A cryptographic checksum for integrity protection
Computers and Security
An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Inductive learning algorithms and representations for text categorization
Proceedings of the seventh international conference on Information and knowledge management
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Study of Approaches to Hypertext Categorization
Journal of Intelligent Information Systems
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
MEF: Malicious Email Filter - A UNIX Mail Filter That Detects Malicious Windows Executables
Proceedings of the FREENIX Track: 2001 USENIX Annual Technical Conference
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Learning to detect malicious executables in the wild
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Detection of unknown computer worms based on behavioral classification of the host
Computational Statistics & Data Analysis
Improving malware detection by applying multi-inducer ensemble
Computational Statistics & Data Analysis
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
A layered classification for malicious function identification and malware detection
Concurrency and Computation: Practice & Experience
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Malicious executables, often spread as email attachments, impose serious security threats to computer systems and associated networks. We investigated the use of byte sequence frequencies as a way to automatically distinguish malicious from benign executables without actually executing them. In a series of experiments, we compared classification accuracies over seven feature selection methods, four classification algorithms, and variable byte sequence lengths. We found that single-byte patterns provided surprisingly reliable features to separate malicious executables from benign. Between classifiers and feature selection methods, the overall performance of the models depended more on the choice of classifier than the method of feature selection. Support vector machine (SVM) classifiers were found to be superior in terms of prediction accuracy, training time, and aversion to overfitting.