Impact Analysis of Malware Based on Call Network API with Heuristic Detection Method

Malware is a program that has a negative influence on computer systems that don\u27t have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements. Based on these conditions, the malware analysis is carried out using the API call network with the heuristic detection method. This aims to identify the behavior of malware that attacks the network. The results of the analysis carried out are that most malware is spyware, which is lurking user activity and retrieving user data without the user\u27s knowledge. In addition, there is also malware that is adware, which displays advertisements through pop-up windows on computer devices that interfaces with user activity. So that with these results, it can also be identified actions that can be taken by the user to protect his computer device, such as by installing antivirus or antimalware, not downloading unauthorized applications and not accessing unsafe websites. &nbsp

Semantic Network Analysis of Ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size

Social Network Analysis for Assessing Social Capital in Biosecurity Ecoliteracy

: Social Network Analysis for Assessing Social Capital in Biosecurity Ecoliteracy. Biosecurity ecoliteracy (BEL) is a view of literacy that applies ecological concepts to promote in-depth understanding, critical reflection, creative thinking, self consciousness, communication and social skills, in analyzing and managing issues around plant health/living, animal health/living and the risks that are associated with the environment. We used social network analysis (SNA) to evaluate two distinct forms of social capital of BEL: social cohesion and network structure. This study was executed by employing cooperative learning in BEL toward 30 undergraduate teacher training students. Data then was analyzed using UCINET software. We found the tendency of so­cial cohesion to increase after students participated in BEL. This was supported by several SNA measures (density, closeness and degree) and these values at the end were statistically different than at the beginning of BEL. The social structure map (sociogram) after BEL visualized that students were much more likely to cluster in groups compared with the sociogram before BEL. Thus BEL, through cooperative learning, was able to promote social capital. In addition SNA proved a useful tool for evaluating the achievement levels of social capital of BEL in the form of network cohesion and network structure

The International Trade Network: weighted network analysis and modelling

Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN.Comment: 5 pages, 5 figure