Weakly supervised deep learning for the detection of domain generation algorithms

Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster. DGAs dynamically and consistently generate large volumes of malicious domain names, only a few of which are registered by the botmaster, within a short time window around their generation time, and subsequently resolved when the malware on the infected machine tries to access them. Deep neural networks that can classify domain names as benign or malicious are of great interest in the real-time defense against DGAs. In contrast with traditional machine learning models, deep networks do not rely on human engineered features. Instead, they can learn features automatically from data, provided that they are supplied with sufficiently large amounts of suitable training data. Obtaining cleanly labeled ground truth data is difficult and time consuming. Heuristically labeled data could potentially provide a source of training data for weakly supervised training of DGA detectors. We propose a set of heuristics for automatically labeling domain names monitored in real traffic, and then train and evaluate classifiers with the proposed heuristically labeled dataset. We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers, and that these deep neural networks significantly outperform a random forest classifier with human engineered features

Solving Solar Neutrino Puzzle via LMA MSW Conversion

We analyze the existing solar neutrino experiment data and show the allowed regions. The result from SNO's salt phase itself restricts quite a lot the allowed region's area. Reactor neutrinos play an important role in determining oscillation parameters. KamLAND gives decisive conclusion on the solution to the solar neutrino puzzle, in particular, the spectral distortion in the 766.3 Ty KamLAND data gives another new improvement in the constraint of solar MSW-LMA solutions. We confirm that at 99.73% C.L. the high-LMA solution is excluded.Comment: 6 eps figure

Electrophysiological Correlates of Reading the Single- and Interactive-Mind

Understanding minds is the cognitive basis of successful social interaction. In everyday life, human mental activity often happens at the moment of social interaction among two or multiple persons instead of only one-person. Understanding the interactive mind of two- or multi-person is more complex and higher than understanding the single-person mind in the hierarchical structure of theory of mind. Understanding the interactive mind maybe differentiate from understanding the single mind. In order to examine the dissociative electrophysiological correlates of reading the single mind and reading the interactive mind, the 64 channels event-related potentials were recorded while 16 normal adults were observing three kinds of Chinese idioms depicted physical scenes, one-person with mental activity, and two- or multi-person with mental interaction. After the equivalent N400, in the 500- to 700-ms epoch, the mean amplitudes of late positive component (LPC) over frontal for reading the single mind and reading the interactive mind were significantly more positive than for physical representation, while there was no difference between the former two. In the 700- to 800-ms epoch, the mean amplitudes of LPC over frontal–central for reading the interactive mind were more positive than for reading the single mind and physical representation, while there was no difference between the latter two. The present study provides electrophysiological signature of the dissociations between reading the single mind and reading the interactive mind