物联网设备数量庞大,分布广泛,防护能力较弱,容易受到恶意攻击。同时,攻击者可以通过俘获大量物联网终端设备发起海量流量攻击。针对上述问题,文章提出一种基于聚类+分类的物联网恶意攻击检测方法。首先,对物联网流量数据进行预处理,采...物联网设备数量庞大,分布广泛,防护能力较弱,容易受到恶意攻击。同时,攻击者可以通过俘获大量物联网终端设备发起海量流量攻击。针对上述问题,文章提出一种基于聚类+分类的物联网恶意攻击检测方法。首先,对物联网流量数据进行预处理,采用随机森林进行特征重要性评估,并采用主成分分析法进行部分特征降维;然后,采用改进的k-means算法对流量预处理结果进行攻击聚类,对不同的攻击簇,基于CART决策树实现攻击分类。文章基于物联网恶意攻击数据集Bot-IoT和网络攻击数据集KDD CUP 99进行实验验证,结果表明,文章方法具有良好的攻击检测效果,尤其可有效提升低频攻击的检测准确率。展开更多
The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained m...The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained monitoring. Large-scale WSNs （Wireless Sensor Networks） have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of ＇WSNs in power grid＇ become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects＇ degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results.Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node＇s dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.展开更多
文摘物联网设备数量庞大,分布广泛,防护能力较弱,容易受到恶意攻击。同时,攻击者可以通过俘获大量物联网终端设备发起海量流量攻击。针对上述问题,文章提出一种基于聚类+分类的物联网恶意攻击检测方法。首先,对物联网流量数据进行预处理,采用随机森林进行特征重要性评估,并采用主成分分析法进行部分特征降维;然后,采用改进的k-means算法对流量预处理结果进行攻击聚类,对不同的攻击簇,基于CART决策树实现攻击分类。文章基于物联网恶意攻击数据集Bot-IoT和网络攻击数据集KDD CUP 99进行实验验证,结果表明,文章方法具有良好的攻击检测效果,尤其可有效提升低频攻击的检测准确率。
文摘The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained monitoring. Large-scale WSNs （Wireless Sensor Networks） have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of ＇WSNs in power grid＇ become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects＇ degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results.Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node＇s dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.
