Skip to main content
SpringerLink
Log in

DockerWatch: a two-phase hybrid detection of malware using various static features in container cloud

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

As an emerging virtualization technology, the Linux container provides a more lightweight, flexible, and high-performance operating-system-level virtual run-time environment. Its appearance has profoundly changed the development and deployment of multi-tier distributed applications. However, the imperfect system resource isolation features and the kernel-sharing mechanism will introduce significant security risks to the cloud platform. In this paper, we present DockerWatch, a real-time detection system for malware detection in the container-based cloud platform. DockerWatch uses a non-intrusive manner to extract executable files inside the containers, then uses the ensemble of various static features and behavior-based graphs as the analysis vector to learn the robust representations of malicious patterns. Consequently, a two-phase hybrid detection method based on deep learning is proposed to accelerate and enhance the detection performance, aiming to address the trade-off between fast and high-performance real-time detection. Extensive experiments are conducted and compared with extensive existing related methods using real-world datasets to validate the effectiveness of our system. The results show that DockerWatch achieves excellent detection performance with acceptable run-time performance overhead introduced into the platform.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Enquiries about data availability should be directed to the authors.

Notes

  1. The Anchore project can be found on GitHub: https://github.com/anchore/anchore-engine.

  2. The Clair project can be found on GitHub: https://github.com/quay/clair.

  3. The UnixBench project can be found on GitHub: https://github.com/kdlucas/byte-unixbench.

References

  • Aafer Y, Du W, Yin H (2013) Droidapiminer: mining api-level features for robust malware detection in android. In: International conference on security and privacy in communication systems, Springer, pp 86–103

  • Abadi M, Barham P, Chen J, et al (2016) Tensorflow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp 265–283

  • Abed AS, Clancy TC, Levy DS (2015) Applying bag of system calls for anomalous behavior detection of applications in linux containers. In: 2015 IEEE globecom workshops (GC Wkshps), pp 1–5. https://doi.org/10.1109/GLOCOMW.2015.7414047

  • Alasmary H, Khormali A, Anwar A et al (2019) Analyzing and detecting emerging internet of things malware: a graph-based approach. IEEE Internet Things J 6(5):8977–8988. https://doi.org/10.1109/JIOT.2019.2925929

    Article  Google Scholar 

  • Amer E, Zelinka I, El-Sappagh S (2021) A multi-perspective malware detection approach through behavioral fusion of api call sequence. Comput Secur 110:102449

    Article  Google Scholar 

  • Cai M, Jiang Y, Gao C et al (2021) Learning features from enhanced function call graphs for android malware detection. Neurocomputing 423:301–307. https://doi.org/10.1016/j.neucom.2020.10.054

    Article  Google Scholar 

  • Chen D, Zhang N, Cheng N et al (2018) Physical layer based message authentication with secure channel codes. IEEE Trans Dependable Secur Comput 17(5):1079–1093

    Article  Google Scholar 

  • Chen D, Zhao Z, Qin X et al (2020) Magleak: a learning-based side-channel attack for password recognition with multiple sensors in iiot environment. IEEE Trans Ind Inf 18(1):467–476

    Article  Google Scholar 

  • Chhikara P, Tekchandani R, Kumar N et al (2020) An efficient container management scheme for resource constrained intelligent iot devices. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.3037181

    Article  Google Scholar 

  • Combe T, Martin A, Di Pietro R (2016) To docker or not to docker: a security perspective. IEEE Cloud Comput 3(5):54–62. https://doi.org/10.1109/MCC.2016.100

    Article  Google Scholar 

  • Cozzi E, Graziano M, Fratantonio Y, et al (2018) Understanding linux malware. In: 2018 IEEE symposium on security and privacy (SP), pp 161–175. https://doi.org/10.1109/SP.2018.00054

  • Cui Z, Xue F, Cai X et al (2018) Detection of malicious code variants based on deep learning. IEEE Trans Ind Inf 14(7):3187–3196. https://doi.org/10.1109/TII.2018.2822680

    Article  Google Scholar 

  • Ding F, Zhu G, Alazab M, et al (2020) Deep-learning-empowered digital forensics for edge consumer electronics in 5g hetnets. IEEE Consum Electron Mag

  • Du Q, Xie T, He Y (2018) Anomaly detection and diagnosis for container-based microservices with performance monitoring. In: International conference on algorithms and architectures for parallel processing, Springer, pp 560–572

  • Elhadi AE, Maarof MA, Barry BI et al (2014) Enhancing the detection of metamorphic malware using call graphs. Comput Secur 46:62–78

    Article  Google Scholar 

  • Felter W, Ferreira A, Rajamony R, et al (2015) An updated performance comparison of virtual machines and linux containers. In: 2015 IEEE international symposium on performance analysis of systems and software (ISPASS), pp 171–172. https://doi.org/10.1109/ISPASS.2015.7095802

  • Flora J, Antunes N (2019) Studying the applicability of intrusion detection to multi-tenant container environments. In: 2019 15th european dependable computing conference (EDCC), IEEE, pp 133–136

  • Gantikow H, Zöhner T, Reich C (2020) Container anomaly detection using neural networks analyzing system calls. In: 2020 28th euromicro international conference on parallel. Distributed and network-based processing (PDP), IEEE, pp 408–412

  • Gao X, Gu Z, Kayaalp M, et al (2017) Containerleaks: Emerging security threats of information leakages in container clouds. In: 2017 47th Annual IEEE/IFIP international conference on dependable systems and networks (DSN), pp 237–248, https://doi.org/10.1109/DSN.2017.49

  • Gao X, Steenkamer B, Gu Z et al (2021) A study on the security implications of information leakages in container clouds. IEEE Trans Dependable Secur Comput 18(1):174–191. https://doi.org/10.1109/TDSC.2018.2879605

    Article  Google Scholar 

  • Guo Z, Yu K, Jolfaei A, et al (2021) Fuz-spam: Label smoothing-based fuzzy detection of spammers in internet of things. IEEE Trans Fuzzy Syst

  • Han KS, Lim JH, Kang B et al (2015) Malware analysis using visualized images and entropy graphs. Int J Inf Secur 14(1):1–14

    Article  Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybern 6:610–621

    Article  Google Scholar 

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning, PMLR, pp 448–456

  • Ji S, Ye K, Xu CZ (2019) Cmonitor: a monitoring and alarming platform for container-based clouds. In: International conference on cloud computing, Springer, pp 324–339

  • Kang B, Yerima SY, McLaughlin K, et al (2016) N-opcode analysis for android malware classification and categorization. In: 2016 International conference on cyber security and protection of digital services (cyber security), IEEE, pp 1–7

  • Karn RR, Kudva P, Huang H et al (2021) Cryptomining detection in container clouds using system calls and explainable machine learning. IEEE Trans Parallel Distrib Syst 32(3):674–691. https://doi.org/10.1109/TPDS.2020.3029088

    Article  Google Scholar 

  • Kaur K, Dhand T, Kumar N et al (2017) Container-as-a-service at the edge: trade-off between energy efficiency and service availability at fog nano data centers. IEEE Wirel Commun 24(3):48–56. https://doi.org/10.1109/MWC.2017.1600427

    Article  Google Scholar 

  • Kim T, Kang B, Rho M et al (2019) A multimodal deep learning method for android malware detection using various features. IEEE Trans Inf Forensics Secur 14(3):773–788. https://doi.org/10.1109/TIFS.2018.2866319

    Article  Google Scholar 

  • Kwon I, Im EG (2017) Extracting the representative api call patterns of malware families using recurrent neural network. In: Proceedings of the international conference on research in adaptive and convergent systems, pp 202–207

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  • Lin X, Lei L, Wang Y, et al (2018) A measurement study on linux container security: attacks and countermeasures. In: Proceedings of the 34th annual computer security applications conference, pp 418–429

  • Mikolov T, Sutskever I, Chen K, et al (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

  • Nataraj L, Karthikeyan S, Jacob G, et al (2011) Malware images: visualization and automatic classification. In: Proceedings of the 8th international symposium on visualization for cyber security, pp 1–7

  • Nix R, Zhang J (2017) Classification of android apps and malware using deep neural networks. In: 2017 International joint conference on neural networks (IJCNN), IEEE, pp 1871–1878

  • Ojala T, Pietikäinen M, Mäenpää T (2000) Gray scale and rotation invariant texture classification with local binary patterns. In: European conference on computer vision, Springer, pp 404–420

  • Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  MATH  Google Scholar 

  • Pascanu R, Stokes JW, Sanossian H et al (2015) Malware classification with recurrent networks. In: 2015 IEEE international conference on acoustics. Speech and signal processing (ICASSP), IEEE, pp 1916–1920

  • Quigley D, Sipek J, Wright CP, et al (2006) Unionfs: User-and community-oriented development of a unification filesystem. In: Proceedings of the 2006 linux symposium, pp 349–362

  • Santos I, Brezo F, Ugarte-Pedrero X et al (2013) Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci 231:64–82

    Article  MathSciNet  Google Scholar 

  • Saracino A, Sgandurra D, Dini G et al (2018) Madam: effective and efficient behavior-based android malware detection and prevention. IEEE Trans Dependable Secur Comput 15(1):83–97. https://doi.org/10.1109/TDSC.2016.2536605

    Article  Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Article  MathSciNet  MATH  Google Scholar 

  • Sharma A, Dash SK (2014) Mining api calls and permissions for android malware detection. In: International conference on cryptology and network security, Springer, pp 191–205

  • Shoshitaishvili Y, Wang R, Salls C, et al (2016) Sok: (state of) the art of war: offensive techniques in binary analysis. In: 2016 IEEE symposium on security and privacy (SP), pp 138–157, https://doi.org/10.1109/SP.2016.17

  • Srinivasan S, Kumar A, Mahajan M, et al (2018) Probabilistic real-time intrusion detection system for docker containers. In: International symposium on security in computing and communication, Springer, pp 336–347

  • Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  • Sun G, Qian Q (2021) Deep learning and visualization for identifying malware families. IEEE Trans Dependable Secur Comput 18(1):283–295. https://doi.org/10.1109/TDSC.2018.2884928

    Article  MathSciNet  Google Scholar 

  • Tunde-Onadele O, He J, Dai T, et al (2019) A study on container vulnerability exploit detection. In: 2019 ieee international conference on cloud engineering (IC2E), pp 121–127. https://doi.org/10.1109/IC2E.2019.00026

  • VirusShare (2011) Analyze suspicious files, domains, ips and urls to detect malware and other breaches, automatically share them with the security community. [EB/OL], https://virusshare.com/ Accessed July 09, 2021

  • VirusTotal (2004) A repository of malware samples to provide security researchers. [EB/OL], https://www.virustotal.com/gui/ Accessed July 09, 2021

  • Wang Q, Chen X, Jin X, et al (2021) Enhancing trustworthiness of internet of vehicles in space-air-ground integrated networks: attestation approach. IEEE Internet Things J

  • Wang Y, Wang Q, Chen X et al (2022) Containerguard: a real-time attack detection system in container-based big data platform. IEEE Trans Ind Inf 18(5):3327–3336. https://doi.org/10.1109/TII.2020.3047416

    Article  Google Scholar 

  • Yin L, Luo J, Luo H (2018) Tasks scheduling and resource allocation in fog computing based on containers for smart manufacturing. IEEE Trans Ind Inf 14(10):4712–4721. https://doi.org/10.1109/TII.2018.2851241

    Article  Google Scholar 

  • Yu K, Tan L, Mumtaz S et al (2021) Securing critical infrastructures: deep-learning-based threat detection in iiot. IEEE Commun Mag 59(10):76–82

    Article  Google Scholar 

  • Yu K, Tan L, Yang C, et al (2021b) A blockchain-based shamir’s threshold cryptography scheme for data protection in industrial internet of things settings. IEEE Internet Things J

  • Yuan Z, Lu Y, Wang Z, et al (2014) Droid-sec: deep learning in android malware detection. In: Proceedings of the 2014 ACM conference on SIGCOMM, pp 371–372

  • Zhang J, Qin Z, Yin H, et al (2016a) Irmd: malware variant detection using opcode image recognition. In: 2016 IEEE 22nd international conference on parallel and distributed systems (ICPADS), IEEE, pp 1175–1180

  • Zhang J, Qin Z, Yin H, et al (2016b) Malware variant detection using opcode image recognition with small training sets. In: 2016 25th International conference on computer communication and networks (ICCCN), IEEE, pp 1–9

  • Zhang J, Qin Z, Yin H et al (2019) A feature-hybrid malware variants detection using cnn based opcode embedding and bpnn based api embedding. Comput Secur 84:376–392

    Article  Google Scholar 

  • Zhang N, Cheng N, Gamage AT et al (2015) Cloud assisted hetnets toward 5g wireless networks. IEEE Commun Mag 53(6):59–65

    Article  Google Scholar 

  • Zhang X, Zhang Y, Zhong M, et al (2020) 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. Association for computing machinery, New York, NY, USA, CCS ’20, p 757–770. https://doi.org/10.1145/3372297.3417291,

  • Zhu HJ, You ZH, Zhu ZX et al (2018) Droiddet: effective and robust detection of android malware using static analysis along with rotation forest model. Neurocomputing 272:638–646

    Article  Google Scholar 

  • Zou Z, Xie Y, Huang K et al (2019) A docker container anomaly monitoring system based on optimized isolation forest. IEEE Trans Cloud Comput. https://doi.org/10.1109/TCC.2019.2935724

    Article  Google Scholar 

  • Zou Z, Xie Y, Huang K, et al (2019b) A docker container anomaly monitoring system based on optimized isolation forest. IEEE Trans Cloud Comput

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant U19A2081.

Funding

The authors have not disclosed any funding.

Author information

Authors and Affiliations

  1. School of Cyber Science and Engineering, Sichuan University, Chengdu, 610065, Sichuan, China

    Yulong Wang, Qixu Wang & Xingshu Chen

  2. Cyber Science Research Institute, Sichuan University, Chengdu, 610065, Sichuan, China

    Qixu Wang & Xingshu Chen

  3. Institute of Computer Application, China Academy of Engineering Physics, Mianyang, 621900, Sichuan, China

    Yulong Wang, Bangzhou Xin & Run Yang

  4. Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada

    Xue Qin

Authors
  1. Yulong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Qixu Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xue Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xingshu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bangzhou Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Run Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingshu Chen.

Ethics declarations

Conflict of interest

The authors of this article declare that they have no conflict of interest.

Ethical approval

The authors of this article declare that this article does not contain any studies with human participants or animals.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Y., Wang, Q., Qin, X. et al. DockerWatch: a two-phase hybrid detection of malware using various static features in container cloud. Soft Comput 27, 1015–1031 (2023). https://doi.org/10.1007/s00500-022-07546-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07546-2

Keywords

Search

Navigation