LAMD: Context-driven Android Malware Detection and Classification with LLMs

M. Weiser, “Program slicing,” IEEE Transactions on software engineering , no. 4, pp. 352–357, 1984

1984

Earlier work this paper cites.

M. L. Menéndez, J. Pardo, L. Pardo, and M. Pardo, “The jensen-shannon divergence,” Journal of the Franklin Institute , vol. 334, no. 2, pp. 307–318, 1997

1997

Earlier work this paper cites.

K. W. Y. Au, Y. F. Zhou, Z. Huang, and D. Lie, “Pscout: analyzing the android permission specification,” in Proceedings of the 2012 ACM conference on Computer and communications security , 2012, pp. 217–228

2012

Earlier work this paper cites.

D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens, “Drebin: Effective and explainable detection of android malware in your pocket.” in Ndss , vol. 14, 2014, pp. 23–26

2014

Earlier work this paper cites.

S. Rasthofer, S. Arzt, and E. Bodden, “A machine-learning approach for classifying and categorizing android sources and sinks.” in NDSS , vol. 14, no. 1125, 2014

2014

Earlier work this paper cites.

S. Arzt, S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein, Y. Le Traon, D. Octeau, and P. McDaniel, “Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for android apps,” ACM sigplan notices , vol. 49, no. 6, pp. 259–269, 2014

2014

Earlier work this paper cites.

R. Jordaney, K. Sharad, S. Dash, Z. Wang, D. Papini, I. Nouretdinov, and L. Cavallaro, “Transcend: Detecting concept drift in malware classification models,” USENIX Security Symposium,USENIX Security Symposium , Dec 2016

2016

Earlier work this paper cites.

A. Hindle, E. T. Barr, M. Gabel, Z. Su, and P. Devanbu, “On the naturalness of software,” Communications of the ACM , vol. 59, no. 5, pp. 122–131, 2016

2016

Earlier work this paper cites.

K. Allix, T. F. Bissyandé, J. Klein, and Y. Le Traon, “Androzoo,” in Proceedings of the 13th International Conference on Mining Software Repositories , May 2016. [Online]. Available: http://dx.doi.org/10.1145/2901739.2903508

2016

Earlier work this paper cites.

B. Miller, A. Kantchelian, M. C. Tschantz, S. Afroz, R. Bachwani, R. Faizullabhoy, L. Huang, V. Shankar, T. Wu, G. Yiu et al. , “Reviewer integration and performance measurement for malware detection,” in Detection of Intrusions and Malware, and Vulnerability Assessment: 13th International Conference, DIMVA 2016, San Sebastián, Spain, July 7-8, 2016, Proceedings 13 . Springer, 2016, pp. 122–141

2016

Earlier work this paper cites.

K. Grosse, N. Papernot, P. Manoharan, M. Backes, and P. McDaniel, “Adversarial examples for malware detection,” in Computer Security–ESORICS 2017: 22nd European Symposium on Research in Computer Security, Oslo, Norway, September 11-15, 2017, Proceedings, Part II 22 . Springer, 2017, pp. 62–79

2017

Earlier work this paper cites.

N. McLaughlin, J. Martinez del Rincon, B. Kang, S. Yerima, P. Miller, S. Sezer, Y. Safaei, E. Trickel, Z. Zhao, A. Doupé et al. , “Deep android malware detection,” in Proceedings of the seventh ACM on conference on data and application security and privacy , 2017, pp. 301–308

2017

Earlier work this paper cites.

M. Hurier, G. Suarez-Tangil, S. K. Dash, T. F. Bissyandé, Y. L. Traon, J. Klein, and L. Cavallaro, “Euphony: harmonious unification of cacophonous anti-virus vendor labels for android malware,” in Proceedings of the 14th International Conference on Mining Software Repositories . IEEE Press, 2017, pp. 425–435

2017

Earlier work this paper cites.

F. Pendlebury, F. Pierazzi, R. Jordaney, J. Kinder, and L. Cavallaro, “ { \{ TESSERACT } \} : Eliminating experimental bias in malware classification across space and time,” in 28th USENIX security symposium (USENIX Security 19) , 2019, pp. 729–746

2019

Earlier work this paper cites.

K. Xu, Y. Li, R. Deng, K. Chen, and J. Xu, “Droidevolver: Self-evolving android malware detection system,” in 2019 IEEE European Symposium on Security and Privacy (EuroS&P) . IEEE, 2019, pp. 47–62

2019

Earlier work this paper cites.

Y. Wu, X. Li, D. Zou, W. Yang, X. Zhang, and H. Jin, “Malscan: Fast market-wide mobile malware scanning by social-network centrality analysis,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 139–150

2019

Earlier work this paper cites.

X. Zhang, Y. Zhang, M. Zhong, D. Ding, Y. Cao, Y. Zhang, M. Zhang, and M. Yang, “Enhancing state-of-the-art classifiers with api semantics to detect evolved android malware,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security , Oct 2020. [Online]. Available: http://dx.doi.org/10.1145/3372297.3417291

2020

Earlier work this paper cites.

A. Wang, K. Cho, and M. Lewis, “Asking and answering questions to evaluate the factual consistency of summaries,” arXiv preprint arXiv:2004.04228 , 2020

Original

2020

Earlier work this paper cites.

Z. Kan, F. Pendlebury, F. Pierazzi, and L. Cavallaro, “Investigating labelless drift adaptation for malware detection,” in Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security , 2021, pp. 123–134

2021

Earlier work this paper cites.

F. Barbero, F. Pendlebury, F. Pierazzi, and L. Cavallaro, “Transcending transcend: Revisiting malware classification in the presence of concept drift,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2022, pp. 805–823

2022

Earlier work this paper cites.

T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa, “Large language models are zero-shot reasoners,” Advances in neural information processing systems , vol. 35, pp. 22 199–22 213, 2022

2022

Earlier work this paper cites.

D. Angioni, L. Demetrio, M. Pintor, and B. Biggio, “Robust machine learning for malware detection over time,” arXiv preprint arXiv:2208.04838 , 2022

Original

2022

Cited alongside, same era.

V. Y. F. Tan, P. L.A., and K. Jagannathan, “Recent advances in concept drift adaptation methods for deep learning,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence , Jun 2022. [Online]. Available: http://dx.doi.org/10.24963/ijcai.2022/784

2022

Cited alongside, same era.

M. K. Belaid, E. Hüllermeier, M. Rabus, and R. Krestel, “Do we need another explainable ai method? toward unifying post-hoc xai evaluation methods into an interactive and multi-dimensional benchmark,” arXiv preprint arXiv:2207.14160 , 2022

Original

2022

Cited alongside, same era.

S. Sarsa, P. Denny, A. Hellas, and J. Leinonen, “Automatic generation of programming exercises and code explanations using large language models,” in Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1 , 2022, pp. 27–43

T. Ahmed, K. S. Pai, P. Devanbu, and E. Barr, “Automatic semantic augmentation of language model prompts (for code summarization),” in Proceedings of the IEEE/ACM 46th International Conference on Software Engineering , 2024, pp. 1–13

2024

Later among the works it cites.

Y. Sun, D. Wu, Y. Xue, H. Liu, W. Ma, L. Zhang, Y. Liu, and Y. Li, “Llm4vuln: A unified evaluation framework for decoupling and enhancing llms’ vulnerability reasoning,” arXiv preprint arXiv:2401.16185 , 2024

Original

2024

Later among the works it cites.

S. Ullah, M. Han, S. Pujar, H. Pearce, A. Coskun, and G. Stringhini, “Llms cannot reliably identify and reason about security vulnerabilities (yet?): A comprehensive evaluation, framework, and benchmarks,” in IEEE Symposium on Security and Privacy , 2024

2024

Later among the works it cites.

P. M. S. Sánchez, A. H. Celdrán, G. Bovet, and G. M. Pérez, “Transfer learning in pre-trained large language models for malware detection based on system calls,” arXiv preprint arXiv:2405.09318 , 2024

Original