Events about entities have been widely collected on Web, allowing us to analyze how peer entities interact and learn the relationships that exist among the entities. In this paper we investigate similar traces that have not been adequately studied so far. Intuitively, peer entities tend to have similar traces. The challenges in mining similar traces are: (1) the occurring time lags of traces are usually unknown and varying; (2) the existence of large-scale events of entities and complexity of the model representing all the events. In this paper we propose a simple, but practical method that addresses all these challenges. Firstly, sliding windows are adopted to filter out the significant events and then find the candidate topic sequences. Secondly, dynamic programming is employed to mine similar candidate topic sequences of entities. Finally, an efficient method is proposed to mine all the similar traces of entities. It is able to mine similar traces of peer entities with high accuracy. We conduct comprehensive experiments on synthetic datasets to demonstrate the efficiency of the method proposed.
In this paper, we use cognitive computing to build a WSN security threat analysis model using a data-driven approach and conduct an in-depth and systematic study. In this paper, we develop a simulation platform (OMNeT++-based WSN Security Protocol Simulation Platform (WSPSim)) based on OMNeT++ to make up for the shortcomings of current WSN simulation platforms, improve the simulation capability of WSN security protocols, and provide a new technical means for designing and verifying security protocols. The WSPSim simulation platform is used to simulate and analyze typical WSN protocols and verify the effectiveness of the platform. In this paper, we mainly analyze the node malicious behavior by listening and judging the communication behavior of the nodes, and the current trust assessment is given by the security management nodes. When the security management node is rotated, its stored trust value is used as historical trust assessment and current trust assessment together to participate in the integrated trust value calculation, which improves the reliability of node trust assessment; to increase the security and reliability of the management node, a trust value factor and residual energy factor are introduced in the security management node election in the paper. According to the time of management node election, the weights of both are changed to optimize the election. Using the WSPSim simulation platform, a typical WSN protocol is simulated and analyzed to verify the effectiveness of the platform. In this paper, the simulation results of the LEACH protocol with an MD5 hash algorithm and trust evaluation mechanism and typical LEACH protocol as simulation samples are compared; i.e., the correctness of the simulation platform is verified, and it is shown that improving the security of the protocol and enhancing the security and energy efficiency of wireless sensor networks provide an effective solution.
A trace of an entity is a behavior trajectory of the entity. Periodicity is a frequent phenomenon for the traces of an entity. Finding periodic traces for an entity is essential to understanding the entity behaviors. However, mining periodic traces is of complexity procedure, involving the unfixed period of a trace, the existence of multiple periodic traces, the large-scale events of an entity and the complexity of the model to represent all the events. However, the existing methods can't offer the desirable efficiency for periodic traces mining. In this paper, Firstly, a graph model(an event relationship graph) is adopted to represent all the events about an entity, then a novel and efficient algorithm, TracesMining, is proposed to mine all the periodic traces. In our algorithm, firstly, the cluster analysis method is adopted according to the similarity of the activity attribute of an event and each cluster gets a different label, and secondly a novel method is proposed to mine all the Star patterns from the event relationship graph. Finally, an efficient method is proposed to merge all the Stars to get all the periodic traces. High efficiency is achieved by our algorithm through deviating from the existing edge-by-edge pattern-growth framework and reducing the heavy cost of the calculation of the support of a pattern and avoiding the production of lots of redundant patterns. In addition, our algorithm could mine all the large periodic traces and most small periodic traces. Extensive experimental studies on synthetic data sets demonstrate the effectiveness of our method.
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