Online social network (OSN) discussion groups are exerting significant effects on political dialogue. In the absence of access control mechanisms, any user can contribute to any OSN thread. Individuals can exploit this characteristic to execute targeted attacks, which increases the potential for subsequent malicious behaviors such as phishing and malware distribution. These kinds of actions will also disrupt bridges among the media, politicians, and their constituencies.For the concern of Security Management, blending malicious cyberattacks with online social interactions has introduced a brand new challenge. In this paper we describe our proposal for a novel approach to studying and understanding the strategies that attackers use to spread malicious URLs across Facebook discussion groups. We define and analyze problems tied to predicting the potential for attacks focused on threads created by news media organizations. We use a mix of macro static features and the micro dynamic evolution of posts and threads to identify likely targets with greater than 90% accuracy. One of our secondary goals is to make such predictions within a short (10 minute) time frame. It is our hope that the data and analyses presented in this paper will support a better understanding of attacker strategies and footprints, thereby developing new system management methodologies in handing cyber attacks on social networks.
Abstract-The Deep Belief Network (DBN) is one of the major algorithms of deep learning. It simulates human brain to extract the features efficiently, so that the model has much strong learning ability. Because it is difficult to extract features from a variety of seismic data effectively, multiple sampling points of seismic data are used as inputs. Then we use DBN to extract the features from seismic data, which can be stacked by RBMs layerby-layer. The model of lithological recognition can be constructed from previous step, further to recognize stratum lithology. By experiments and practical application, it is proved that partial strata information can be utilized effectively when multiple sampling points of seismic data are used as inputs. In this way, we can effectively recognize the stratum lithology based on DBN.
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