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Filters: Author is Aseeri, Mohammed  [Clear All Filters]
2017-09-05
Huang, Xu, Ahmed, Muhammad R., Rojas, Raul Fernandez, Cui, Hongyan, Aseeri, Mohammed.  2016.  Effective Algorithm for Protecting WSNs from Internal Attacks in Real-time. Proceedings of the Australasian Computer Science Week Multiconference. :40:1–40:7.

Wireless sensor networks (WSNs) are playing a vital role in collecting data about a natural or built environment. WSNs have attractive advantages such as low-cost, low maintains and flexible arrangements for applications. Wireless sensor network has been used for many different applications such as military implementations in a battlefield, an environmental monitoring, and multifunction in health sector. In order to ensure its functionality, especially in malicious environments, security mechanisms become essential. Especially internal attacks have gained prominence and pose most challenging threats to all WSNs. Although, a number of works have been done to discuss a WSN under the internal attacks it has gained little attention. For example, the conventional cryptographic technique does not give the appropriated security to save the network from internal attack that causes by abnormally behaviour at the legitimate nodes in a network. In this paper, we propose an effective algorithm to make an evaluation for detecting internal attack by multi-criteria in real time. This protecting is based on the combination of the multiple pieces of evidences collected from the nodes under an internal attacker in a network. A theory of the decision is carefully discussed based on the Dempster-Shafer Theory (DST). If you really wanted to make sure the designed network works exactly works as you expected, you will be benefited from this algorithm. The advantage of this proposed method is not just its performance in real-time but also it is effective as it does not need the knowledge about the normal or malicious node in advance with very high average accuracy that is close to 100%. It also can be used as one of maintaining tools for the regulations of the deployed WSNs.