Security analysis of secure data aggregation protocols in wireless sensor networks

Rahayu, Triana Mugia; Lee, Sang-Gon; Lee, Hoon Jae

doi:10.1109/icact.2014.6779005

Cited by 6 publications

(2 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has the highest detection rate, and at the same time it has the highest false positive rate. The misuse detection identifies the various types of attackers by equating or comparing the present attack behavior and the past attack behavior [9]. It has the highest accuracy but with low detection rate.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

Singh

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS) that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack). For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

show abstract

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

Singh

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…However, most of these reviews evaluated the secure clustering algorithms based on two main processes only, i.e., CH selection and cluster formation [10], [12], [13]. Other works discussed the existing secure routing protocols of clustering model with the aim of protecting the data transmission CHs and the base station [8], [14]- [16]. But for any secure clustering algorithm, a set of criteria must be used to be an effective one.…”

Section: Introductionmentioning

confidence: 99%

Recent Advances of Secure Clustering Protocols in Wireless Sensor Networks

2014

Int. j. comput. netw. commun. secur.

View full text Add to dashboard Cite

Wireless Sensor Networks (WSNs) have been employed in many real-world applications that greatly improve our life. The ubiquitous WSNs make security a prime issue, and new technologies have been developed recently. In this article, we review the most recent secure clustering protocols in WSNs. We start with a description of the security requirements for WSNs and discuss the existing security schemes. We analyze to what extend they have been applied to the clustering structure of WSNs. Then, we review secure clustering protocols in emerged recent years. Finally, we present a set of criteria which must be applied to build a secure clustering algorithm.

show abstract