Intrusion Detection in Smart Grid Using Data Mining Techniques
Intrusion Detection in Smart Grid Using Data Mining Techniques
复制标题
使用数据挖掘技术的智能电网入侵检测
DOI：
--
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
K. A. Rambo
中科院分区：
文献类型：
--
作者：
A. Subasi;Khloud Al;Reem Alghamdi;Aisha Kwairanga;S. Qaisar;Malak T. Al;K. A. Rambo
人口和工业化的快速增长催生了物联网（IoT）等技术的使用方式。信息和通信技术 (ICT) 的创新给我们的隐私期望和安全带来了许多挑战。在智能环境中，会使用安全设备、智能电器、传感器和电表。连接到物联网的设备数量的大幅增长推动了安全和隐私的新要求，这增加了人们对安全和隐私的担忧。对智能电网 (SG) 安全最普遍的威胁来自基础设施物理损坏、数据破坏、恶意软件、DoS 和入侵。入侵检测包括对信息的非法访问和对服务器可用性造成物理破坏的攻击。这项工作提出了一种使用数据挖掘技术的入侵检测系统，用于智能电网环境中的入侵检测。结果表明，所提出的随机森林方法的总分类精度为98.94%，F-measure为0.989，ROC曲线下面积（AUC）为0.999，kappa值为0.9865，优于其他分类方法。此外，通过比较其他分类技术（如 ANN、k-NN、SVM 和旋转森林），成功证明了我们方法的可行性。
The rapid growth of population and industrialization has given rise to the way for the use of technologies like the Internet of Things (IoT). Innovations in Information and Communication Technologies (ICT) carries with it many challenges to our privacy’s expectations and security. In Smart environments there are uses of security devices and smart appliances, sensors and energy meters. New requirements in security and privacy are driven by the massive growth of devices numbers that are connected to IoT which increases concerns in security and privacy. The most ubiquitous threats to the security of the smart grids (SG) ascended from infrastructural physical damages, destroying data, malwares, DoS, and intrusions. Intrusion detection comprehends illegitimate access to information and attacks which creates physical disruption in the availability of servers. This work proposes an intrusion detection system using data mining techniques for intrusion detection in smart grid environment. The results showed that the proposed random forest method with a total classification accuracy of 98.94 %, F-measure of 0.989, area under the ROC curve (AUC) of 0.999, and kappa value of 0.9865 outperforms over other classification methods. In addition, the feasibility of our method has been successfully demonstrated by comparing other classification techniques such as ANN, k-NN, SVM and Rotation Forest.