Zhao Z, Wang J, Bai J (2014) Malware detection method based on the control-flow construct feature of software.Hi all, I just want to keep the group here posted and be transparent on what happened. Yuan MY (2014) Data mining and machine learning: WEKA applied technology and practice. Yeung DY, Ding Y (2003) Host-based intrusion detection using dynamic and static behavioral models. In: Proceedings of the 5th international conference on malicious and unwanted software: MALWARE 2010, pp 23–30 Tian R, Islam R, Batten L, Versteeg S (2010) Differentiating malware from cleanware using behavioral analysis. In: Proceedings of the third international conference on neural networks, Barcelona Stopel D, Boger Z, Moskovitch R, Shahar Y, Elovici Y (2006b) Improving worm detection with artificial neural networks through feature selection and temporal analysis techniques. In: Proceedings of IEEE international joint conference on neural networks, Vancouver Stopel D, Boger Z, Moskovitch R, Shahar Y, Elovici Y (2006a) Application of Artificial Neural Networks Techniques to Computer Worm Detections. In: Proceedings of the IEEE symposium on security and privacy, Oakland USA, pp 38–49 Schultz MG, Eskin E, Zadok E, Stolfo SJ (2001) Data mining methods for detection of new malicious executables.
#AUTO TEXT EXPANDER MALWARE CODE#
Shabtai A, Moskovitch R, Elovici Y, Glezer C (2009) Detection of malicious code by applying machine learning classifiers on static features-a state-of-the-art survey. In: International conference on malicious & unwanted software, pp 11–20 Saxe J, Berlin K (2015) Deep neural network based malware detection using two dimensional binary program features. IEEE Trans Audio Speech Lang Process 22(4):778–784 Sarikaya R, Hinton GE, Deoras A (2014) Application of deep belief networks for natural language understanding. Santos I, Brezo F, Ugarte-pedrero X, Bringas PG (2013) Opcode sequences as representation of executables for data-mining-based unknown malware detection. Salton G, Buckley C (1988) Term-weighting approaches in automatic text retrieval.
Salakhutdinov R, Hinton G (2012) An efficient learning procedure for deep Boltzmann machines. In: IEEE intelligence and security informatics, Taiwan, pp 156–161 Moskovitch R, Stopel D, Feher C, Nissim N, Elovici Y (2008b) Unknown malcode detection via text categorization and the imbalance problem. In: European conference on intelligence and security informatics 2008 (EuroISI08), Esbjerg, Denmark, pp 204–215 Moskovitch R, Feher C, Zachar N, Berger E, Gitelman M, Dolev S, et al (2008a) Unknown malcode detection using OPCODE representation. Manuel E, Theodoor S, Engin K, Christopher K (2012) A survey on automated dynamic malware-analysis techniques and tools.
In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. Kolter JZ, Maloof MA (2004) Learning to detect malicious executables in the wild.
Islam R et al (2013) Classification of malware based on integrated static and dynamic features. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Hinton G et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. In: Proceedings ofICDM-2006, Hong Kong, pp 891–895 Henchiri O, Japkowicz N (2006) A feature selection and evaluation scheme for computer virus detection. Comput Secur 46:62–78Įrhan D, Bengio Y, Courville A, Manzagol P, Vincent P (2010) Why does unsupervised pre-training help deep learning? J Mach Learn Res 11:625–660Įskandari M, Hashemi S (2012) A graph mining approach for detecting unknown malwares.
Comput Secur 44(1):64–82Įlhadi AAE, Maarof MA, Barry BIA, Hamza H (2014) Enhancing the detection of metamorphic malware using call graphs. IEEE Trans Audio Speech Lang Process 20(1):30–41ĭing Y, Dai W, Yan S et al (2014) Control flow-based opcode behavior analysis for malware detection.
#AUTO TEXT EXPANDER MALWARE SOFTWARE#
In: ACM SIGSOFT international symposium on software testing and analysis (ISSTA ‘04), Boston, USA, pp 34–44ĭahl GE, Yu D, Deng L, Acero A (2012) Context-dependent pretrained deep neural networks for large-vocabulary speech recognition. In: AISec ‘09 Proceedings of the 2nd ACM workshop on Security and artificial intelligence, pp 55–62Ĭhristodorescu M, Jha S (2004) Testing malware detectors. Ahmed F, Hameed H, Shafiq MZ, Farooq M (2009) Using spatio-temporal information in API calls with machine learning algorithms for malware detection.
0 kommentar(er)
