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SETTI: A Self-supervised AdvErsarial Malware DeTection ArchiTecture in an IoT Environment

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Abstract

In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practise available malware features for IoT devices and lack real-time prediction behaviours. More research is thus required on malware detection to cope with real-time misclassification of the input IoT data. Motivated by this, in this article, we propose an adversarial self-supervised architecture for detecting malware in IoT networks, SETTI, considering samples of IoT network traffic that may not be labeled. In the SETTI architecture, we design three self-supervised attack techniques, namely, Self-MDS, GSelf-MDS, and ASelf-MDS. The Self-MDS method considers the IoT input data and the adversarial sample generation in real-time. The GSelf-MDS builds a generative adversarial network model to generate adversarial samples in the self-supervised structure. Finally, ASelf-MDS utilises three well-known perturbation sample techniques to develop adversarial malware and inject it over the self-supervised architecture. Also, we apply a defence method to mitigate these attacks, namely, adversarial self-supervised training, to protect the malware detection architecture against injecting the malicious samples. To validate the attack and defence algorithms, we conduct experiments on two recent IoT datasets: IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the Self-MDS method has the most damaging consequences from the attacker’s point of view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the ASelf-MDS method is the most devastating algorithm that can plunge the accuracy rate from 98% to 77%.

REFERENCES

  1. [1] Alizadehsani Roohallah, Sharifrazi Danial, Izadi Navid Hoseini, Joloudari Javad Hassannataj, Shoeibi Afshin, Gorriz Juan M., Hussain Sadiq, Arco Juan E., Sani Zahra Alizadeh, Khozeimeh Fahime et al. 2021. Uncertainty-aware semi-supervised method using large unlabeled and limited labeled COVID-19 data. ACM Trans. Multim. Comput., Commun. Applic. 17, 3s (2021), 124.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Alletto Stefano, Abati Davide, Calderara Simone, Cucchiara Rita, and Rigazio Luca. 2018. Self-supervised optical flow estimation by projective bootstrap. IEEE Trans. Intell. Transport. Syst. 20, 9 (2018), 32943302.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. [3] Alqurashi Saja and Batarfi Omar. 2017. A comparison between API call sequences and opcode sequences as reflectors of malware behavior. In Proceedings of the 12th International Conference for Internet Technology and Secured Transactions (ICITST). IEEE, 105110.Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Alwassel Humam, Mahajan Dhruv, Korbar Bruno, Torresani Lorenzo, Ghanem Bernard, and Tran Du. 2020. Self-supervised learning by cross-modal audio-video clustering. Adv. Neural Inf. Process. Syst. 33 (2020).Google ScholarGoogle Scholar
  5. [5] Arshad Saba, Shah Munam A., Wahid Abdul, Mehmood Amjad, Song Houbing, and Yu Hongnian. 2018. SAMADroid: A novel 3-level hybrid malware detection model for android operating system. IEEE Access 6 (2018), 43214339.Google ScholarGoogle Scholar
  6. [6] Bazrafshan Zahra, Hashemi Hashem, Fard Seyed Mehdi Hazrati, and Hamzeh Ali. 2013. A survey on heuristic malware detection techniques. In Proceedings of the 5th Conference on Information and Knowledge Technology. IEEE, 113120.Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Bouguet Jean-Yves et al. 2001. Pyramidal implementation of the affine Lucas Kanade feature tracker description of the algorithm. Intel Corp. 5, 1–10 (2001), 4.Google ScholarGoogle Scholar
  8. [8] Chen Chao, Wang Yu, Zhang Jun, Xiang Yang, Zhou Wanlei, and Min Geyong. 2016. Statistical features-based real-time detection of drifted Twitter spam. IEEE Trans. Inf. Forens. Secur. 12, 4 (2016), 914925.Google ScholarGoogle ScholarCross RefCross Ref
  9. [9] Clark Kevin, Luong Minh-Thang, Le Quoc V., and Manning Christopher D.. 2020. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555 (2020).Google ScholarGoogle Scholar
  10. [10] Cruz Tiago, Rosa Luis, Proença Jorge, Maglaras Leandros, Aubigny Matthieu, Lev Leonid, Jiang Jianmin, and Simões Paulo. 2016. A cybersecurity detection framework for supervisory control and data acquisition systems. IEEE Trans. Industr. Inform. 12, 6 (2016), 22362246.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Dai Jifeng, He Kaiming, and Sun Jian. 2015. BoxSup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation. In Proceedings of the IEEE International Conference on Computer Vision. 16351643.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Dai Quanyu, Li Qiang, Tang Jian, and Wang Dan. 2018. Adversarial network embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarGoogle ScholarCross RefCross Ref
  13. [13] Deng Jia, Dong Wei, Socher Richard, Li Li-Jia, Li Kai, and Fei-Fei Li. 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 248255.Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Dib Mirabelle, Torabi Sadegh, Bou-Harb Elias, Bouguila Nizar, and Assi Chadi. 2022. EVOLIoT: A self-supervised contrastive learning framework for detecting and characterizing evolving IoT malware variants. In Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security. 452–466. Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Ding Ming, Tang Jie, and Zhang Jie. 2018. Semi-supervised learning on graphs with generative adversarial nets. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 913922.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. [16] Doersch Carl, Gupta Abhinav, and Efros Alexei A.. 2015. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE International Conference on Computer Vision. 14221430.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. [17] Donahue Jeff and Simonyan Karen. 2019. Large scale adversarial representation learning. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1054210552.Google ScholarGoogle Scholar
  18. [18] Dosovitskiy Alexey, Fischer Philipp, Springenberg Jost Tobias, Riedmiller Martin, and Brox Thomas. 2015. Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Trans. Patt. Anal. Mach. Intell. 38, 9 (2015), 17341747.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. [19] Duan Mingxing, Li Kenli, Deng Jiayan, Xiao Bin, and Tian Qi. 2022. A novel multi-sample generation method for adversarial attacks. ACM Trans. Multim. Comput., Commun. Applic. 18, 4 (2022), 121.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. [20] Gharib Amirhossein and Ghorbani Ali. 2017. DNA-Droid: A real-time Android ransomware detection framework. In Proceedings of the International Conference on Network and System Security. Springer, 184198.Google ScholarGoogle ScholarCross RefCross Ref
  21. [21] Dizaji Kamran Ghasedi, Wang Xiaoqian, and Huang Heng. 2018. Semi-supervised generative adversarial network for gene expression inference. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 14351444.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. [22] Gidaris Spyros, Singh Praveer, and Komodakis Nikos. 2018. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728 (2018).Google ScholarGoogle Scholar
  23. [23] Goodfellow Ian, Pouget-Abadie Jean, Mirza Mehdi, Xu Bing, Warde-Farley David, Ozair Sherjil, Courville Aaron, and Bengio Yoshua. 2014. Generative adversarial nets. In Proceedings of the Conference on Neural Information Processing Systems. 26722680.Google ScholarGoogle Scholar
  24. [24] Goodfellow Ian J., Shlens Jonathon, and Szegedy Christian. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014).Google ScholarGoogle Scholar
  25. [25] Guo Jianmin, Zhao Yue, Song Houbing, and Jiang Yu. 2020. Coverage guided differential adversarial testing of deep learning systems. IEEE Trans. Netw. Sci. Eng. 8, 2 (2020), 933942.Google ScholarGoogle ScholarCross RefCross Ref
  26. [26] Henaff Olivier. 2020. Data-efficient image recognition with contrastive predictive coding. In Proceedings of the International Conference on Machine Learning. PMLR, 41824192.Google ScholarGoogle Scholar
  27. [27] Hendrycks Dan and Dietterich Thomas. 2019. Benchmarking neural network robustness to common corruptions and perturbations. arXiv preprint arXiv:1903.12261 (2019).Google ScholarGoogle Scholar
  28. [28] Hendrycks Dan, Mazeika Mantas, Kadavath Saurav, and Song Dawn. 2019. Using self-supervised learning can improve model robustness and uncertainty. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1566315674.Google ScholarGoogle Scholar
  29. [29] Hendrycks Dan, Mazeika Mantas, Wilson Duncan, and Gimpel Kevin. 2018. Using trusted data to train deep networks on labels corrupted by severe noise. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1045610465.Google ScholarGoogle Scholar
  30. [30] Hjelm R. Devon, Fedorov Alex, Lavoie-Marchildon Samuel, Grewal Karan, Bachman Phil, Trischler Adam, and Bengio Yoshua. 2018. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670 (2018).Google ScholarGoogle Scholar
  31. [31] Hung Wei-Chih, Jampani Varun, Liu Sifei, Molchanov Pavlo, Yang Ming-Hsuan, and Kautz Jan. 2019. SCOPS: Self-supervised co-part segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 869878.Google ScholarGoogle ScholarCross RefCross Ref
  32. [32] Joshi Mandar, Chen Danqi, Liu Yinhan, Weld Daniel S., Zettlemoyer Luke, and Levy Omer. 2020. SpanBERT: Improving pre-training by representing and predicting spans. Trans. Assoc. Computat. Ling. 8 (2020), 6477.Google ScholarGoogle ScholarCross RefCross Ref
  33. [33] Khoreva Anna, Benenson Rodrigo, Hosang Jan, Hein Matthias, and Schiele Bernt. 2017. Simple does it: Weakly supervised instance and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 876885.Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Kim Dahun, Cho Donghyeon, Yoo Donggeun, and Kweon In So. 2018. Learning image representations by completing damaged jigsaw puzzles. In Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 793802.Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Kingma Durk P. and Dhariwal Prafulla. 2018. Glow: Generative flow with invertible 1x1 convolutions. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 1021510224.Google ScholarGoogle Scholar
  36. [36] Kurakin Alexey, Goodfellow Ian, and Bengio Samy. 2016. Adversarial examples in the physical world. arXiv preprint arXiv:1607.02533 (2016).Google ScholarGoogle Scholar
  37. [37] Larsson Gustav, Maire Michael, and Shakhnarovich Gregory. 2017. Colorization as a proxy task for visual understanding. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 68746883.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Li Zhenchuan, Liu Guanjun, and Jiang Changjun. 2020. Deep representation learning with full center loss for credit card fraud detection. IEEE Trans. Computat. Soc. Syst. 7, 2 (2020), 569579.Google ScholarGoogle ScholarCross RefCross Ref
  39. [39] Lieb David, Lookingbill Andrew, and Thrun Sebastian. 2005. Adaptive road following using self-supervised learning and reverse optical flow. In Robotics: Science and Systems. MIT Press, Cambridge, Massachusetts, 273280.Google ScholarGoogle Scholar
  40. [40] Liu Pengpeng, Lyu Michael, King Irwin, and Xu Jia. 2019. SelFlow: Self-supervised learning of optical flow. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 45714580.Google ScholarGoogle ScholarCross RefCross Ref
  41. [41] Lucas Bruce D., Kanade Takeo et al. 1981. An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th International Joint Conference on Artificial Intelligence. 674679.Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. [42] Madry Aleksander, Makelov Aleksandar, Schmidt Ludwig, Tsipras Dimitris, and Vladu Adrian. 2017. Towards deep learning models resistant to adversarial attacks. arXiv preprint arXiv:1706.06083 (2017).Google ScholarGoogle Scholar
  43. [43] Mahendran Aravindh, Thewlis James, and Vedaldi Andrea. 2018. Cross pixel optical-flow similarity for self-supervised learning. In Proceedings of the Asian Conference on Computer Vision. Springer, 99116.Google ScholarGoogle Scholar
  44. [44] Meidan Yair, Bohadana Michael, Mathov Yael, Mirsky Yisroel, Shabtai Asaf, Breitenbacher Dominik, and Elovici Yuval. 2018. N-BaIoT–network-based detection of IoT botnet attacks using deep autoencoders. IEEE Pervas. Comput. 17, 3 (2018), 1222.Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. [45] Meister Simon, Hur Junhwa, and Roth Stefan. 2018. Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Menze Moritz and Geiger Andreas. 2015. Object scene flow for autonomous vehicles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 30613070.Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Mugnai Daniele, Pernici Federico, Turchini Francesco, and Bimbo Alberto Del. 2022. Fine-grained adversarial semi-supervised learning. ACM Trans. Multim. Comput., Commun. Applic. 18, 1s (2022), 119.Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Naval Smita, Laxmi Vijay, Rajarajan Muttukrishnan, Gaur Manoj Singh, and Conti Mauro. 2015. Employing program semantics for malware detection. IEEE Trans. Inf. Forens. Secur. 10, 12 (2015), 25912604.Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. [49] Nettleton David F., Orriols-Puig Albert, and Fornells Albert. 2010. A study of the effect of different types of noise on the precision of supervised learning techniques. Artif. Intell. Rev. 33, 4 (2010), 275306.Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. [50] Oord Aaron van den, Li Yazhe, and Vinyals Oriol. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).Google ScholarGoogle Scholar
  51. [51] Papernot Nicolas et al. 2016. The limitations of deep learning in adversarial settings. In Proceedings of the IEEE European Symposium on Security and Privacy. 372387.Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Parmisano A., Garcia S., and Erquiaga M. J.. 2020. A Labeled Dataset with Malicious and Benign IoT Network Traffic. Stratosphere Laboratory, Praha, Czech Republic. Stratosphere Laboratory: Praha, Czech Republic.Google ScholarGoogle Scholar
  53. [53] Patrini Giorgio, Rozza Alessandro, Menon Aditya Krishna, Nock Richard, and Qu Lizhen. 2017. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 19441952.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Qin Zixuan, Yin Mengxiao, Li Guiqing, and Yang Feng. 2020. SP-Flow: Self-supervised optical flow correspondence point prediction for real-time SLAM. Comput.-aid. Geom. Des. 82 (2020), 101928.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Ren Zhe, Yan Junchi, Ni Bingbing, Liu Bin, Yang Xiaokang, and Zha Hongyuan. 2017. Unsupervised deep learning for optical flow estimation. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31.Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Sadeghzadeh Amir Mahdi, Shiravi Saeed, and Jalili Rasool. 2021. Adversarial network traffic: Towards evaluating the robustness of deep learning-based network traffic classification. IEEE Trans. Netw. Serv. Manag. 18, 2 (2021), 1962–1976.Google ScholarGoogle ScholarCross RefCross Ref
  57. [57] Saeed Aaqib, Salim Flora D., Ozcelebi Tanir, and Lukkien Johan. 2020. Federated self-supervised learning of multisensor representations for embedded intelligence. IEEE Internet Things J. 8, 2 (2020), 10301040.Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Schmidt Ludwig, Santurkar Shibani, Tsipras Dimitris, Talwar Kunal, and Madry Aleksander. 2018. Adversarially robust generalization requires more data. In Proceedings of the Conference on Advances in Neural Information Processing Systems. 50145026.Google ScholarGoogle Scholar
  59. [59] Song Yaguang, Yang Xiaoshan, and Xu Changsheng. 2022. Self-supervised calorie-aware heterogeneous graph networks for food recommendation. ACM Trans. Multim. Comput., Commun. Applic. (2022). Google ScholarGoogle ScholarCross RefCross Ref
  60. [60] Taheri Rahim, Javidan Reza, Shojafar Mohammad, Pooranian Zahra, Miri Ali, and Conti Mauro. 2020. On defending against label flipping attacks on malware detection systems. Neural Comput. Applic. 32, 18 (2020), 1478114800.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. [61] Taheri Rahim, Shojafar Mohammad, Alazab Mamoun, and Tafazolli Rahim. 2020. FED-IIoT: A robust federated malware detection architecture in industrial IoT. IEEE Trans. Industr. Inform. 17, 12 (2020), 8442–8452.Google ScholarGoogle Scholar
  62. [62] Wang Yude, Zhang Jie, Kan Meina, Shan Shiguang, and Chen Xilin. 2020. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1227512284.Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Xia Zhuoqun, Tan Jingjing, Gu Ke, Li Xiong, and Jia Weijia. 2021. SEDMDroid: An enhanced stacking ensemble framework for Android malware detection. IEEE Trans. Netw. Sci. Eng. 8, 2 (2021), 9951008.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Yang Yanchao, Loquercio Antonio, Scaramuzza Davide, and Soatto Stefano. 2019. Unsupervised moving object detection via contextual information separation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 879888.Google ScholarGoogle ScholarCross RefCross Ref
  65. [65] Yin Zhichao and Shi Jianping. 2018. GeoNet: Unsupervised learning of dense depth, optical flow and camera pose. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 19831992.Google ScholarGoogle ScholarCross RefCross Ref
  66. [66] Yu Lingjing, Luo Bo, Ma Jun, Zhou Zhaoyu, and Liu Qingyun. 2020. You are what you broadcast: Identification of mobile and IoT devices from (public) WiFi. In Proceedings of the 29th USENIX Security Symposium (USENIX Security’20). 5572.Google ScholarGoogle Scholar
  67. [67] Zhang Hongyang, Yu Yaodong, Jiao Jiantao, Xing Eric P., Ghaoui Laurent El, and Jordan Michael I.. 2019. Theoretically principled trade-off between robustness and accuracy. arXiv preprint arXiv:1901.08573 (2019).Google ScholarGoogle Scholar
  68. [68] Zhang Jing and Tao Dacheng. 2021. Empowering things with intelligence: A survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internet Things J. 8, 10 (2021), 7789–7817.Google ScholarGoogle Scholar
  69. [69] Zhang Zhilu and Sabuncu Mert. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. Adv. Neural Inf. Process. Syst. 31 (2018), 87788788.Google ScholarGoogle Scholar
  70. [70] Zhang Zhaoxi, Zhang Leo Yu, Zheng Xufei, Hu Shengshan, Tian Jinyu, and Zhou Jiantao. 2021. Self-supervised adversarial example detection by disentangled representation. arXiv preprint arXiv:2105.03689 (2021).Google ScholarGoogle Scholar
  71. [71] Zou Han, Zhou Yuxun, Yang Jianfei, and Spanos Costas J.. 2018. Unsupervised WiFi-enabled IoT device-user association for personalized location-based service. IEEE Internet Things J. 6, 1 (2018), 12381245.Google ScholarGoogle ScholarCross RefCross Ref

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        cover image ACM Transactions on Multimedia Computing, Communications, and Applications
        ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 18, Issue 2s
        June 2022
        383 pages
        ISSN:1551-6857
        EISSN:1551-6865
        DOI:10.1145/3561949
        • Editor:
        • Abdulmotaleb El Saddik
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        Publication History

        • Published: 6 October 2022
        • Online AM: 17 May 2022
        • Accepted: 6 May 2022
        • Revised: 2 April 2022
        • Received: 21 November 2021
        Published in tomm Volume 18, Issue 2s

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