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In this study, we examined the impact of college students' mental health on their social behavior. A social network was identified based on the behavior of college students sharing a meal. We analyzed the impact of depression on the structure of this network and found that students without obvious depressive symptoms, based on the test data of the SCL-90 Assessment Scale, were better at socializing than students with obvious depressive symptoms. We proposed a public opinion spreading model on social networks and formulated a heterogeneous mean-field theory to describe it. Further, using computer simulation experiments, we studied the impact of students' mental health on the process of information propagation in college. The results of the experiments showed that students without obvious depressive symptoms were more likely to receive information than students with obvious depressive symptoms. Based on the results of our study, college psychological consultants can actively identify students who may be at risk of mental illness and give them attention and guidance. [ABSTRACT FROM AUTHOR]

Authentication protocol verification is a difficult problem. The problem of "state space explosion" has always been inevitable in the field of verification. Using inductive characteristics, we combine mathematical induction and model detection technology to solve the problem of "state space explosion" in verifying the OSK protocol and VOSK protocol of RFID system. In this paper, the security and privacy of protocols in RFID systems are studied and analysed to verify the effectiveness of the combination of mathematical induction and model detection. We design a (r,s,t)-security experiment on the basis of privacy experiments in the RFID system according to the IND-CPA security standard in cryptography, using mathematical induction to validate the OSK protocol and VOSK protocol. Finally, the following conclusions are presented. The OSK protocol cannot resist denial of service attacks or replay attacks. The VOSK protocol cannot resist denial of service attacks but can resist replay attacks. When there is no limit on communication, the OSK protocol and VOSK protocol possess (r,s,t)-privacy; that is to say they can resist denial of service attacks. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threat (APT) is a serious threat against sensitive information. Current detection approaches are time-consuming since they detect APT attack by in-depth analysis of massive amounts of data after data breaches. Specifically, APT attackers make use of DNS to locate their command and control (C&C) servers and victims’ machines. In this paper, we propose an efficient approach to detect APT malware C&C domain with high accuracy by analyzing DNS logs. We first extract 15 features from DNS logs of mobile devices. According to Alexa ranking and the VirusTotal’s judgement result, we give each domain a score. Then, we select the most normal domains by the score metric. Finally, we utilize our anomaly detection algorithm, called Global Abnormal Forest (GAF), to identify malware C&C domains. We conduct a performance analysis to demonstrate that our approach is more efficient than other existing works in terms of calculation efficiency and recognition accuracy. Compared with Local Outlier Factor (LOF), k-Nearest Neighbor (KNN), and Isolation Forest (iForest), our approach obtains more than 99% F-M and R for the detection of C&C domains. Our approach not only can reduce data volume that needs to be recorded and analyzed but also can be applicable to unsupervised learning. [ABSTRACT FROM AUTHOR]