A New Energy Prediction Approach for Intrusion Detection in Cluster-Based Wireless Sensor Networks

Shen, Wen; Han, Guangjie; Shu, Lei; Rodrigues, Joel J. P. C.; Chilamkurti, Naveen

doi:10.1007/978-3-642-33368-2_1

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…This step can be further improved on using a minimal amount of data prediction at the cluster head . Such an information is acquired from the cloud or the sink based on the history of the collected data, as per the work of Shen et al 41 Our suggested framework relies on random selections among the sensors of a cluster. Therefore, it is possible that we are not getting the minimal amount of data from the sensors and the compression is not optimized.…”

Section: Joint Wsn Designmentioning

confidence: 99%

On the joint design of compressed sensing and network coding for wireless communications

Taghouti

Waurick

Tömösközi

et al. 2019

Trans Emerging Tel Tech

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Compressed sensing and network coding techniques have seen a widespread interest in many disciplines during the last decade. Recently, a novel idea emerged for the combination of these areas in wireless communications to leverage the benefits from network coding while taking advantage of the correlations in the (sensory) data. The potential gains, such as lower latency for large‐scale sensing scenarios, reduced energy consumption, and a decrease in the amount of data during transmissions, are alluring to many use‐cases. However, a common issue one faces when joining both techniques is encountered in the fact that network coding is designed to operate over finite fields, whereas compressed sensing is mainly concerned with real numbers. This paper studies the impact of compressed sensing on network coding to enable one step decoding for the reconstruction of compressed data. We emphasize and discuss the design of the sensing and coding matrices, as well as the algorithms that enable accurate reconstructions. We employ the KL1p compressed sensing library and the NS3 simulator to evaluate this joint design. Our simulations show that using normalized coefficient matrices drawn from Gaussian distributions has higher efficiency and scalability including, but not limited to, multi‐hop networks, where the recoding feature of network coding can be exploited. Furthermore, the Subspace Pursuit algorithm outperforms the state‐of‐the‐art reconstruction algorithms, with respect to the reconstruction signal‐to‐noise ratio, by more than two folds compared to the other benchmark algorithms in cluster‐based Wireless Sensor Networks, where recoding using real network codes are involved.

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Section: Joint Wsn Designmentioning

confidence: 99%

On the joint design of compressed sensing and network coding for wireless communications

Taghouti

Waurick

Tömösközi

et al. 2019

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…Bao et al [35] proposed a trust based IDS by using statistical methods for WSNs. Shen et al [36] utilized node energy as the main parameter to detect the intrusion. Recently, Hidoussi et al [37] proposed centralized IDS that detect intruders based on misuse of the network activities by nodes.…”

Section: Related Workmentioning

confidence: 99%

Trust Aware Data Aggregation And Intrusion Detection System For Wireless Sensor Networks

Vamsi

Kant

2016

International Journal on Smart Sensing and Intelligent Systems

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Data Aggregation (DA) is one of the most frequently used techniques in Wireless Sensor Networks (WSNs) to improve the network lifetime. It involves gathering, consolidating, and routing the sensory data collected by sensor nodes. However, research studies have demonstrated that the dependability of the DA process affects severely when malicious nodes are present in the network. Many security solutions using cryptography and Intrusion Detection System (IDS) have been proposed in the literature for smooth conduction of the aggregation process. However, these solutions require more energy and processing capacity, which are the main constraints for sensor nodes. To this end, the current study presents Trust-Aware Data Aggregation and Intrusion Detection System (TDAGIDS) for clustered WSNs. This TDAGIDS has two modules viz., trust management and intrusion detection. Each node runs a Trust Management System (TMS) that computes the trust value of neighbor nodes by observing their activities. On the other hand, the Base Station (BS) is equipped with IDS that collects the trust values of network nodes and runs a statistical test to identify malicious nodes. In this way, TMS at the node level and IDS at BS level work in collaboration to detect and isolate the malicious nodes from the DA process. The simulation results show the effectiveness of the TDAGIDS over baseline T-LEACH (Trusted Low Energy Adaptive Clustering Hierarchy) protocol and recently proposed TDAGIOT (Trust based Data Aggregation for Internet of Things) method.

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“…A scheme to detect malicious nodes based on energy prediction is proposed in [27]. Most schemes use node interactions or traffic profiles to detect an intrusion.…”

Section: Prediction and Intrusion Detectionmentioning

confidence: 99%

Application of Machine-Learning Based Prediction Techniques in Wireless Networks

Bhutani¹

2014

IJCNS

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Wireless networks are key enablers of ubiquitous communication. With the evolution of networking technologies and the need for these to inter-operate and dynamically adapt to user requirements, intelligent networks are the need of the hour. Use of machine learning techniques allows these networks to adapt to changing environments and enables them to make decisions while continuing to learn about their environment. In this paper, we survey the various problems of wireless networks that have been solved using machine-learning based prediction techniques and identify additional problems to which prediction can be applied. We also look at the gaps in the research done in this area till date.

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A New Energy Prediction Approach for Intrusion Detection in Cluster-Based Wireless Sensor Networks

Cited by 11 publications

References 10 publications

On the joint design of compressed sensing and network coding for wireless communications

On the joint design of compressed sensing and network coding for wireless communications

Trust Aware Data Aggregation And Intrusion Detection System For Wireless Sensor Networks

Application of Machine-Learning Based Prediction Techniques in Wireless Networks

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