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A multiple linear regression method was applied to predict real values of solvent accessibility from the sequence and evolutionary information. This method allowed us to obtain coefficients of regression and correlation between the occurrence of an amino-acid residue at a specific target and its sequence neighbor positions on the one hand, and the solvent accessibility of that residue on the other. Our linear regression model based on sequence information and evolutionary models was found to predict residue accessibility with 18.9% and 16.2% mean absolute error respectively, which is better than or comparable to the best available methods. A correlation matrix for several neighbor positions to examine the role of evolutionary information at these positions has been developed and analyzed. As expected, the effective frequency of hydrophobic residues at target positions shows a strong negative correlation with solvent accessibility, whereas the reverse is true for charged and polar residues. The correlation of solvent accessibility with effective frequencies at neighboring positions falls abruptly with distance from target residues. Longer protein chains have been found to be more accurately predicted than their smaller counterparts.

Capturing Fast-Flux Service Networks (FFSNs) by temporal variances is an intuitive way for seeking to identify rapid changes of DNS records. Unfortunately, the features regard to temporal variances would lead to the delay detection (more than one hour) of FFSN which could cause more damages, such as Botnet propagation and malware delivery. In this study, we proposed a delay-free detection system, Spatial Snapshot Fast-flux Detection system (SSFD), for identifying FFSN in real time and alleviating these potential damages. SSFD is capable to capture the geographical pattern of hosts as well as mapping IP addresses in a DNS response into geographic coordinate system for revealing FFSNs at the moment. The SSFD benefits from two novel spatial measures proposed in this study-spatial distribution estimation and spatial service relationship evaluation. These two measures consider the degree of uniform geographic distribution of infected hosts among FFSN composed of Bots, Content Distribution Network and general benign services. After that, Bayesian network classifier is applied to identify the FFSNs with the joint probability consideration against evading our proposed detection technique easily for attackers. Our experiment results indicate that the proposed SSFD system is more effective and efficient (within less than 0.5 second) with lower False Positive rate than flux-score based detection through one public dataset and two collected datasets.