Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.Efficient Estimation of Word Representations in Vector Space. Mikolov, T., Chen, K., Corrado, G., Dean, J.IEEE Computer Society Washington, DC, USA.
#Super vectorizer malware software
MALWARE '15 Proceedings of the 2015 10th International Conference on Malicious and Unwanted Software (MALWARE). Deep neural network based malware detection using two dimensional binary program features. Conference: ICEIS 2009 - Proceedings of the 11th International Conference on Enterprise Information Systems. N-grams-based File Signatures for Malware Detection. AISec '11 Proceedings of the 4th ACM workshop on Security and artificial intelligence. A comparative assessment of malware classification using binary texture analysis and dynamic analysis. Nataraj, L., Yegneswaran, V., Porras, P., Zhang, J.VizSec '11 Proceedings of the 8th International Symposium on Visualization for Cyber Security. Malware images: visualization and automatic classification. Nataraj, L., Karthikeyan, S., Jacob, G., Manjunath, B.
Latest Intelligence for August 2016 | Symantec Connect Community.IT threat evolution in Q1 2016 - Securelist.McAfee Labs Threats Report: November 2015.We think that new malware subspecies can be effectively detected by our method without existing methods for malware analysis such as generic method and dynamic heuristic method. We evaluate the accuracy of the classification by experiments. Moreover, malware subspecies are also classified into malware families by pre-learned k-nearest. Unknown execution files are classified into malware or benignware by pre-learned SVM. Support vector machine and classifier of k-nearest neighbor algorithm are used in our method, and the classifier learns paragraph vectors of information by static analysis. Paragraph vectors of information by static analysis of execution files are created by machine learning of PV-DBOW model for natural language processing.
#Super vectorizer malware code
Information of DLL Import, assembly code and hexdump are acquired by static analysis of execution files of malware and benignware to create feature vectors.
We combine static analysis of execution files with machine learning classifier and natural language processing by machine learning. The method can distinguish malware from benignware and it can also classify malware subspecies into malware families. In this paper, we propose a new method which automatically detects new malware subspecies by static analysis of execution files and machine learning. However, it is difficult to detect all new malware subspecies perfectly by existing methods. Malware can be detected by pattern matching method or dynamic heuristic method. Malware damages computers and the threat is a serious problem.
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