[1] C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, “DDoS in the IoT: Mirai and other botnets,” IEEE Computer, vol. 50, pp. 80–84, Jul. 2017.
[2] I. Makhdoom, M. Abolhasan, J. Lipman, R. P. Liu, and W. Ni, “Anatomy of threats to the Internet of Things,” IEEE Commun. Surveys Tuts., vol. 21, no. 2, pp. 1636–1675, Oct. 2018.
[3] S.-M. Cheng, P.-Y. Chen, C.-C. Lin, and H.-C. Hsiao, “Traffic-aware patching for cyber security in mobile IoT,” IEEE Commun. Mag., vol. 55, no. 7, pp. 29–35, Jul. 2017.
[4] A. D. Raju, I. Y. Abualhaol, R. S. Giagone, Y. Zhou, and S. Huang, “A survey on cross-architectural IoT malware threat hunting,” IEEE Access, vol. 9, pp. 91 686–91 709, Jun. 2021.
[5] Q.-D. Ngo, H.-T. Nguyen, V.-H. Lec, and D.-H. Nguyen, “A survey of IoT malware and detection methods based on static features,” ICT Express, vol. 6, no. 4, pp. 280–286, Dec. 2020.
[6] E. Raff, J. Barker, J. Sylvester, R. Brandon, B. Catanzaro, and C. Nicholas, “Malware detection by eating a whole EXE,” in Proc. AAAI 2018, Jun. 2018.
[7] H. S. Anderson and P. Roth, “EMBER: An open dataset for training static PE malware machine learning models,” arXiv preprint arXiv:1804.04637, Apr. 2018.
[8] J. Su, D. V. Vasconcellos, S. Prasad, D. Sgandurra, Y. Feng, and K. Sakurai, “Lightweight classification of IoT malware based on image recognition,” in Proc. IEEE COMPSAC 2018, Jul. 2018, pp. 664–669.
[9] X. Liu, Y. Lin, H. Li, and J. Zhang, “A novel method for malware detection on ML-based visualization technique,” Computers & Security, vol. 89, p. 101682, Feb. 2020.
[10] H. HaddadPajouh, A. Dehghantanha, R. Khayami, and K.-K. R. Choo, “A deep recurrent neural network based approach for internet of things malware threat hunting,” Future Generation Computer Systems, pp. 88–96, Aug. 2018.
[11] M. Alhanahnah, Q. Lin, Q. Yan, N. Zhang, and Z. Chen, “Efficient signature generation for classifying cross-architecture IoT malware,” in Proc. IEEE CNS 2018, May 2018.
[12] H. Alasmary, A. Khormali, A. Anwar, J. Park, J. Choi, A. Abusnaina, A. Awad, D. Nyang, and A. Mohaisen, “Analyzing and detecting emerging Internet of Things malware: A graph-based approach,” IEEE Internet Things J., vol. 6, no. 5, pp. 8977–8988, Oct. 2019.
[13] B. Wu, Y. Xu, and F. Zou, “Malware classification by learning semantic and structural features of control flow graphs,” in Proc. IEEE TrustCom 2021, Oct. 2021, pp. 540–547.
[14] C.-Y. Wu, T. Ban, S.-M. Cheng, B. Sun, and T. Takahashi, “IoT malware detection using function-call-graph embedding,” in Proc. IEEE PST 2021, Dec. 2021, pp. 1–9.
[15] I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv preprint arXiv:1412.6572, Mar. 2015.
[16] W. Fleshman, E. Raff, R. Zak, M. McLean, and C. Nicholas, “Static malware detection & subterfuge: Quantifying the robustness of machine learning and current anti-virus,” in Proc. IEEE MALWARE 2018, Oct. 2018, pp. 1–10.
[17] A. Abusnaina, A. Anwar, S. Alshamrani, A. Alabduljabbar, R. Jang, D. Nyang, and D. Mohaisen, “Systemically evaluating the robustness of ML-based IoT malware detectors,” in Proc. IEEE/IFIP DSN-S 2021, Jun. 2021, pp. 3–4.
[18] A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” arXiv preprint arXiv:1706.06083, Sep. 2019.
[19] D. Park, H. Khan, and B. Yener, “Generation & evaluation of adversarial examples for malware obfuscation,” in Proc. IEEE ICMLA 2019, Dec. 2019, pp. 1283–1290.
[20] X. Li, K. Qiu, C. Qian, and G. Zhao, “An adversarial machine learning method based on opcode n-grams feature in malware detection,” in Proc. IEEE DSC 2020, Jul. 2020, pp. 380–387.
[21] K. Lucas, M. Sharif, L. Bauer, M. K. Reiter, and S. Shintre, “Malware makeover: Breaking ML-based static analysis by modifying executable bytes,” in Proc. ACM Asia CCS 2021, May 2021, pp. 744–758.
[22] T.-Y. Chen, “Structural attack against graph-based IoT malware detection at assembly level,” Master, NTUST, Taipei, Taiwan, Jan. 2022.
[23] X. Chen, C. Li, D. Wang, S. Wen, J. Zhang, S. Nepal, Y. Xiang, and K. Ren, “Android HIV: A study of repackaging malware for evading machine-learning detection,” IEEE Trans. Inf. Forensics Security, vol. 15, pp. 987–1001, 2020.
[24] K. Zhao, H. Zhou, Y. Zhu, X. Zhan, K. Zhou, J. Li, L. Yu, W. Yuan, and X. Luo, “Structural attack against graph based android malware detection,” in Proc. ACM Asia CCS 2021, Nov. 2021, p. 3218–3235.
[25] A. Abusnaina, A. Khormali, H. Alasmary, J. Park, A. Anwar, and A. Mohaisen, “Adversarial learning attacks on graph-based IoT malware detection systems,” in Proc. IEEE ICDCS 2019, Jul. 2019, pp. 1296–1305.
[26] L. Demetrio, B. Biggio, G. Lagorio, F. Roli, and A. Armando, “Functionalitypreserving black-box optimization of adversarial windows malware,” IEEE Trans. Inf. Forensics Security, vol. 16, pp. 3469–3478, May 2021.
[27] M. Ebrahimi, N. Zhang, J. Hu, M. T. Raza, and H. Chen, “Binary black-box evasion attacks against deep learning-based static malware detectors with adversarial byte-level language model,” in Proc. AAAI Workshop on RSEML, Feb. 2021.
[28] L. Demetrio, S. E. Coull, B. Biggio, G. Lagorio, A. Armando, and F. Roli, “Adversarial EXEmples: A survey and experimental evaluation of practical attacks on machine learning for windows malware detection,” ACM Trans. Privacy and Security, vol. 24, no. 4, pp. 1–31, Nov. 2021.
[29] C. Yang, J. Xu, S. Liang, Y. Wu, Y. Wen, B. Zhang, and D. Meng, “DeepMal: maliciousness-preserving adversarial instruction learning against static malware detection,” Cybersecurity, vol. 4, May 2021.
[30] “Executable and linking format (ELF) specification version 1.2,” Tool Interface Standard (TIS), (1995, May). [Online]. Available: https://refspecs.linuxbase.org/elf/elf.pdf
[31] M. Krčál, O. Švec, M. Bálek, and O. Jašek, “Deep convolutional malware classifiers can learn from raw executables and labels only,” in Proc. ICLR Workshop 2018, Apr. 2018.
[32] W. Fleshman, E. Raff, J. Sylvester, S. Forsyth, and M. McLean, “Non-negative networks against adversarial attacks,” arXiv preprint arXiv:1806.06108, Jan. 2019.
[33] Z. Fang, J. Wang, J. Geng, and X. Kan, “Feature selection for malware detection based on reinforcement learning,” IEEE Access, vol. 7, pp. 176 177–176 187, Dec. 2019.
[34] T. Rezaei and A. Hamze, “An efficient approach for malware detection using PE header specifications,” in Proc. IEEE ICWR 2020, Apr. 2020, pp. 234–239.
[35] Y.-T. Lee, T. Ban, T.-L. Wan, S.-M. Cheng, R. Isawa, T. Takahashi, and D. Inoue, “Cross platform IoT-malware family classification based on printable strings,” in Proc. IEEE TrustCom 2020, Dec. 2020, pp. 775–784.
[36] E. M. Dovom, A. Azmoodeh, A. Dehghantanha, D. E. Newton, R. M. Parizi, and H. Karimipour, “Fuzzy pattern tree for edge malware detection and categorization in IoT,” Journal of Systems Architecture, vol. 97, pp. 1–7, Aug. 2019.
[37] C.-W. Tien, S.-W. Chen, T. Ban, and S.-Y. Kuo, “Machine learning framework to analyze IoT malware using ELF and opcode features,” Digital Threats: Research and Practice, vol. 1, no. 1, pp. 1–19, Mar. 2020.
[38] 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 Proc. NDSS Symposium 2014, Feb. 2014.
[39] L. Onwuzurike, E. Mariconti, P. Andriotis, E. D. Cristofaro, G. Ross, and G. Stringhini, “MaMaDroid: Detecting android malware by building markov chains of behavioral models (extended version),” ACM Trans. Privacy and Security, vol. 22, no. 2, Apr. 2019.
[40] N. Namani and A. Khan, “Symbolic execution based feature extraction for detection of malware,” in Proc. IEEE ICCCS 2020, Dec. 2020, pp. 1–6.
[41] X.-W. Wu, Y. Wang, Y. Fang, and P. Jia, “Embedding vector generation based on function call graph for effective malware detection and classification,” Neural Computing and Applications, pp. 1–14, Feb. 2022.
[42] T. N. Phu, L. Hoang, N. N. Toan, N. D. Tho, and N. N. Binh, “C500-CFG: A novel algorithm to extract control flow-based features for IoT malware detection,” in Proc. IEEE ISCIT 2019, Sep. 2019, pp. 568–573.
[43] L.-B. Ouyang, “Robustness evaluation of graph-based malware detection using code-level adversarial attack with explainability,” Master, NTUST, Taipei, Taiwan, Jul. 2021.
[44] H.-T. Nguyen, Q.-D. Ngo4, and V.-H. Le, “A novel graph-based approach for IoT botnet detection,” International Journal of Information Security, vol. 19, no. 5, pp. 567–577, Oct. 2020.
[45] S. Gülmez and I. Sogukpinar, “Graph-based malware detection using opcode sequences,” in Proc. IEEE ISDFS 2021, Jun. 2021, pp. 1–5.
[46] A. Pektaş and T. Acarman, “Deep learning for effective Android malware detection using API call graph embeddings,” Soft Computing, vol. 24, no. 2, pp. 1027–1043, Jan. 2020.
[47] K. Simonyan and A. Zisserman, “Very deep convolutional networks for largescale image recognition,” arXiv preprint arXiv:1409.1556, Apr. 2015.
[48] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE CVPR 2016, Dec. 2016, pp. 770–778.
[49] N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in Proc. IEEE S&P 2017, May 2017, pp. 39–57.
[50] F. Pierazzi, F. Pendlebury, J. Cortellazzi, and L. Cavallaro., “Intriguing properties of adversarial ML attacks in the problem space,” in Proc. IEEE S&P 2020, May 2020, p. 1332–1349.
[51] A. Abusnaina, H. Alasmary, M. Abuhamad, S. Salem, D. Nyang, and A. Mohaisen, “Subgraph-based adversarial examples against graph-based IoT malware detection systems,” in Proc. Computational Data and Social Networks 2019, Nov. 2019, pp. 268–281.
[52] L. Zhang, P. Liu, Y.-H. Choi, and P. Chen, “Semantics-preserving reinforcement learning attack against graph neural networks for malware detection,” IEEE Trans. Dependable Secure Comput., Mar. 2022.
[53] S. M.Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in Proc. NeurIPS 2017, vol. 30, Dec. 2017, pp. 4768–4777.
[54] T. Ribeiro, S. Singh, and C. Guestrin, “”Why should I trust you?” Explaining the predictions of any classifier,” in Proc. ACM SIGKDD 2016, Aug. 2016, pp. 1135–1144.
[55] L. Demetrio, B. Biggio, G. Lagorio, F. Roli, and A. Armando, “Explaining vulnerabilities of deep learning to adversarial malware binaries,” arXiv preprint arXiv:1901.03583, Jan. 2019.
[56] I. Rosenberg, S. Meir, J. Berrebi, I. Gordon, G. Sicard, and E. O. David, “Generating end-to-end adversarial examples for malware classifiers using explainability,” in Proc. IEEE IJCNN 2020, Jul. 2020, pp. 1–10.
[57] I. G. Nicolas Papernot, Patrick McDaniel, “Transferability in machine learning: From phenomena to black-box attacks using adversarial samples,” arXiv preprint arXiv:1605.07277, May 2016.
[58] Y. Shoshitaishvili, R. Wang, C. Salls, N. Stephens, M. Polino, A. Dutcher, J. Grosen, S. Feng, C. Hauser, C. Kruegel, and G. Vigna, “SoK: (State of) The Art of War: Offensive Techniques in Binary Analysis,” in Proc. IEEE S&P 2016, May 2016, pp. 138–157.
[59] A. Hagberg, P. Swart, and D. Chult, “Exploring network structure, dynamics, and function using NetworkX,” in Proc. SciPy 2008, Aug. 2008, p. 11–15.