Skip to main content
SpringerLink
Log in

Jointly beam stealing attackers detection and localization without training: an image processing viewpoint

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Recently revealed beam stealing attacks could greatly threaten the security and privacy of IEEE 802.11ad communications. The premise to restore normal network service is detecting and locating beam stealing attackers without their cooperation. Current consistency-based methods are only valid for one single attacker and are parameter-sensitive. From the viewpoint of image processing, this paper proposes an algorithm to jointly detect and locate multiple beam stealing attackers based on RSSI (Received Signal Strength Indicator) map without the training process involved in deep learning-based solutions. Firstly, an RSSI map is constructed based on interpolating the raw RSSI data for enabling high-resolution localization while reducing monitoring cost. Secondly, three image processing steps, including edge detection and segmentation, are conducted on the constructed RSSI map to detect and locate multiple attackers without any prior knowledge about the attackers. To evaluate our proposal’s performance, a series of experiments are conducted based on the collected data. Experimental results have shown that in typical parameter settings, our algorithm’s positioning error does not exceed 0.41 m with a detection rate no less than 91%.

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

Similar content being viewed by others

References

  1. Wu W, Cheng N, Zhang N, Yang P, Aldubaikhy K, Shen X. Performance analysis and enhancement of beamforming training in 802.11ad. IEEE Transactions on Vehicular Technology, 2020, 69(5): 5293–5306

    Article  Google Scholar 

  2. Steinmetzer D, Yuan Y, Hollick M. Beam-stealing: intercepting the sector sweep to launch man-in-the-middle attacks on wireless IEEE 802.11ad networks. In: Proceedings of the 11th ACM Conference on Security & Privacy in Wireless and Mobile Networks. 2018, 12–22

  3. Shen Z, Xu K, Xia X, Xie W, Zhang D. Spatial sparsity based secure transmission strategy for massive MIMO systems against simultaneous jamming and eavesdropping. IEEE Transactions on Information Forensics and Security, 2020, 15: 3760–3774

    Article  Google Scholar 

  4. Molina-Coronado B, Mori U, Mendiburu A, Miguel-Alonso J. Survey of network intrusion detection methods from the perspective of the knowledge discovery in databases process. IEEE Transactions on Network and Service Management, 2020, 17(4): 2451–2479

    Article  Google Scholar 

  5. Amma N G B, Selvakumar S, Velusamy R L. A statistical approach for detection of denial of service attacks in computer networks. IEEE Transactions on Network and Service Management, 2020, 17(4): 2511–2522

    Article  Google Scholar 

  6. Ahmed U, Lin J C W, Srivastava G. Generative ensemble learning for mitigating adversarial malware detection in IoT. In: Proceedings of the 29th IEEE International Conference on Network Protocols (ICNP). 2021, 1–5

  7. Ahmed U, Lin J C W, Srivastava G. Network-aware SDN load balancer with deep active learning based intrusion detection model. In: Proceedings of 2021 International Joint Conference on Neural Networks (IJCNN). 2021, 1–6

  8. Lin J C W, Srivastava G, Zhang Y, Djenouri Y, Aloqaily M. Privacy-preserving multiobjective sanitization model in 6G IoT environments. IEEE Internet of Things Journal, 2021, 8(7): 5340–5349

    Article  Google Scholar 

  9. Wei X, Tang C. Location consistency-based MITM attack detection in 802.11ad networks. In: Proceedings of the 11th International Symposium on Cyberspace Safety and Security. 2019, 18–29

  10. Yang Y, Wei X, Xu R, Peng L, Zhang L, Ge L. Man-in-the-middle attack detection and localization based on cross-layer location consistency. IEEE Access, 2020, 8: 103860–103874

    Article  Google Scholar 

  11. Li S, Hedley M, Bengston K, Humphrey D, Johnson M, Ni W. Passive localization of standard WiFi devices. IEEE Systems Journal, 2019, 13(4): 3929–3932

    Article  Google Scholar 

  12. Zhao Y, Xu J, Wu J, Hao J, Qian H. Enhancing camera-based multimodal indoor localization with device-free movement measurement using WiFi. IEEE Internet of Things Journal, 2020, 7(2): 1024–1038

    Article  Google Scholar 

  13. Grossi E, Lops M, Tulino A M, Venturino L. Extended target detection and localization in 802.11ad/y radars. In: Proceedings of 2020 IEEE Radar Conference (RadarConf20). 2020, 1–5

  14. Al-Rashdan W Y, Tahat A. A comparative performance evaluation of machine learning algorithms for fingerprinting based localization in DM-MIMO wireless systems relying on big data techniques. IEEE Access, 2020, 8: 109522–109534

    Article  Google Scholar 

  15. Prasad K N R S V, D’souza K B, Bhargava V K. A downscaled faster-RCNN framework for signal detection and time-frequency localization in wideband RF systems. IEEE Transactions on Wireless Communications, 2020, 19(7): 4847–4862

    Article  Google Scholar 

  16. Selim A, Paisana F, Arokkiam J A, Zhang Y, Doyle L, DaSilva L A. Spectrum monitoring for radar bands using deep convolutional neural networks. In: Proceedings of GLOBECOM 2017–2017 IEEE Global Communications Conference. 2017, 1–6

  17. Lees W M, Wunderlich A, Jeavons P J, Hale P D, Souryal M R. Deep learning classification of 3.5-GHz band spectrograms with applications to spectrum sensing. IEEE Transactions on Cognitive Communications and Networking, 2019, 5(2): 224–236

    Article  Google Scholar 

  18. Huang D, Shi X, Zhang W A. False data injection attack detection for industrial control systems based on both time-and frequency-domain analysis of sensor data. IEEE Internet of Things Journal, 2020, 8(1): 585–595

    Article  Google Scholar 

  19. Yoo J. Change detection of RSSI fingerprint pattern for indoor positioning system. IEEE Sensors Journal, 2020, 20(5): 2608–2615

    Article  Google Scholar 

  20. You Y, Wu C. Indoor positioning system with cellular network assistance based on received signal strength indication of beacon. IEEE Access, 2019, 8: 6691–6703

    Article  Google Scholar 

  21. Liu F, Liu J, Yin Y, Wang W, Hu D, Chen P, Niu Q. Survey on WiFi-based indoor positioning techniques. IET Communications, 2020, 14(9): 1372–1383

    Article  Google Scholar 

  22. Xue W, Li Q, Hua X, Yu K, Qiu W, Zhou B. A new algorithm for indoor RSSI radio map reconstruction. IEEE Access, 2018, 6: 76118–76125

    Article  Google Scholar 

  23. Tao Y, Zhao L. A novel system for WiFi radio map automatic adaptation and indoor positioning. IEEE Transactions on Vehicular Technology, 2018, 67(11): 10683–10692

    Article  Google Scholar 

  24. Katagiri K, Fujii T. Radio environment map updating procedure considering change of surrounding environment. In: Proceedings of 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). 2020, 1–6

  25. Zheng Y, Lin Z. The augmented homogeneous coordinates matrix-based projective mismatch removal for partial-duplicate image search. IEEE Transactions on Image Processing, 2019, 28(1): 181–193

    Article  MathSciNet  MATH  Google Scholar 

  26. Yang Y, Wei X, Xu R, Peng L, Liao Y, Ge L. Security-oriented indoor robots tracking: an object recognition viewpoint. Security and Communication Networks, 2021, 2021: 7456552

    Google Scholar 

  27. Lembo S, Horsmanheimo S, Somersalo M, Laukkanen M, Tuomimäki L, Huilla S. Enhancing WiFi RSS fingerprint positioning accuracy: lobe-forming in radiation pattern enabled by an air-gap. In: Proceedings of 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN). 2019, 1–8

  28. Draman N N C, Karim S A A, Hashim I. Scattered data interpolation using rational quartic triangular patches with three parameters. IEEE Access, 2020, 8: 44239–44262

    Article  Google Scholar 

  29. Zhuang H, Oh B S, Lin D, Toh K A, Lin Z P. Multicomponent signal decomposition using morphological operations. In: Proceedings of the 23rd IEEE International Conference on Digital Signal Processing (DSP). 2018, 1–5

  30. Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62–66

    Article  Google Scholar 

  31. Gong S, Li G, Zhang Y, Li C, Yu L. Application of static gesture segmentation based on an improved canny operator. The Journal of Engineering, 2019, 2019(15): 543–546

    Article  Google Scholar 

  32. Canny J. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679–698

    Article  Google Scholar 

  33. Bakkay M C, Chambon S, Rashwan H A, Lubat C, Barsotti S. Automatic detection of individual and touching moths from trap images by combining contour-based and region-based segmentation. IET Computer Vision, 2018, 12(2): 138–145

    Article  Google Scholar 

  34. Nassar M A, Hasan M, Khan M, Sultana M, Hasan M, Luxford L, Cole P, Oatley G, Koutsakis P. Wifi-based localisation datasets for No-GPS open areas using smart bins. Computer Networks, 2020, 180: 107422

    Article  Google Scholar 

  35. Zafari F, Gkelias A, Leung K K. A survey of indoor localization systems and technologies. IEEE Communications Surveys & Tutorials, 2019, 21(3): 2568–2599

    Article  Google Scholar 

  36. Ma R, Guo Q, Hu C, Xue J. An improved WiFi indoor positioning algorithm by weighted fusion. Sensors, 2015, 15(9): 21824–21843

    Article  Google Scholar 

  37. Xu H, Ding Y, Li P, Wang R, Li Y. An RFID indoor positioning algorithm based on Bayesian probability and K-nearest neighbor. Sensors, 2017, 17(8): 1806

    Article  Google Scholar 

  38. Bai Y, Berezovsky V, Popov V. Digital core 3D reconstruction based on micro-CT images via a deep learning method. In: Proceedings of 2020 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). 2020, 1–6

  39. Rosenfeld A, Kak A C. Digital Picture Processing. New York: Academic Press, 1976

    MATH  Google Scholar 

  40. Redmon J, Farhadi A. YOLOv3: an incremental improvement. 2018, arXiv preprint arXiv: 1804.02767

  41. Rupa C, Srivastava G, Bhattacharya S, Reddy P, Gadekallu T R. A machine learning driven threat intelligence system for malicious URL detection. In: Proceedings of the 16th International Conference on Availability, Reliability and Security. 2021, 154

  42. Ch R, Srivastava G, Gadekallu T R, Maddikunta P K R, Bhattacharya S. Security and privacy of UAV data using blockchain technology. Journal of Information Security and Applications, 2020, 55: 102670

    Article  Google Scholar 

  43. Chiramdasu R, Srivastava G, Bhattacharya S, Reddy P K, Gadekallu T R. Malicious URL detection using logistic regression. In: Proceedings of 2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS). 2021, 1–6

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61671471).

Author information

Authors and Affiliations

  1. College of Communication Engineer, Army Engineering University of PLA, Nanjing, 210000, China

    Yaoqi Yang, Renhui Xu & Laixian Peng

  2. The 63rd Research Institute, National University of Defense Technology, Nanjing, 210007, China

    Xianglin Wei & Yangang Wang

  3. Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China

    Weizheng Wang

Authors
  1. Yaoqi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xianglin Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renhui Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weizheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Laixian Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yangang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xianglin Wei or Renhui Xu.

Additional information

Yaoqi Yang received the MS degree from the College of Communications Engineering, Army Engineering University of PLA, China in 2021. His research interests include wireless communications systems and signal processing.

Xianglin Wei received the PhD degree from the PLA University of Science and Technology, China in 2012. He is currently working as a Researcher with the 63rd Research Institute, National University of Defense Technology, China. His research interests include mobile edge computing and wireless network optimization.

Renhui Xu received the PhD degree in communications and information systems from Southeast University, Nanjing, China in 2010. He is currently an Associate Professor with the College of Communications Engineering, Army Engineering University of PLA, China. His research interests include signal processing, and wireless ad hoc networks.

Weizheng Wang received the BS degree in software engineering from Yangzhou University, China in 2019, the MS degrees in computer science and engineering from the University of Aizu, Aizu-Wakamatsu, Japan in 2021. Now he is a Research Associate in University of Aizu and pursuing the PhD degree at the Department of Computer Science, City University of Hong Kong, China. His research interests include blockchain technology and IoT system.

Laixian Peng received the BS and PhD degrees in telecom engineering from the Nanjing Institute of Communications Engineering, China in 1999 and 2004, respectively. His research interests include high-speed switching architectures and ad hoc networks and applications. He was a recipient of the Excellence PhD Thesis Award of Jiangsu Province, in 2005.

Yangang Wang is currently working as a Researcher with the 63rd Research Institute, National University of Defense Technology, China. His research interests include mobile edge computing and wireless network optimization.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, Y., Wei, X., Xu, R. et al. Jointly beam stealing attackers detection and localization without training: an image processing viewpoint. Front. Comput. Sci. 17, 173704 (2023). https://doi.org/10.1007/s11704-022-1550-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-022-1550-6

Keywords

Search

Navigation