Biblio

Despite widespread use of commercial anti-virus products, the number of malicious files detected on home and corporate computers continues to increase at a significant rate. Recently, anti-virus companies have started investing in machine learning solutions to augment signatures manually designed by analysts. A malicious file's determination is often represented as a hierarchical structure consisting of a type (e.g. Worm, Backdoor), a platform (e.g. Win32, Win64), a family (e.g. Rbot, Rugrat) and a family variant (e.g. A, B). While there has been substantial research in automated malware classification, the aforementioned hierarchical structure, which can provide additional information to the classification models, has been ignored. In this paper, we propose the novel idea and study the performance of employing hierarchical learning algorithms for automated classification of malicious files. To the best of our knowledge, this is the first research effort which incorporates the hierarchical structure of the malware label in its automated classification and in the security domain, in general. It is important to note that our method does not require any additional effort by analysts because they typically assign these hierarchical labels today. Our empirical results on a real world, industrial-scale malware dataset of 3.6 million files demonstrate that incorporation of the label hierarchy achieves a significant reduction of 33.1% in the binary error rate as compared to a non-hierarchical classifier which is traditionally used in such problems.

The behavior patterns in everyday life such as home, office, and commuting, and buying behavior model by day of the week, sea-son, location have hierarchies of various temporal granularity. Generally, in usual hierarchical data analysis, a basic hierarchical structure is given in advance. But it is difficult to estimate hierarchical structure beforehand for complex data. Therefore, in this study, we propose the algorithm to automatically extract both hierarchical structure and pattern from time series data using swarm intelligent method. We performed the initial operation test and confirmed that patterns can be extracted hierarchically.

With the rapid development of information technology, information security management is ever more important. OpenSSL security incident told us, there's distinct disadvantages of security management of current hierarchical structure, the software and hardware facilities are necessary to enforce security management on their implements of crucial basic protocols, in order to ease the security threats against the facilities in a certain extent. This article expounded cross-layer security management and enumerated 5 contributory factors for the core problems that management facing to.