Intrusion detection in wireless sensor network using genetic K-means algorithm

Sandhya, G.; Julian, Anitha

doi:10.1109/icaccct.2014.7019418

Cited by 12 publications

(4 citation statements)

References 7 publications

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“…The proposed IDM does not require dedicated hardware resources and presented negligible performance overhead with 10.45% increase in the power consumption and the feasibility of the proposed solution is verified for a WSN system, with the detection accuracy of 97.23%. Sandhya G et al (2014) present a conceptual framework for identifying attacks for intrusion detection by applying genetic K-means algorithm. The algorithm classifies instances to a pre-defined number of clusters.…”

Section: Wireless Sensor Network Intrusion Detection Systemsmentioning

confidence: 99%

Survey of Intrusion Detection Techniques and Architectures in Wireless Sensor Networks

Sharma¹,

Athavale²

2019

IJANA

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Advances in electronics and wireless communication technologies have enabled the development of large-scale wireless sensor networks (WSNs). However, WSNs practice from lots of constraints, with low computation capability, small memory, limited energy resources, vulnerability to physical capture, and the need of infrastructure, which enforce lonely security challenges mostly for the applications where confidentiality has key significance. There are diverse applications for wireless sensor networks, and security is very major issue for several of them. Before attackers can damage the WSN system (i.e., sensor nodes) and/or information destination (i.e., data sink or base station) in order to work WSNs in a secure way, any sort of intrusions should be detected.

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Section: Wireless Sensor Network Intrusion Detection Systemsmentioning

confidence: 99%

Survey of Intrusion Detection Techniques and Architectures in Wireless Sensor Networks

Sharma¹,

Athavale²

2019

IJANA

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“…Some proposed solutions deal with mobility of network nodes [47]. Some authors introduce different possible detection methods (based on data mining, machine learning, game theory, or genetic algorithms) which require adaptation for implementation into the IPv6-based WSN [48,49]. Most of the proposed solutions still focus on certain attack type and reside at particular network layer (usually the application layer) [50][51][52].…”

Section: Intrusion Detection In Wireless Sensor Networkmentioning

confidence: 99%

System for Malicious Node Detection in IPv6-Based Wireless Sensor Networks

2016

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The trend of implementing the IPv6 into wireless sensor networks (WSNs) has recently occurred as a consequence of a tendency of their integration with other types of IP-based networks. The paper deals with the security aspects of these IPv6-based WSNs. A brief analysis of security threats and attacks which are present in the IPv6-based WSN is given. The solution to an adaptive distributed system for malicious node detection in the IPv6-based WSN is proposed. The proposed intrusion detection system is based on distributed algorithms and a collective decision-making process. It introduces an innovative concept of probability estimation for malicious behaviour of sensor nodes. The proposed system is implemented and tested through several different scenarios in three different network topologies. Finally, the performed analysis showed that the proposed system is energy efficient and has a good capability to detect malicious nodes.

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“…These works were published in several papers. The exemplary research results were presented in [12], where authors described modifications of the anomaly based IDS exploiting genetic k-means algorithm. Authors have improved algorithm efficiency and increased attack detection rate compared to basic algorithm.…”

Section: Ids For Internet Of Thingsmentioning

confidence: 99%

A distributed IDS architecture model for Smart Home systems

et al. 2017

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The common use of smart devices encourages potential attackers to violate privacy. Sometimes taking control of one device allows the attacker to obtain secret data (such as password for home WiFi network) or tools to carry out DoS attack, and this, despite the limited resources of such devices. One of the solutions for gaining users' confidence is to assign responsibility for detecting attacks to the service provider, particularly Internet Service Provider (ISP). It is possible, since ISP often provides also the Home Gateway (HG)-device that has multiple roles: residential router, entertainment center, and home's "command and control" center which allows to manage the Smart Home entities. The ISP may extend this set of functionalities by implementing an intrusion detection software in HG provisioned to their customers. In this article we propose an Intrusion Detection System (IDS) distributed between devices residing at user's and ISP's premises. The Home Gateway IDS and the ISP's IDS constitute together a distributed structure which allows spreading computations related to attacks against Smart Home ecosystem. On the other hand, it also leverages the operator's knowledge of security incidents across the customer premises. This distributed structure is supported by the ISP's expert system that helps to detect distributed attacks i.e., using botnets.

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Intrusion detection in wireless sensor network using genetic K-means algorithm

Cited by 12 publications

References 7 publications

Survey of Intrusion Detection Techniques and Architectures in Wireless Sensor Networks

Survey of Intrusion Detection Techniques and Architectures in Wireless Sensor Networks

System for Malicious Node Detection in IPv6-Based Wireless Sensor Networks

A distributed IDS architecture model for Smart Home systems

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