A New Scalable Botnet Detection Method in the Frequency Domain

Author(s):  
Giovanni Bottazzi ◽  
Giuseppe F. Italiano ◽  
Giuseppe G. Rutigliano
Author(s):  
Maria Mesimeri ◽  
Kristine L. Pankow ◽  
James Rutledge

ABSTRACT We propose a new frequency-domain-based algorithm for detecting small-magnitude seismic events using dense surface seismic arrays. Our proposed method takes advantage of the high energy carried by S waves, and approximate known source locations, which are used to rotate the horizontal components to obtain the maximum amplitude. By surrounding the known source area with surface geophones, we achieve a favorable geometry for locating the detected seismic events with the backprojection method. To test our new detection method, we used a dense circular array, consisting of 151 5 Hz three-component geophones, over a 5 km aperture that was in operation at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) in southcentral Utah. We apply the new detection method during a small-scale test injection phase at FORGE, and during an aftershock sequence of an Mw 4.1 earthquake located ∼30  km north of the geophone array, within the Black Rock volcanic field. We are able to detect and locate microseismic events (Mw<0) during injections, despite the high level of anthropogenic activity, and several aftershocks that are missing from the regional catalog. By comparing our method with known algorithms that operate both in the time and frequency domain, we show that our proposed method performs better in the case of the FORGE injection monitoring, and equally well for the off-array aftershock sequence. Our new method has the potential to improve microseismic event detections even in extremely noisy environments, and the proposed location scheme serves as a direct discriminant between true and false detections.


2020 ◽  
Vol 49 (3) ◽  
pp. 330002-330002
Author(s):  
陈朋 Peng CHEN ◽  
赵智 Zhi ZHAO ◽  
赵冬冬 Dong-dong ZHAO ◽  
韩洋洋 Yang-yang HAN ◽  
梁荣华 Rong-hua LIANG

Author(s):  
Yingchun Guo ◽  
Yanhong Feng ◽  
Gang Yan ◽  
Shuo Shi

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.


Author(s):  
K. Manoj Kumar ◽  
P. J. Sijomon ◽  
K. Shamju Joseph ◽  
D. M. Premod ◽  
V. S. Shenoi ◽  
...  

Author(s):  
Wei Ma ◽  
Xing Wang ◽  
Jiguang Wang ◽  
Qianyun Chen

Botnet is a serious threat for the Internet and it has created great damage to the Internet. How to detect botnet has become an ongoing endeavor research. Series of methods have been discussed in recent research. However, one of the remaining challenges is that the high computational overhead. In this paper, a lightweight hybrid botnet detection method is proposed. Considering the features in the botnet data packets and the characteristic of employing DGA (Domain Generation Algorithm) domain names to connect to the botnet, two sensors are designed and deployed individually and parallelly. Signature detection is used on the gateway sensor to dig out known bot software and deep learning based techniques are used on the DNS (Domain Name Server) server sensor to find DGA domain names. With this method, the computational overhead would be shared by the two sensors and experiments are conducted and the results indicate that the method is effective in detecting botnet


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