SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods
暂无分享,去创建一个
Lior Rokach | Yuval Elovici | Nir Nissim | Aviad Cohen | L. Rokach | Y. Elovici | N. Nissim | Aviad Cohen
[1] Xun Lu,et al. De-obfuscation and Detection of Malicious PDF Files with High Accuracy , 2013, 2013 46th Hawaii International Conference on System Sciences.
[2] Yuval Shahar,et al. An Active Learning Framework for Efficient Condition Severity Classification , 2015, AIME.
[3] Himanshu Pareek,et al. Entropy and n-gram Analysis of Malicious PDF Documents , 2013 .
[4] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[5] Giorgio Giacinto,et al. A Pattern Recognition System for Malicious PDF Files Detection , 2012, MLDM.
[6] Lior Rokach,et al. Detecting unknown computer worm activity via support vector machines and active learning , 2012, Pattern Analysis and Applications.
[7] J. Mesirov,et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.
[8] Lior Rokach,et al. ALDROID: efficient update of Android anti-virus software using designated active learning methods , 2016, Knowledge and Information Systems.
[9] Vlado Keselj,et al. N-gram-based detection of new malicious code , 2004, Proceedings of the 28th Annual International Computer Software and Applications Conference, 2004. COMPSAC 2004..
[10] Razvan Benchea,et al. A practical approach on clustering malicious PDF documents , 2012, Journal in Computer Virology.
[11] Francesco Palmieri,et al. New Steganographic Techniques for the OOXML File Format , 2011, ARES.
[12] Stefan Berger,et al. BISSAM: Automatic Vulnerability Identification of Office Documents , 2012, DIMVA.
[13] Andrew Walenstein,et al. Tracking concept drift in malware families , 2012, AISec.
[14] Paul A. Viola,et al. Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.
[15] Giorgio Giacinto,et al. A structural and content-based approach for a precise and robust detection of malicious PDF files , 2015, 2015 International Conference on Information Systems Security and Privacy (ICISSP).
[16] Sotiris B. Kotsiantis,et al. Logitboost of Simple Bayesian Classifier , 2005, Informatica.
[17] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[18] J. Ross Quinlan,et al. Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.
[19] Lior Rokach,et al. Novel active learning methods for enhanced PC malware detection in windows OS , 2014, Expert Syst. Appl..
[20] Steve R. White,et al. Anatomy of a Commercial-Grade Immune System , 1999 .
[21] Angelos Stavrou,et al. Malicious PDF detection using metadata and structural features , 2012, ACSAC '12.
[22] Lior Rokach,et al. Mal-ID: Automatic Malware Detection Using Common Segment Analysis and Meta-Features , 2012, J. Mach. Learn. Res..
[23] Bo Li,et al. Forensic investigation of OOXML format documents , 2011, Digit. Investig..
[24] Žliobait . e,et al. Learning under Concept Drift: an Overview , 2010 .
[25] Yuval Elovici,et al. Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework , 2016, Security Informatics.
[26] Somesh Jha,et al. Testing malware detectors , 2004, ISSTA '04.
[27] Charles X. Ling,et al. Using AUC and accuracy in evaluating learning algorithms , 2005, IEEE Transactions on Knowledge and Data Engineering.
[28] Salvatore J. Stolfo,et al. A Study of Malcode-Bearing Documents , 2007, DIMVA.
[29] Leyla Bilge,et al. Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks , 2015, DIMVA.
[30] Yuval Shahar,et al. Improving condition severity classification with an efficient active learning based framework , 2016, J. Biomed. Informatics.
[31] Yuval Elovici,et al. Unknown malcode detection and the imbalance problem , 2009, Journal in Computer Virology.
[32] Felix C. Freiling,et al. Toward Automated Dynamic Malware Analysis Using CWSandbox , 2007, IEEE Secur. Priv..
[33] Nir Friedman,et al. Bayesian Network Classifiers , 1997, Machine Learning.
[34] Elmar Gerhards-Padilla,et al. PDF Scrutinizer: Detecting JavaScript-based attacks in PDF documents , 2012, 2012 Tenth Annual International Conference on Privacy, Security and Trust.
[35] Ian H. Witten,et al. The WEKA data mining software: an update , 2009, SKDD.
[36] Yuval Elovici,et al. Detection of malicious PDF files and directions for enhancements: A state-of-the art survey , 2015, Comput. Secur..
[37] Pavel Laskov,et al. Detection of Malicious PDF Files Based on Hierarchical Document Structure , 2013, NDSS.
[38] J. Platt. Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .
[39] Yuval Elovici,et al. ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files , 2014, 2014 IEEE Joint Intelligence and Security Informatics Conference.
[40] Nathalie Japkowicz,et al. A Feature Selection and Evaluation Scheme for Computer Virus Detection , 2006, Sixth International Conference on Data Mining (ICDM'06).
[41] Lior Rokach,et al. Improving malware detection by applying multi-inducer ensemble , 2009, Comput. Stat. Data Anal..
[42] Salvatore J. Stolfo,et al. Data mining methods for detection of new malicious executables , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.
[43] Evangelos P. Markatos,et al. Combining static and dynamic analysis for the detection of malicious documents , 2011, EUROSEC '11.
[44] Pavel Laskov,et al. Static detection of malicious JavaScript-bearing PDF documents , 2011, ACSAC '11.