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Log analysis is an efficiency way to detect threats by scrutinizing the events recorded by the operating systems and devices. However, it is more and more difficult to discover threats accurately due to the massive amount of logs and their various formats. Focusing on this problem, the authors propose a method for potential threats mining based on the correlation analysis of multi-type logs. Firstly, they extract 12 features, including behavior-related, attribute-related and measurable features, from multi-type logs based on the characteristics of known and potential attacks. They also propose normalization method to deal with these heterogeneous features. Secondly, focusing on solving the problem that analyzing a single type of log can only detect some specific attacks, they employ the logistic regression model to perform correlation analysis on multi-type logs. Finally, they construct an anomaly detection platform integrated with parallel processing mechanism to process the massive records. The experimental results based on logs collected show that the proposed method has high detection accuracy and low computational complexity, which can be applied to mine potential threats and abnormal users from the massive logs in an actual network environment.

We report on the study of moving filaments in a honeycomb pattern in a dielectric barrier discharge system using photomultipliers, a high-speed video camera, and a spectrometer. The honeycomb pattern bifurcates from the hexagonal super-lattice pattern with increasing voltage. It is found that the honeycomb framework is composed of filaments with irregular reciprocating motion, which indicates that the honeycomb framework results from statistical self-organization. The spatiotemporal dynamics show that the pattern consists of three different sub-lattices. The plasma parameters (molecular vibrational temperature and electron density) of the pattern, determined from the optical emission spectra, show that different sub-lattices are in different plasma states. Based on these measurements, the mechanism of the movement of filaments is analyzed briefly.

A novel type of white-eye pattern in a dielectric barrier discharge system has been investigated in this paper. It is a superposition of a hexagonal lattice and a white-eye stripe in appearance and evolves from a white-eye square grid state with the applied voltage increasing. Its spatio-temporal dynamics obtained by an intensified charge-coupled device shows that it consists of three transient rectangular sublattices. The spatiotemporally resolved evolutions of the molecular vibrational temperature and electron density of the pattern are measured by optical emission spectra. The evolution of surface charge distribution is given and its effect on the self-organized pattern formation is discussed.

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