SKWN: Smart and dynamic key management scheme for wireless sensor networks

被引:8
|
作者
Mesmoudi, Samira [1 ]
Benadda, Belkacem [2 ]
Mesmoudi, Amin [3 ]
机构
[1] Univ Abou Bekr Belkaid Tlemcen, Lab Syst & Technol Informat & Commun, Tilimsen, Algeria
[2] Abou Bekr Belkaid Univ Tlemcen, Fac Technol, Tilimsen, Algeria
[3] Univ Poitiers, Lab Comp Sci & Automat Control Syst, Poitiers, France
关键词
hierarchical wireless sensor network; IoT; key management; machine learning; resources usage optimization; security; INTRUSION DETECTION SYSTEMS; ESTABLISHMENT SCHEME; ENERGY-EFFICIENT;
D O I
10.1002/dac.3930
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In the era of the Internet of Things (IoT), we are witnessing to an unprecedented data production because of the massive deployment of wireless sensor networks (WSNs). Typically, a network of several hundred sensors is created to ensure the interactions between the cyber world and the physical world. Unfortunately, the intensive use of this kind of networks has raised several security issues. Indeed, many WSN-based applications require secure communication in order to protect collected data. This security is generally ensured by encryption of communication between sensors, which requires the establishment of many cryptographic keys. Managing these keys, within a protocol, is an important task that guarantees the effectiveness of the security mechanism. The protocol should be intelligently adaptable not only to intrusion events but also to the security level needed by some applications. An efficient protocol optimizes also sensors energy and consequently increases the network life cycle. In this paper, we propose, a smart and dynamic key management scheme for hierarchical wireless sensor networks (SKWN). Our protocol offers three subschemes to deal with key establishment, key renewal, and new node integration. Regarding existing schemes, SKWN does not only provide reliable security mechanisms, but it also optimizes energy consumption and overheads related to the communication and memory usage. Furthermore, our approach relies on a machine learning approach to monitor the state of the network and decide the appropriate security level. We provide a formal approach and its implementation, together with simulations allowing to compare resources usage with respect to existing approaches.
引用
收藏
页数:23
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