detect a specific form of intrusion or only applied for one specific type of network structure has apparently restrictions in facing various attacks and different network structures. To bridge this gap, based on the mechanism that attacks are much likely to be deviated from normal features and from different shapes of aggregations in feature space, we proposed a knowledge based intrusion detection strategy (KBIDS) to detect multiple forms of attacks over different network structure. We firstly, in the training stage, used a modified unsupervised mean shift clustering algorithm to discover clusters in network features. Then the discovered clusters were classified as an anomaly if they had a certain amount of deviation from the normal cluster captured at the initial stage where no attacks could occur at all. The training data combined with a weighted support vector machine were then used to build the decision function that was used to flag network behaviors. The decision function was updated periodically after training by merging newly added network features to adapt network variability as well as to achieve time efficiency. During network running, each node uniformly captured their status as feature vector at certain interval and forwarded them to the base station on which the model was deployed and run. Using this way, our model can work independently of network structure in both detection and deployment. The efficiency and adaptability of the proposed method have been tested and evaluated by simulation experiments deployed on QualNet. The simulations were conducted as a full-factorial experiment in which all combinations of three forms of attacks and two types of WSN structures were tested. Results demonstrated that the detection accuracy and network structure adaptability of the proposed method outperforms the state-of-the-art intrusion detection methods for WSN.

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“…In order to protect sensor nodes from malicious attacks, Huang et al proposed a new intrusion detection method. The method constructed Markov decision processes based on an attack pattern mining in order to predict future attack patterns and implement appropriate measures [26]. In order to explore, analyze, and extract useful information and knowledge from the larger number of personal data which came from smartphone and wearable devices, Muhammad et al proposed the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity [27].…”

Section: Introductionmentioning

confidence: 99%

Distributed Global Function Model Finding for Wireless Sensor Network Data

Deng

Yang

et al. 2016

Applied Sciences

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Abstract:Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.

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Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining

Cited by 48 publications

References 13 publications

Intrusion Detection System in Wireless Sensor Networks: A Review

Intrusion Detection System in Wireless Sensor Networks: A Review

An Adaptive Intrusion Detection Method for Wireless Sensor Networks

Distributed Global Function Model Finding for Wireless Sensor Network Data

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