Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Learning to detect malicious executables in the wild
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data
IEEE Transactions on Knowledge and Data Engineering
IMDS: intelligent malware detection system
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Subspace clustering of text documents with feature weighting k-means algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
Recently, automated malware (e.g., viruses, backdoors, spyware, Trojans and worms) categorization methods and an industry-wide naming convention have been the computer security topics that are of great interest. Resting on the analysis of function based instruction sequence, we develop an intelligent instruction sequence based malware categorization system (ISMCS) using a novel weighted subspace clustering method. ISMCS is an integrated system consisting of three major modules: feature exactor, malware categorizer using weighted subspace clustering method and malware signature generator. ISMCS can not only effectively categorize malwares to different families, but also automatically generate the unify signature for every family. Promising experimental results demonstrate that the effectiveness of our ISMCS system outperform other existing malware categorization methods, such as K-Means and hierarchical clustering algorithms.