Aafer, Y, Du W, Yin H (2013) Droidapiminer: Mining api-level features for robust malware detection in android. In: Zia TA, Zomaya AY, Varadharajan V, Mao ZM (eds)Security and Privacy in Communication Networks - 9th International ICST Conference, SecureComm 2013, Sydney, NSW, Australia, September 25-28, 2013, Revised Selected Papers, Springer, vol 127, 86–103. https://doi.org/10.1007/978-3-319-04283-1_6.
Abu-El-Haija, S, Perozzi B, Kapoor A, Alipourfard N, Lerman K, Harutyunyan H, Steeg GV, Galstyan A (2019) MixHop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. PMLR Long Beach Calif USA Proc Mach Learn Res 97:21–29.
Google Scholar
Allamanis, M, Barr ET, Devanbu PT, Sutton CA (2018) A survey of machine learning for big code and naturalness. ACM Comput Surv 51(4):81:1–81:37. https://doi.org/10.1145/3212695.
Article
Google Scholar
Andriesse, D, Chen X, van der Veen V, Slowinska A, Bos H (2016) An in-depth analysis of disassembly on full-scale x86/x64 binaries. In: USENIX In: USENIX Association, Austin. https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/andriesse.
Ben-Nun, T, Jakobovits AS, Hoefler T (2018) Neural code comprehension: A learnable representation of code semantics. In: Bengio S, Wallach HM, Larochelle H, Grauman K, Cesa-Bianchi N, Garnett R (eds)Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December, 2018, Montréal, Canada, 3589–3601. http://papers.nips.cc/paper/7617-neural-code-comprehension-a-learnable-representation-of-code-semantics.
(2017) 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net(Bengio Y, LeCun Y, eds.)https://openreview.net/group?id=ICLR.cc/2017/conference.
Boland, T, Black PE (2012) Juliet 1.1 C/C++ and java test suite. IEEE Comput 45(10):88–90. https://doi.org/10.1109/MC.2012.345.
Article
Google Scholar
Cha, SK, Avgerinos T, Rebert A, Brumley D (2012) Unleashing mayhem on binary code In: IEEE Symposium on Security and Privacy, SP 2012, 21-23 May, 2012, San Francisco, California, USA, IEEE Computer Society, 380–394. https://doi.org/10.1109/SP.2012.31.
Chen, J, Ma T, Xiao C (2018) Fastgcn: Fast learning with graph convolutional networks via importance sampling In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net. https://openreview.net/forum?id=rytstxWAW.
Ding, SHH, Fung BCM, Charland P (2019) Asm2vec: Boosting static representation robustness for binary clone search against code obfuscation and compiler optimization In: 2019 IEEE Symposium on Security and Privacy, SP 2019, San Francisco, CA, USA, May 19-23, 2019, IEEE, 472–489. https://doi.org/10.1109/SP.2019.00003.
Eschweiler, S, Yakdan K, Gerhards-Padilla E (2016) discovre: Efficient cross-architecture identification of bugs in binary code In: 23rd Annual Network and Distributed System Security Symposium, NDSS 2016, San Diego, California, USA, February 21-24, 2016, The Internet Society. http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2017/09/discovre-efficient-cross-architecture-identification-bugs-binary-code.pdf.
Feng, Q, Zhou R, Xu C, Cheng Y, Testa B, Yin H (2016) Scalable graph-based bug search for firmware images. In: Weippl ER, Katzenbeisser S, Kruegel C, Myers AC, Halevi S (eds)Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, October 24-28, 2016, 480–491.. ACM. https://doi.org/10.1145/2976749.2978370.
Goseva-Popstojanova, K, Perhinschi A (2015) On the capability of static code analysis to detect security vulnerabilities. Inform Softw Technol 68:18–33. https://doi.org/10.1016/j.infsof.2015.08.002.
Article
Google Scholar
Grieco, G, Grinblat GL, Uzal LC, Rawat S, Feist J, Mounier L (2016) Toward large-scale vulnerability discovery using machine learning. In: Bertino E, Sandhu R, Pretschner A (eds)Proceedings of the Sixth ACM on Conference on Data and Application Security and Privacy, CODASPY 2016, New Orleans, LA, USA, March 9-11, 2016, 85–96.. ACM. https://doi.org/10.1145/2857705.2857720.
(2017) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4-9 December 2017(Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R, eds.), Long Beach.
Hamilton, WL, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs In: (Guyon et al. 2017), 1024–1034. http://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.
He, J, Ivanov P, Tsankov P, Raychev V, Vechev MT (2018) Debin: Predicting debug information in stripped binaries. In: Lie D, Mannan M, Backes M, Wang X (eds)Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS 2018, Toronto, ON, Canada, October 15-19, 2018, 1667–1680.. ACM. https://doi.org/10.1145/3243734.3243866.
Hindle, A, Barr ET, Gabel M, Su Z, Devanbu PT (2016) On the naturalness of software. Commun ACM 59(5):122–131. https://doi.org/10.1145/2902362.
Article
Google Scholar
Kang, B, Yerima SY, Sezer S, McLaughlin K (2016) N-gram opcode analysis for android malware detection. IJCSA 1(1):231–255. https://doi.org/10.22619/ijcsa.2016.1001011.
Article
Google Scholar
Karbab, EB, Debbabi M, Derhab A, Mouheb D (2018) Maldozer: Automatic framework for android malware detection using deep learning. Digit Inv 24:S48—S59. https://doi.org/10.1016/j.diin.2018.01.007.
Google Scholar
Kipf, TN, Welling M (2017) Semi-supervised classification with graph convolutional networks In: (Bengio and LeCun 2017). https://openreview.net/forum?id=SJU4ayYgl.
Kolosnjaji, B, Zarras A, Webster GD, Eckert C (2016) Deep learning for classification of malware system call sequences. In: Kang BH Bai Q (eds)AI 2016: Advances in Artificial Intelligence - 29th Australasian Joint Conference, Hobart, TAS, Australia, December 5-8, 2016, Proceedings, Springer, Lecture Notes in Computer Science, vol. 9992, 137–149. https://doi.org/10.1007/978-3-319-50127-7_11.
Lee, T, Choi B, Shin Y, Kwak J (2018) Automatic malware mutant detection and group classification based on the n-gram and clustering coefficient. J Supercomput 74(8):3489–3503. https://doi.org/10.1007/s11227-015-1594-6.
Article
Google Scholar
Li, Y, Gu C, Dullien T, Vinyals O, Kohli P (2019b) Graph matching networks for learning the similarity of graph structured objects. In: Chaudhuri K Salakhutdinov R (eds)Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, PMLR, Proceedings of Machine Learning Research, vol. 97, 3835–3845. http://proceedings.mlr.press/v97/li19d.html.
Li, Y, Tarlow D, Brockschmidt M, Zemel RS (2016) Gated graph sequence neural networks. In: Bengio Y LeCun Y (eds)4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings. http://arxiv.org/abs/1511.05493.
Li, B, Zhang Y, Yao J, Yin T (2019a) MDBA: detecting malware based on bytes n-gram with association mining In: 26th International Conference on Telecommunications, ICT 2019, Hanoi, Vietnam, April 8-10, 2019, 227–232.. IEEE. https://doi.org/10.1109/ICT.2019.8798828.
Li, Z, Zou D, Xu S, Ou X, Jin H, Wang S, Deng Z, Zhong Y (2018) Vuldeepecker: A deep learning-based system for vulnerability detection In: 25th Annual Network and Distributed System Security Symposium, NDSS 2018, San Diego, California, USA, February 18-21, 2018, The Internet Society. http://wp.internetsociety.org/ndss/wp-content/uploads/sites/25/2018/02/ndss2018_03A-2_Li_paper.pdf.
Lin, Z, Feng M, dos Santos CN, Yu M, Xiang B, Zhou B, Bengio Y (2017b) A structured self-attentive sentence embedding In: (Bengio and LeCun 2017). https://openreview.net/forum?id=BJC_jUqxe.
Lin, G, Zhang J, Luo W, Pan L, Xiang Y (2017a) POSTER: vulnerability discovery with function representation learning from unlabeled projects In: (Thuraisingham et al. 2017), 2539–2541. https://doi.org/10.1145/3133956.3138840.
Liu, Z, Chen C, Li L, Zhou J, Li X, Song L, Qi Y (2019) Geniepath: Graph neural networks with adaptive receptive paths In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, 4424–4431.. AAAI Press. https://doi.org/10.1609/aaai.v33i01.33014424.
Mikolov, T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. In: Bengio Y LeCun Y (eds)1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings. http://arxiv.org/abs/1301.3781.
Mikolov, T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Burges CJC, Bottou L, Ghahramani Z, Weinberger KQ (eds)Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8 2013, Lake Tahoe, Nevada, United States, 3111–3119. http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.
Monti, F, Boscaini D, Masci J, Rodolà E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model cnns In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, 5425–5434.. IEEE Computer Society. https://doi.org/10.1109/CVPR.2017.576.
Mou, L, Li G, Zhang L, Wang T, Jin Z (2016) Convolutional neural networks over tree structures for programming language processing. In: Schuurmans D Wellman MP (eds)Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, AAAI Press 2016, 1287–1293, Phoenix. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11775.
Murtaza, SS, Khreich W, Hamou-Lhadj A, Bener AB (2016) Mining trends and patterns of software vulnerabilities, Vol. 117. https://doi.org/10.1016/j.jss.2016.02.048.
Pang, Y, Xue X, Namin AS (2015) Predicting vulnerable software components through n-gram analysis and statistical feature selection. In: Li T, Kurgan LA, Palade V, Goebel R, Holzinger A, Verspoor K, Wani MA (eds)14th IEEE International Conference on Machine Learning and Applications, ICMLA 2015, Miami, FL, USA, December 9-11, 2015, 543–548.. IEEE. https://doi.org/10.1109/ICMLA.2015.99.
Perozzi, B, Al-Rfou R, Skiena S (2014) Deepwalk: online learning of social representations. In: Macskassy SA, Perlich C, Leskovec J, Wang W, Ghani R (eds)The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, New York, NY, USA - August 24 - 27, 2014, 701–710.. ACM. https://doi.org/10.1145/2623330.2623732.
Rawat, S, Mounier L (2012) Finding buffer overflow inducing loops in binary executables In: Sixth International Conference on Software Security and Reliability, SERE 2012, Gaithersburg, Maryland, USA, 20-22 June 2012, 177–186.. IEEE. https://doi.org/10.1109/SERE.2012.30.
Ray, B, Hellendoorn V, Godhane S, Tu Z, Bacchelli A, Devanbu PT (2016) On the “naturalness” of buggy code. In: Dillon LK, Visser W, Williams L (eds)Proceedings of the 38th International Conference on Software Engineering, ICSE 2016, Austin, TX, USA, May 14-22, 2016, 428–439.. ACM. https://doi.org/10.1145/2884781.2884848.
Santos, I, Penya YK, Devesa J, Bringas PG (2009) N-grams-based file signatures for malware detection(Cordeiro J, Filipe J, eds.)
Shoshitaishvili, Y, Wang R, Salls C, Stephens N, Polino M, Dutcher A, Grosen J, Feng S, Hauser C, Krügel C, Vigna G (2016) SOK: (state of) the art of war: Offensive techniques in binary analysis In: IEEE Symposium on Security and Privacy, SP 2016, San Jose, CA, USA, May 22-26, 2016, 138–157.. IEEE Computer Society. https://doi.org/10.1109/SP.2016.17.
Simonyan, K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: Bengio Y LeCun Y (eds)3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings. http://arxiv.org/abs/1409.1556.
Theisen, C, Herzig K, Morrison P, Murphy B, Williams LA (2015) Approximating attack surfaces with stack traces. In: Bertolino A, Canfora G, Elbaum SG (eds)37th IEEE/ACM International Conference on Software Engineering, ICSE 2015, Florence, Italy, May 16-24, 2015, Volume 2, 199–208.. IEEE Computer Society. https://doi.org/10.1109/ICSE.2015.148.
Thuraisingham, BM, Evans D, Malkin T, Xu D (2017) Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, CCS 2017, Dallas, TX, USA, October 30 - November 03, 2017. ACM. https://doi.org/10.1145/3133956.
Velicheti, LMR, Feiock DC, Peiris M, Raje RR, Hill JH (2014) Towards modeling the behavior of static code analysis tools. In: Abercrombie RK McDonald JT (eds)Cyber and Information Security Research Conference, CISR ’14, Oak Ridge, TN, USA, April 8-10, 2014, 17–20.. ACM. https://doi.org/10.1145/2602087.2602101.
Velickovic, P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2017) Graph attention networks. CoRR:abs/1710.10903. http://arxiv.org/abs/1710.10903.
White, M, Tufano M, Vendome C, Poshyvanyk D (2016) Deep learning code fragments for code clone detection. In: Lo D, Apel S, Khurshid S (eds)Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, ASE 2016, Singapore, September 3-7, 2016, 87–98.. ACM. https://doi.org/10.1145/2970276.2970326.
Wu, S, Wang P, Li X, Zhang Y (2016) Effective detection of android malware based on the usage of data flow apis and machine learning. Inform Softw Technol 75:17–25. https://doi.org/10.1016/j.infsof.2016.03.004.
Article
Google Scholar
Wüchner, T, Ochoa M, Pretschner A (2015) Robust and effective malware detection through quantitative data flow graph metrics. In: Almgren M, Gulisano V, Maggi F (eds)Detection of Intrusions and Malware, and Vulnerability Assessment - 12th International Conference, DIMVA 2015, Milan, Italy, July 9-10, 2015, Proceedings, Springer, Lecture Notes in Computer Science, vol 9148, 98–118. https://doi.org/10.1007/978-3-319-20550-2_6.
Xu, X, Liu C, Feng Q, Yin H, Song L, Song D (2017) Neural network-based graph embedding for cross-platform binary code similarity detection In: (Thuraisingham et al. 2017), 363–376. https://doi.org/10.1145/3133956.3134018.
Yamaguchi, F, Golde N, Arp D, Rieck K (2014) Modeling and discovering vulnerabilities with code property graphs In: 2014 IEEE Symposium on Security and Privacy, SP 2014, Berkeley, CA, USA, May 18-21, 2014, 590–604.. IEEE Computer Society. https://doi.org/10.1109/SP.2014.44.
Zalewski, M (2017) American Fuzzy Lop. http://lcamtuf.coredump.cx/afl/.
Zhang, Y, Shen D, Wang G, Gan Z, Henao R, Carin L (2017) Deconvolutional paragraph representation learning In: (Guyon et al. 2017), 4169–4179. http://papers.nips.cc/paper/7005-deconvolutional-paragraph-representation-learning.
Zhou, Y, Liu S, Siow JK, Du X, Liu Y (2019) Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks(Wallach HM, Larochelle H, Beygelzimer A, d’Alché-Buc F, Fox EB, Garnett R, eds.), Vancouver. http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.
Zuo, F, Li X, Young P, Luo L, Zeng Q, Zhang Z (2019) Neural machine translation inspired binary code similarity comparison beyond function pairs In: 26th Annual Network and Distributed System Security Symposium, NDSS 2019, San Diego, California, USA, February 24-27, 2019, The Internet Society. https://www.ndss-symposium.org/ndss-paper/neural-machine-translation-inspired-binary-code-similarity-comparison-beyond-function-pairs/.