There have been several recent reports that botnet communication between bot-infected computers and Command and Control servers (C&C servers) using the Domain Name System (DNS) protocol has been used by many cyber attackers. In particular, botnet communication based on the DNS TXT record type has been observed in several kinds of botnet attack. Unfortunately, the DNS TXT record type has many forms of legitimate usage, such as hostname description. In this paper, in order to detect and block out botnet communication based on the DNS TXT record type, we first differentiate between legitimate and suspicious usages of the DNS TXT record type and then analyze real DNS TXT query data obtained from our campus network. We divide DNS queries sent out from an organization into three types-via-resolver, and indirect and direct outbound queriesand analyze the DNS TXT query data separately. We use a 99-day dataset for via-resolver DNS TXT queries and an 87-day dataset for indirect and direct outbound DNS TXT queries. The results of our analysis show that about 30%, 8% and 19% of DNS TXT queries in via-resolver, indirect and direct outbound queries, respectively, could be identified as suspicious DNS traffic. Based on our analysis, we also consider a comprehensive botnet detection system and have designed a prototype system.

The clustering algorithm plays a very important role in the applications of medical analysis, it can effective analysis the log of disease. It can accurately analyze the characteristics of various diseases, thus providing accurate basis for the doctor's diagnosis. In this paper, we will analysis the cluster algorithm--Fuzzy C-Means Algorithm (FCM). The traditional FCM is liable to trap into the problem of local optimum. We propose an improved algorithm of FCM based on the smooth technology. It will consider the sample points in different positions have different effects on cluster and cluster number has a great influence on the clustering results, so the new algorithm combines the point density and the method of determining the optimal number of clusters, finally use the effective evaluation function to evaluate the effective of the algorithm. Finally, we will use the case of Parkinson's disease to do the experimental verification, the results showed that the new clustering algorithms have better clustering effect, and it can more accurate analysis the characteristics of the disease, and it is using in some applications

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-Spoof which comprised of 625,537 pictures of 10,177 subjects has been released. It is the largest face anti-spoofing dataset in terms of the numbers of the data and the subjects. This paper reports methods and results in the CelebA-Spoof Challenge 2020 on Face Anti-Spoofing which employs the CelebA-Spoof dataset. The model evaluation is conducted online on the hidden test set. A total of 134 participants registered for the competition, and 19 teams made valid submissions. We will analyze the top ranked solutions and present some discussion on future work directions.

magnet (FSPM) linear motor are analyzed and compared with those of the conventional FSPM linear motor. Optimal parameters of both stator and mover teeth are further discussed focusing on thrust ripple and thrust force. Finally, performance comparisons between two FSPM linear motors with different end shapes are made. It is found that the structure with two-tooth end is more desirable. It shows that the FSPM linear motor with multi-tooth structure can not only decrease half number of permanent magnets, but also output high thrust force at low current density with small thrust ripple.