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DeepAM: a heterogeneous deep learning framework for intelligent malware detection

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Abstract

With computers and the Internet being essential in everyday life, malware poses serious and evolving threats to their security, making the detection of malware of utmost concern. Accordingly, there have been many researches on intelligent malware detection by applying data mining and machine learning techniques. Though great results have been achieved with these methods, most of them are built on shallow learning architectures. Due to its superior ability in feature learning through multilayer deep architecture, deep learning is starting to be leveraged in industrial and academic research for different applications. In this paper, based on the Windows application programming interface calls extracted from the portable executable files, we study how a deep learning architecture can be designed for intelligent malware detection. We propose a heterogeneous deep learning framework composed of an AutoEncoder stacked up with multilayer restricted Boltzmann machines and a layer of associative memory to detect newly unknown malware. The proposed deep learning model performs as a greedy layer-wise training operation for unsupervised feature learning, followed by supervised parameter fine-tuning. Different from the existing works which only made use of the files with class labels (either malicious or benign) during the training phase, we utilize both labeled and unlabeled file samples to pre-train multiple layers in the heterogeneous deep learning framework from bottom to up for feature learning. A comprehensive experimental study on a real and large file collection from Comodo Cloud Security Center is performed to compare various malware detection approaches. Promising experimental results demonstrate that our proposed deep learning framework can further improve the overall performance in malware detection compared with traditional shallow learning methods, deep learning methods with homogeneous framework, and other existing anti-malware scanners. The proposed heterogeneous deep learning framework can also be readily applied to other malware detection tasks.

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  1. https://www.virustotal.com/.

References

  1. Arel I, Rose DC, Karnowski TP (2010) Deep machine learning—a new frontier in artificial intelligence research. IEEE Comput Intell Mag 5(4):13–18

    Article  Google Scholar 

  2. Bailey M, Oberheide J, Andersen J, Mao Z, Ahanian F, Nazario J (2007) Automated classification and analysis of internet malware. In: 10th international symposium on research in attacks, intrusions and defenses (RAID) 2007, LNCS, pp 178–197

  3. Bengio Y, LeCun Y (2007) Scaling learning algorithms towards AI. Large-Scale Kernel Mach 34(5):1–41

  4. Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

    Article  MathSciNet  MATH  Google Scholar 

  5. Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems 19 (NIPS’06), pp 153–160

  6. Carreira-Perpinan M, Hinton G (2005) On contrastive divergence learning. In: Proceedings of the tenth international workshop on artificial intelligence and statistics

  7. Cesare S, Xiang Y, Zhou W (2014) Control flow-based malware variant detection. IEEE Trans Dependable Secure Comput 11(4):307–317

    Article  Google Scholar 

  8. Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning (ICML’08), pp 160–167

  9. Dunne RA (2007) A statistical approach to neural networks for pattern recognition, 1st edn. Wiley, New York

    Book  MATH  Google Scholar 

  10. Egele M, Scholte T, Kirda E, Kruegel C (2008) A survey on automated dynamic malware analysis techniques and tools. In: ACM computing surveys (CSUR), vol 44(2), pp 6:1–6:42

  11. Filiol E (2006) Malware pattern scanning schemes secure against blackbox analysis. J Comput Virol 2(1):35–50

    Article  Google Scholar 

  12. Filiol E, Jacob G, Liard ML (2007) Evaluation methodology and theoretical model for antiviral behavioural detection strategies. J Comput Virol 3(1):27–37

    Article  Google Scholar 

  13. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  14. Hinton GE, Osindero S, Teh Y (2006) A fast learning algorithm for deep belief nets. Neural Comput 18:1527–1554

    Article  MathSciNet  MATH  Google Scholar 

  15. Hinton GE (2012) A practical guide to training restricted Boltzmann machines. Neural Netw Tricks Trade 7700:599–619

    Article  Google Scholar 

  16. Hinton GE, Dayan P, Frey BJ, Neal RM (1995) The wake-sleep algorithm for unsupervised neural networks. Science 268(5214):1158–1161

    Article  Google Scholar 

  17. Hinton GE (2007) To recognize shapes, first learn to generate images. Prog Brain Res 165:535–547

    Article  Google Scholar 

  18. Hou S, Chen L, Tas E, Demihovskiy I, Ye Y (2015) Cluster-oriented ensemble classifiers for malware detection. In: IEEE international conference on semantic computing (IEEE ICSC), pp 189–196

  19. Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15(5):2191–2201

    Article  Google Scholar 

  20. Jung W, Kim S, Choi S (2015) Poster: deep learning for zero-day flash malware detection. In: 36th IEEE symposium on security and privacy

  21. Kaspersky Lab (2015) The great bank robbery. http://www.kaspersky.com/about/news/virus/2015/Carbanak-cybergang-steals-1-bn-USD-from-100-financial-institutions-worldwide

  22. Kavukcuoglu K, Sermanet P, Boureau Y, Gregor K, Mathieu M, LeCun Y (2010) Learning convolutional feature hierarchies for visual recognition. In: Advances in neural information processing systems (NIPS 2010), vol 23

  23. Kephart J, Arnold W (1994) Automatic extraction of computer virus signatures. In: Proceedings of 4th virus bulletin international conference, pp 178–184

  24. Kolter J, Maloof M (2004) Learning to detect malicious executables in the wild. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (ACM SIGKDD’04), pp 470–478

  25. Kong D, Yan G (2013) Discriminant malware distance learning on structural information for automated malware classification. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1357–1365

  26. Li Y, Ma R, Jiao R (2015) A hybrid malicious code detection method based on deep learning. Int J Secur Appl 9(5):205–216

    Google Scholar 

  27. Lv Y, Duan Y, Kang W, Li Z, Wang F (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  28. Masud MM, Al-Khateeb TM, Hamlen KW, Gao J, Khan L, Han J, Thuraisingham B (2008) Cloud-based malware detection for evolving data streams. In: ACM transactions on management information systems (TMIS), vol 2(3), pp 16:1–16:27

  29. Menahem E, Shabtai A, Levhar A (2013) Detecting malware through temporal function-based features. In: Proceedings of the 2013 ACM SIGSAC conference on computer and communications security, pp 1379–1382

  30. Ouellette J, Pfeffer A, Lakhotia A (2013) Countering malware evolution using cloud-based learning. In: 8th international conference on malicious and unwanted software (MALWARE), pp 85–94

  31. Park Y, Zhang Q, Reeves D, Mulukutla V (2010) AntiBot: clustering common semantic patterns for bot detection. In: IEEE 34th annual computer software and applications conference, pp 262–272

  32. Schultz M, Eskin E, Zadok E (2001) Data mining methods for detection of new malicious executables. In: Proccedings of IEEE symposium on security and privacy

  33. Shah S, Jani H, Shetty S, Bhowmick K (2013) Virus detection using artificial neural networks. Int J Comput Appl 84(5):3–21

  34. Sung A, Xu J, Chavez P, Mukkamala S (2005) Static analyzer of vicious executables (save). In: Proceedings of the 20th annual computer security applications conference (ACSAC), pp 326–334

  35. Symantec (2016) Internet security threat report. https://www.symantec.com/secu-rity-center/threat-report

  36. Teh YW, Hinton GE (2001) Rate-coded restricted Boltzmann machines for face recognition. In: Proceedings of advances in neural information processing systems, pp 908–914

  37. Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

    MathSciNet  MATH  Google Scholar 

  38. Wang J, Deng P, Fan Y, Jaw L, Liu Y (2003) Virus detection using data mining techniques. In: Proccedings of IEEE 37th annual 2003 international Carnahan conference security technology

  39. Wueest C (2016) Symantec security response: financial threats 2015. http://www.syman-tec.com/content/en/us/enterprise/media/security_response/whitepapers/financial-threats-2015.pdf

  40. Ye Y, Wang D, Li T, Ye D, Jiang Q (2008) An intelligent PE-malware detection system based on association mining. J Comput Virol 4:323–334

    Article  Google Scholar 

  41. Ye Y, Wang D, Li T, Ye D (2007) IMDS: intelligent malware detection system. In: Proceedings of the 13th ACM SIGKDD, pp 1043–1047

  42. Ye Y, Li T, Zhu S, Zhuang W, Tas E, Gupta U, Abdulhayoglu M (2011) Combining file content and file relations for cloud based malware detection. In: Proceedings of ACM international conference on knowledge discovery and data mining (ACM SIGKDD), pp 222–230

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Acknowledgements

The authors would also like to thank the anti-malware experts of Comodo Security Lab for the data collection as well as helpful discussions and supports. This work is partially supported by the US National Science Foundation under Grant CNS-1618629.

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Authors and Affiliations

  1. Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, 26506, USA

    Yanfang Ye, Lingwei Chen, Shifu Hou, William Hardy & Xin Li

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  1. Yanfang Ye
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  2. Lingwei Chen
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  3. Shifu Hou
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  4. William Hardy
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  5. Xin Li
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Correspondence to Yanfang Ye.

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Ye, Y., Chen, L., Hou, S. et al. DeepAM: a heterogeneous deep learning framework for intelligent malware detection. Knowl Inf Syst 54, 265–285 (2018). https://doi.org/10.1007/s10115-017-1058-9

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