Citation: | Jia YAN, Jia YAN, Chujiang NIE, Purui SU. Method for Generating Malicious Code Adversarial Samples Based on Genetic Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2126-2133. doi: 10.11999/JEIT191059 |
LANDAGE J and WANKHADE M P. Malware and malware detection techniques: A survey[J]. International Journal of Engineering Research & Technology, 2013, 2(12): 61–68.
|
SAXE J and BERLIN K. Deep neural network based malware detection using two dimensional binary program features[C]. The 10th International Conference on Malicious and Unwanted Software (MALWARE), Fajardo, USA, 2015: 11–20. doi: 10.1109/MALWARE.2015.7413680.
|
ARP D, SPREITZENBARTH M, HUBNER M, et al. Drebin: Effective and explainable detection of android malware in your pocket[C]. Network and Distributed System Security Symposium, San Diego, USA, 2014: 23–26. doi: 10.14722/ndss.2014.23247.
|
RAFF E, SYLVESTER J, and NICHOLAS C. Learning the PE header, malware detection with minimal domain knowledge[C]. The 10th ACM Workshop on Artificial Intelligence and Security, Dallas, USA, 2017: 121–132. doi: 10.1145/3128572.3140442.
|
RAFF E, ZAK R, COX R, et al. An investigation of byte n-gram features for malware classification[J]. Journal of Computer Virology and Hacking Techniques, 2018, 14(1): 1–20. doi: 10.1007/s11416-016-0283-1
|
Cylance Inc. What’s new in CylancePROTECT and CylanceOPTICS[EB/OL]. https://s7d2.scene7.com/is/content/cylance/prod/cylance-web/en-us/resources/knowledge-center/resource-library/briefs/Whats-New-CylancePROTECT-and-CylanceOPTICS.pdf, 2020.
|
Sophos Inc. Sophos central migration tool articles, documentation and resources[EB/OL]. https://community.sophos.com/kb/en-us/122264#Product%20Information, 2020.
|
梁光辉, 庞建民, 单征. 基于代码进化的恶意代码沙箱规避检测技术研究[J]. 电子与信息学报, 2019, 41(2): 341–347. doi: 10.11999/JEIT180257
LIANG Guanghui, PANG Jianmin, and SHAN Zheng. Malware sandbox evasion detection based on code evolution[J]. Journal of Electronics &Information Technology, 2019, 41(2): 341–347. doi: 10.11999/JEIT180257
|
GROSSE K, PAPERNOT N, MANOHARAN P, et al. Adversarial perturbations against deep neural networks for malware classification[J]. arXiv, 2016, 1606.04435.
|
XU Weilin, QI Yanjun, and EVANS D. Automatically evading classifiers[C]. The 23rd Annual Network and Distributed System Security Symposium, San Diego, USA, 2016: 21–24. doi: 10.14722/ndss.2016.23115.
|
HU Weiwei and TAN Ying. Generating adversarial malware examples for black-box attacks based on GAN[J]. arXiv, 2017, 1702.05983.
|
HU Weiwei and TAN Ying. Black-box attacks against RNN based malware detection algorithms[C]. The Workshops of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018.
|
RAFF E, BARKER J, SYLVESTER J, et al. Malware detection by eating a whole exe[C]. The Workshops of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018: 268–276.
|
TOTAL V. VirusTotal-free online virus, malware and url scanner[EB/OL]. https//www.virustotal.com/en, 2012.
|
PASCANU R, STOKES J W, SANOSSIAN H, et al. Malware classification with recurrent networks[C]. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, 2015: 1916–1920. doi: 10.1109/ICASSP.2015.7178304.
|
KOLOSNJAJI B, ZARRAS A, WEBSTER G, et al. Deep learning for classification of malware system call sequences[C]. The 29th Australasian Joint Conference on Artificial Intelligence, Hobart, Australia, 2016: 137–149. doi: 10.1007/978-3-319-50127-7_11.
|
HUANG Wenyi and STOKES J W. MtNet: A multi-task neural network for dynamic malware classification[C]. The 13th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, San Sebastián, Spain, 2016: 399–418. doi: 10.1007/978-3-319-40667-1_20.
|
MANNING C D, RAGHAVAN P, and SCHÜTZE H. Introduction to Information Retrieval[M]. Cambridge: Cambridge University Press, 2008.
|
HAN K S, LIM J H, KANG B, et al. Malware analysis using visualized images and entropy graphs[J]. International Journal of Information Security, 2015, 14(1): 1–14. doi: 10.1007/s10207-014-0242-0
|
KANCHERLA K and MUKKAMALA S. Image visualization based malware detection[C]. 2013 IEEE Symposium on Computational Intelligence in Cyber Security (CICS), Singapore, 2013: 40–44. doi: 10.1109/CICYBS.2013.6597204.
|
LIU Xinbo, LIN Yaping, LI He, et al. A novel method for malware detection on ML-based visualization technique[J]. Computers & Security, 2020, 89: 101682. doi: 10.1016/j.cose.2019.101682
|
Skylight. Cylance, I kill you![ EB/OL]. https://skylightcyber.com/2019/07/18/cylance-i-kill-you/, 2019.
|
MOHURLE S and PATIL M. A brief study of wannacry threat: Ransomware attack 2017[J]. International Journal of Advanced Research in Computer Science, 2017, 8(5): 1938–1940. doi: 10.26483/ijarcs.v8i5.4021
|
DANG Hung, HUANG Yue, and CHANG E C. Evading classifiers by morphing in the dark[C]. 2017 ACM SIGSAC Conference on Computer and Communications Security, Dallas, USA, 2017: 119–133. doi: 10.1145/3133956.3133978.
|
戚利. Windows PE权威指南[M]. 北京: 机械工业出版社, 2011: 67–68.
QI Li. Windows PE: The Definitive Guide[M]. Beijing: Machinery Industry Press, 2011: 67–68.
|
KOZA J R. Genetic Programming II: Automatic Discovery of Reusable Subprograms[M]. Cambridge, MA, USA: MIT Press, 1994: 32.
|
Cuckoo Sandbox. Cuckoo Sandbox–Automated malware analysis[EB/OL]. http://www.cuckoosandbox.org, 2017.
|
BANON S. Elastic endpoint security[EB/OL]. https://www.elastic.co/cn/blog/introducing-elastic-endpoint-security, 2019.
|
Trapmine Inc. TRAPMINE integrates machine learning engine into VirusTotal[EB/OL]. https://trapmine.com/blog/trapmine-machine-learning-virustotal/, 2018.
|