Search Results - "JavaScript obfuscation"
-
1
Authors: João Vitor Freitas Fernandes
Subject Terms: Engenharia electrotécnica, electrónica e informática, Electrical engineering, Electronic engineering, Information engineering
File Description: application/pdf
Availability: https://hdl.handle.net/10216/152115
-
2
Authors:
Source: Proceedings of the ACM Internet Measurement Conference. :648-661
-
3
Authors: et al.
Source: 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN).
-
4
Authors: et al.
Source: Security and Communication Networks. 8:1092-1107
-
5
Authors: et al.
Source: 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications. :1370-1375
-
6
-
7
Authors: et al.
Source: 2025 29th International Conference on System Theory, Control and Computing (ICSTCC) ; page 631-636
-
8
-
9
Authors: et al.
Source: Lecture Notes in Computer Science ISBN: 9783642105081
-
10
-
11
-
12
Authors: et al.
Source: Mathematics and Mathematical Modeling; № 2 (2020); 1-24 ; Математика и математическое моделирование; № 2 (2020); 1-24 ; 2412-5911
Subject Terms: obfuscation, obfuscation detection, JavaScript obfuscation, AST, AST coloring, обфускация, обнаружение обфускации, детектирование обфускации, обфускация JavaScript, АСД, раскраска АСД
File Description: application/pdf
Relation: https://www.mathmelpub.ru/jour/article/view/218/167; Collberg C., Thomborson C., Low D. A taxonomy of obfuscating transformations // New Zealand. Univ. of Auckland. Dep. of Computer Science. Technical report. 1997. No. 148. 36 p.; Cesare S., Yang Xiang. Software similarity and classification. L.; N.Y.: Springer, 2012. 88 p.; Curtsinger C., Livshits B., Zorn B.G., Seifert C. ZOZZLE: Fast and precise in-browser JavaScript malware detection // 20th USENIX security symp. (San Francisco, CA, USA, August 10-12, 2011): Proc. Berkeley: USENIX Assoc., 2011. Pp. 33-48.; Kapravelos A., Shoshitaishvili Y., Cova M., Kruegel C., Vigna G. Revolver: An automated approach to the detection of evasive web-based malware // 22nd USENIX security symp. (Washington. DC, USA, August 14-16, 2013): Proc. Berkeley: USENIX Assoc., 2013. Pp. 637-651.; Fass A., Krawczyk R.P., Backes M., Stock B. JaSt: Fully syntactic detection of malicious (obfuscated) JavaScript // Detection of intrusions and malware and vulnerability assessment: 15th intern. conf. on detection of intrusions and malware and vulnerability assessment: DIMVA 2018 (Saclay, France, June 28-29, 2018): Proc. Cham: Springer, 2018. Pp. 303-325. DOI:10.1007/978-3-319-93411-2_14; Junjie Wang, Yinxing Xue, Yang Liu, Tian Huat Tan. JSDC: A hybrid approach for JavaScript malware detection and classification // 10th ACM symp. on information, computer and communications security: ASIA CCS’15 (Singapore, April 14-17, 2015): Proc. N.Y.: ACM, 2015. Pp. 109-120. DOI:10.1145/2714576.2714620; Blanc G., Miyamoto D., Akiyama M., Kadobayashi Y. Characterizing obfuscated JavaScript using abstract syntax trees: Experimenting with malicious scripts // 26th intern. conf. on advanced information networking and applications workshops (Fukuoka, Japan, March 26-29, 2012): Proc. N.Y.: IEEE, 2012. Pp. 344-351. DOI:10.1109/WAINA.2012.140; Tellenbach B., Paganoni S., Rennhard M. Detecting obfuscated JavaScripts from known and unknown obfuscators using machine learning // Intern. J. on Advances in Security. 2016. Vol. 9. No. 3-4. Pp. 196-206. DOI:10.21256/zhaw-1537; Ndichu S., Kim S., Ozawa S., Misu T., Makishima K. A machine learning approach to detection of JavaScript-based attacks using AST features and paragraph vectors // Applied Soft Computing. 2019. Vol. 84. Article105721. DOI:10.1016/j.asoc.2019.105721; ECMAScript 2019 Language Specification. Режим доступа: https://www.ecma-international.org/ecma-262/10.0/index.html (дата обращения: 20.03.2020).; Friedman J.H. Greedy function approximation: a gradient boosting machine // Annals of Statistics. 2001. Vol. 29. No. 5. Pp. 1189-1232.; Сервис GitHub [Электрон. ресурс]. Режим доступа: https://github.com/ (дата обращения: 20.03.2020).; Acornjs/acorn [Электрон. ресурс]. Режим доступа: https://github.com/acornjs/acorn (дата обращения: 20.03.2020).; Alexhorn/defendjs [Электрон. ресурс]. Режим доступа: https://github.com/alexhorn/defendjs (дата обращения: 20.03.2020).; Gnirts: Obfuscate string literals in JavaScript code [Электрон. ресурс]. Режим доступа: https://anseki.github.io/gnirts/ (дата обращения: 20.03.2020).; JavaScript obfuscator tool [Электрон. ресурс]. Режим доступа: https://obfuscator.io/ (дата обращения: 20.03.2020).; Zswang/jfogs [Электрон. ресурс]. Режим доступа: https://github.com/zswang/jfogs (дата обращения: 20.03.2020).; JScrewlt [Электрон. ресурс]. Режим доступа: https://jscrew.it/ (дата обращения: 20.03.2020).; UglifyJS: JavaScript compressor/minifier [Электрон. ресурс]. Режим доступа: http://lisperator.net/uglifyjs/ (дата обращения: 20.03.2020).; Closure tools [Электрон. ресурс]. Режим доступа: https://developers.google.com/closure (дата обращения: 20.03.2020).; Huu-Danh Pham, Tuan Dinh Le, Vu Thanh Nguyen. Static PE malware detection using gradient boosting decision trees algorithm // Future data and security engineering: Intern. conf. on future data and security engineering: FDSE 2018 (Ho Chi Minh City, Vietnam, November 28-30, 2018): Proc. Cham: Springer, 2018. Pp. 228-236. DOI:10.1007/978-3-030-03192-3_17; Singh L., Hofmann M. Dynamic behavior analysis of android applications for malware detection // Intern. conf. on intelligent communication and computational techniques: ICCT 2017 (Jaipur, India, December 22-23, 2017): Proc. N.Y.: IEEE, 2018. Pp. 1-7. DOI:10.1109/intelcct.2017.8324010; Handong Cui, Delu Huang, Yong Fang, Liang Liu, Cheng Huang. Webshell detection based on random forest–gradient boosting decision tree algorithm // 3rd intern. conf. on data science in cyberspace: DSC 2018 (Guangzhou, China, June 18-21, 2018): Proc. N.Y.: IEEE, 2018. Pp. 153-160. DOI:10.1109/DSC.2018.00030; Pogosova M. Detecting obfuscated scripts with machine-learning techniques: Cand. diss. Helsinki: Aalto Univ., 2020. 58 p. Режим доступа: https://aaltodoc.aalto.fi/bitstream/handle/123456789/43575/master_Pogosova_Mariam_2020.pdf?sequence=1&isAllowed=y (дата обращения 28.06.2020).; Hyafil L., Rivest R.L. Constructing optimal binary decision trees is NP-complete // Information Processing Letters. 1976. Vol. 5. No. 1. Pp. 15-17. DOI:10.1016/0020-0190(76)90095-8; Prokhorenkova L., Gusev G., Vorobev A., Dorogush A.V., Gulin A. CatBoost: unbiased boosting with categorical features // NIPS 2018: 32nd conf. on neural information processing systems (Montreal, Canada, December 3-8, 2018): Proc. Red Hook: Curran Assoc. Inc., 2019. Pp. 6639-6649.; Dorogush A.V., Ershov V., Gulin A. CatBoost: gradient boosting with categorical features support. Режим доступа: https://arxiv.org/pdf/1810.11363.pdf (дата обращения 28.06.2020).; Fass A., Backes M., Stock B. HideNoSeek: Camouflaging malicious JavaScript in Benign ASTs // ACM SIGSAC conf. on computer and communications security: CCS’19 (London, UK, November 11-15, 2019): Proc. N.Y.: ACM, 2019. Pp. 1899-1913. DOI:10.1145/3319535.3345656; https://www.mathmelpub.ru/jour/article/view/218
-
13
Authors: et al.
Contributors: et al.
Source: Lecture Notes in Computer Science ; 9th International Conference on Network and Parallel Computing (NPC) ; https://inria.hal.science/hal-01551330 ; 9th International Conference on Network and Parallel Computing (NPC), Sep 2012, Gwangju, South Korea. pp.129-137, ⟨10.1007/978-3-642-35606-3_15⟩
Subject Terms: Malicious Web Sites, JavaScript Obfuscation, JavaScript Extraction, Hybrid Strength Analysis System, [INFO]Computer Science [cs]
Subject Geographic: Gwangju, South Korea
-
14
-
15
Authors: et al.
Subject Terms: AST representation, detection, encoding, feature-based, JavaScript malware, obfuscation, static analysis
Relation: Proc. of the 11th IEEE Int. Conference on Trust, Security and Privacy in Computing and Communications, TrustCom-2012 - 11th IEEE Int. Conference on Ubiquitous Computing and Communications, IUCC-2012, pages 1370 - 1375; http://ir.lib.ntust.edu.tw/handle/987654321/43617; http://ir.lib.ntust.edu.tw/bitstream/987654321/43617/-1/index.html
-
16
Authors:
Source: 2012 7th International Conference on Malicious & Unwanted Software; 1/ 1/2012, p9-16, 8p
-
17
Authors: Ladyga, Linas
Thesis Advisors: Čenys, Antanas, Juknius, Jonas, Goranin, Nikolaj, Vilnius Gediminas Technical University
Subject Terms: Informatics, Virusai, Kenkėjiškos programos, JavaScript kodo maskavimas, Kenksmingo kodo aptikimo metodai, Užmaskuoto kodo detektorius, Virus, Malware, JavaScript obfuscation, Malicious code detection techniques, Obfuscated code detector
File Description: application/pdf
-
18
Authors: et al.
Contributors: et al.
Subject Terms: Malicious Web Sites, Obfuscation JavaScript, Entropy, Frequency, Density
File Description: application/pdf
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.359.7432; http://www.sersc.org/journals/IJAST/vol26/2.pdf
-
19
Authors: et al.
Source: Future Internet; Aug2022, Vol. 14 Issue 8, p217-N.PAG, 15p
-
20
Authors: et al.
Index Terms: Article - Refereed
URL: CCS '24: Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security
Nájsť tento článok vo Web of Science 
Full Text Finder