Skip to main content
SpringerLink
Log in

Security Approaches in Machine Learning for Satellite Communication

  • Chapter
  • First Online:
Book cover Machine Learning and Data Mining in Aerospace Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 836))

Abstract

The emerging technical approach Machine Learning (ML) is apprehensive with the design and growth of algorithms and techniques that allocate computers to “learn”. The major focus of ML research is to extract information from data automatically, by computational and statistical methods. It is thus closely related to data mining and statistics. The power of neural networks stems from their representation capability. In many applications including current discussion of security in satellite communication, feed forward networks are proved to offer the capability of universal function approximation. This chapter thrashes out in details and highlights on important technical issues during machine learning strategies in developing satellite communication systems.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. A. Gharanjik, M.R.B. Shankar, F. Zimmer, B. Ottersten, Centralized rainfall estimation using carrier to noise of satellite communication links. IEEE J. Sel. Areas Commun. 36(5), 1065–1073 (2018). https://doi.org/10.1109/jsac.2018.2832798

    Article  Google Scholar 

  2. L. Bragilevsky, I.V. Bajić, “Deep learning for Amazon satellite image analysis, in IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM), (Victoria, BC, 2017) pp. 1–5. https://doi.org/10.1109/pacrim.2017.8121895

  3. Y. Xin et al., Machine learning and deep learning methods for cybersecurity. IEEE Access 6, 35365–35381 (2018). https://doi.org/10.1109/access.2018.2836950

    Article  Google Scholar 

  4. Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu, V.C.M. Leung, A survey on security threats and defensive techniques of machine learning: a data driven view. IEEE Access 6, 12103–12117 (2018)

    Article  Google Scholar 

  5. M. Rath, B. Pati, C.R. Panigrahi, J.L. Sarkar, QTM: A QoS task monitoring system for mobile ad hoc networks, in Recent Findings in Intelligent Computing Techniques, ed by P. Sa, S. Bakshi, I. Hatzilygeroudis, M. Sahoo. Advances in Intelligent Systems and Computing, vol 707 (Springer, Singapore, 2019)

    Google Scholar 

  6. M. Rath, B. Pati, B.K. Pattanayak, An overview on social networking: design, issues, emerging trends,and security, in Social Network Analytics: Computational Research Methods and Techniques, (Academic Press, Elsevier, 2018), pp. 21–47

    Chapter  Google Scholar 

  7. M. Rath, J. Swain, B. Pati, B.K. Pattanayak,”Attacks and Control in MANET, in Handbook of Research on Network Forensics and Analysis Techniques. IGI Global, (2018), pp. 19–37

    Google Scholar 

  8. M. Rath, B. Pati, B.K. Pattanayak, Energy efficient MANET protocol using cross layer design for military applications. Defense Sci. J. 66(2) (DRDO Publication, 2016)

    Google Scholar 

  9. M. Rath, B. Pati, B.K. Pattanayak, Comparative analysis of AODV routing protocols based on network performance parameters in Mobile Adhoc Networks, in Foundations and Frontiers in Computer, Communication and Electrical Engineering. (CRC Press, Taylor & Francis, 2016), pp. 461–466. ISBN: 978-1-138-02877-7

    Google Scholar 

  10. M. Rath, C.R. Panigrahi, Prioritisation of security measures at the junction of MANET and IoT, in Second International Conference on Information and Communication Technology for Competitive Strategies. (ACM Publication, New York, USA, 2016) http://www.acm.org/publications. ISBN: 978-1-4503-3962-9

  11. M. Rath, B. Pati, B.K Pattanayak, Energy competent routing protocol design in MANET with real time application provision. Int. J. Bus. Data Comm. Network. IGI Global 11(1), 50–60 (2015)

    Article  Google Scholar 

  12. M. Rath, Delay and power based network assessment of network layer protocols in MANET, in 2015 International Conference on Control, Instrumentation, Communication and Computational Technologies (IEEE ICCICCT). (Kumaracoil, India, 2015), pp. 682–686

    Google Scholar 

  13. M.M. Kiliç, Y.S. Akgül, Ship location estimation from radar and optic images using metric learning, in 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir (2018), pp. 1–4

    Google Scholar 

  14. P. McDaniel, N. Papernot, Z.B. Celik, Machine learning in adversarial settings. IEEE Secur. Priv. 14(3), 68–72 (2016)

    Article  Google Scholar 

  15. R.J. Mangialardo, J.C. Duarte, Integrating static and dynamic malware analysis using machine learning. IEEE Lat. Am. Trans. 13(9), 3080–3087 (2015)

    Article  Google Scholar 

  16. N. Islam, S. Das and Y. Chen, On-device mobile phone security exploits machine learning. IEEE Pervasive Comput. 16(2), 92–96 (2017)

    Article  Google Scholar 

  17. M. Mozaffari-Kermani, S. Sur-Kolay, A. Raghunathan, N.K. Jha, Systematic poisoning attacks on and defenses for machine learning in healthcare. IEEE J. Biomed. Health Inform. 19(6), 1893–1905 (2015)

    Article  Google Scholar 

  18. P.V.R. Ferreira et al., Multi objective reinforcement learning for cognitive satellite communications using deep neural network ensembles. IEEE J. Sel. Areas Commun., 36(5). 1030–1041 (2018)

    Google Scholar 

  19. A.L. Buczak, E. Guven, A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. vol. 18(2), 1153–1176 (2016)

    Article  Google Scholar 

  20. J. Wang, Q. Tao, Machine learning: the state of the art. IEEE Intell. Syst. 23(6), 49–55 (2008)

    Article  Google Scholar 

  21. Ahmad, M. Basheri, M.J. Iqbal, A. Rahim, Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6, 33789–33795 (2018). https://doi.org/10.1109/access.2018.2841987

    Article  Google Scholar 

  22. P.V.R. Ferreira et al., Multi-objective reinforcement learning-based deep neural networks for cognitive space communications, in 2017 Cognitive Communications for Aerospace Applications Workshop (CCAA), Cleveland, OH (2017), pp. 1–8. https://doi.org/10.1109/ccaaw.2017.8001880

  23. A. Panda, A. Singh, K. Kumar, A. Kumar, Uddeshya, A. Swetapadma, Land cover prediction from satellite imagery using machine learning techniques, in Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore (2018), pp. 1403–1407. https://doi.org/10.1109/icicct.2018.8473241

  24. D. He, C. Liu, T.Q.S. Quek, H. Wang, Transmit antenna selection in MIMO wiretap channels: a machine learning approach. IEEE Wirel. Commun. Lett. 7(4), 634–637 (2018)

    Article  Google Scholar 

  25. X. Chen, J. Weng, W. Lu, J. Xu, J. Weng, Deep manifold learning combined with convolutional neural networks for action recognition. IEEE Trans. Neural Netw. Learn. Syst. 29(9), 3938–3952 (2018)

    Article  Google Scholar 

  26. W. Zeng, Z. Hong, SPN-based performance analysis of LEO satellite networks with multiple users, in International Conference on Machine Learning and Cybernetics, Guilin ( 2011), pp. 1425–1429. https://doi.org/10.1109/icmlc.2011.6016850

  27. M. Rath, B.K. Pattanayak, B. Pati, Energetic routing protocol design for real-time transmission in mobile ad hoc network, in Computing and Network Sustainability, Lecture Notes in Networks and Systems, vol 12. (Springer, Singapore, 2017)

    Google Scholar 

  28. T. Liu, K. Kang, H. Sun, Fault prediction for satellite communication equipment based on deep neural network, in International Conference on Virtual Reality and Intelligent Systems (ICVRIS), Changsha (2018), pp. 176–178. https://doi.org/10.1109/icvris.2018.00050

  29. M. Rath, B.K. Pattanayak, SCICS: a soft computing based intelligent communication system in VANET. Smart Secure Systems – IoT and Analytics Perspective. Communications in Computer and Information Science, vol 808. Springer (2018)

    Google Scholar 

  30. M. Rath, G.S. Oreku, Security issues in mobile devices and mobile adhoc networks,in Mobile Technologies and Socio-Economic Development in Emerging Nations (IGI Global, 2018), p. 80, ISBN 152254030X. DOI-https://doi.org/10.4018/978-1-5225-4029-8.ch009

  31. M. Rath, An analytical study of security and challenging issues in social networking as an emerging connected technology (20 Apr 2018). In Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018 held at Malaviya National Institute of Technology, Jaipur (India) on 26–27 Mar 2018

    Google Scholar 

  32. M. Rath, J. Swain, IoT security: a challenge in wireless technology. Int. J. Emerg. Technol. Adv. Eng. 8(4), 43–46 (2018), April ISSN: 2250 – 2459 (Online)

    Google Scholar 

  33. Z. Gao, Q. Guo, P. Wang, An adaptive routing based on an improved ant colony optimization in leo satellite networks, in 2007 International Conference on Machine Learning and Cybernetics, Hong Kong (2007), pp. 1041–1044. https://doi.org/10.1109/icmlc.2007.4370296

  34. M. Burmester, B. de Medeiros, On the security of route discovery in MANETs. IEEE Trans. Mob. Comput. 8(9), 1180–1188 (2009)

    Article  Google Scholar 

  35. M. Carvalho, security in mobile ad hoc networks. IEEE Secur. Priv. 6(2), 72–75 (2008)

    Article  Google Scholar 

  36. H.M. Keshk, X. Yin, Satellite super-resolution images depending on deep learning methods: a comparative study, in 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen (2017), pp. 1–7. https://doi.org/10.1109/icspcc.2017.8242625

  37. A. Tsakmalis, S. Chatzinotas, B. Ottersten, Automatic modulation classification for adaptive power control in cognitive satellite communications, in 7th Advanced Satellite Multimedia Systems Conference and the 13th Signal Processing for Space Communications Workshop (ASMS/SPSC), Livorno (2014), pp. 234–240

    Google Scholar 

  38. M. Ozay, I. Esnaola, F.T. Yarman Vural, S.R. Kulkarni, H.V. Poor, Machine learning methods for attack detection in the smart grid. IEEE Trans. Neural Netw. Learn. Syst. 27(8), 1773–1786 (2016)

    Article  MathSciNet  Google Scholar 

  39. A. Diro, N. Chilamkurti, Leveraging LSTM networks for attack detection in fog-to-things communications. IEEE Commun. Mag. 56(9), 24–130 (2018)

    Article  Google Scholar 

  40. B. Rong, H. Chen, Y. Qian, K. Lu, R.Q. Hu, S. Guizani, A pyramidal security model for large-scale group-oriented computing in mobile ad hoc networks: the key management study. IEEE Trans. Veh. Technol. 58(1), 398–408 (2009)

    Article  Google Scholar 

  41. N. Saxena, G. Tsudik, J.H. Yi, Efficient node admission and certificateless secure communication in short-lived MANETs. IEEE Trans. Parallel Distrib. Syst. 20(2), 158–170 (2009)

    Article  Google Scholar 

  42. Y. Wang, F.R. Yu, H. Tang, M. Huang, A mean field game theoretic approach for security enhancements in mobile ad hoc networks. IEEE Trans. Wirel. Commun. 13(3), 1616–1627 (2014)

    Article  Google Scholar 

  43. U. Ghosh, R. Datta, A secure addressing scheme for large-scale managed MANETs. IEEE Trans. Netw. Serv. Manage. 12(3), 483–495 (2015)

    Article  Google Scholar 

  44. Z. Wei, H. Tang, F.R. Yu, M. Wang, P. Mason, Security enhancements for mobile ad hoc networks with trust management using uncertain reasoning. IEEE Trans. Veh. Technol. 63(9), 4647–4658 (2014)

    Article  Google Scholar 

  45. S. Surendran, S. Prakash, An ACO look-ahead approach to QOS enabled fault- tolerant routing in MANETs. China Commun. 12(8), 93–110 (2015)

    Article  Google Scholar 

  46. D.Q. Nguyen, M. Toulgoat, L. Lamont, Impact of trust-based security association and mobility on the delay metric in MANET. J. Commun. Netw. 18(1), 105–111 (2016)

    Article  Google Scholar 

  47. S.K. Dhurandher, M.S. Obaidat, K. Verma, P. Gupta, P. Dhurandher, Faces: friend-based ad hoc routing using challenges to establish security in MANETs systems. IEEE Syst. J. 5(2), 176–188 (2011)

    Article  Google Scholar 

  48. J. Chang, P. Tsou, I. Woungang, H. Chao, C. Lai, Defending against collaborative attacks by malicious nodes in MANETs: a cooperative bait detection approach. IEEE Syst. J. 9(1), 65–75 (2015)

    Article  Google Scholar 

  49. M. Rath, B.K. Pattanayak, Security protocol with ids framework using mobile agent in robotic MANET. Int. J. Inf. Secur. Priv. 13(1), 46–58 (2019). https://doi.org/10.4018/ijisp.2019010104

    Article  Google Scholar 

  50. M. Rath, B. Pati, B. Pattanayak, Manifold surveillance issues in wireless network and the secured protocol. Int. J. Inf. Secur. Priv.(IJISP) 13(3), Article 3 (2019)

    Google Scholar 

  51. M. Rath, B. Pattanayak, Technological improvement in modern health care applications using Internet of Things (IoT) and proposal of novel health care approach. Int. J. Hum. Rights Healthc., ISSN: 2056-4902. (2018). https://doi.org/10.1108/ijhrh-01-2018-0007

    Article  Google Scholar 

  52. M. Rath, Big data and iot-allied challenges associated with healthcare applications in smart and automated systems. Int. J. Strat. Inf. Technol. Appl. (IJSITA) 9(2) (2018). DOI: https://doi.org/10.4018/ijsita.201804010

  53. M. Rath, B. Pati (2017) Load balanced routing scheme for MANETs with power and delay optimisation, Int. J. Commun. Netw. Distrib. Syst. (IJCNDS) 19. Inderscience Publishers

    Google Scholar 

  54. M. Rath, Resource provision and QoS support with added security for client side applications in cloud computing. Int. J. Inf. Technol. 9(3), pp 1–8 (2017)

    Google Scholar 

  55. M. Rath, B.K. Pattanayak, Monitoring of QoS in MANET based real time applications, in Information and communication technology for intelligent systems, vol. 2, ICTIS, ed. by S. Satapathy, A. Joshi. Smart Innovation, Systems and Technologies, vol 84, pp. 579–586, Springer (2018)

    Google Scholar 

  56. M. Rath, B. Pati and B.K. Pattanayak, Cross layer based QoS platform for multimedia transmission in MANET, in 11th International Conference on Intelligent Systems and Control (ISCO), Coimbatore (2017), pp. 402–407

    Google Scholar 

  57. M. Rath, B. Pattanayak “MAQ: a mobile agent based QoS platform for MANETs. Int. J. Bus. Data Commun. Netw. 13(1), 1–8 (2017). IGI Global

    Article  Google Scholar 

  58. M. Rath, M.R. Panda, MAQ system development in mobile ad-hoc networks using mobile agents, in IEEE 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida (2017), pp. 794–798

    Google Scholar 

  59. S. Chaturvedi, V. Mishra, N. Mishra, Sentiment analysis using machine learning for business intelligence, in IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai (2017), pp. 2162–2166

    Google Scholar 

  60. C. Feng, S. Wu, N. Liu, A user-centric machine learning framework for cyber security operations center, in IEEE International Conference on Intelligence and Security Informatics (ISI), Beijing (2017), pp. 173–175

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Birla Global University, Bhubaneswar, India

    Mamata Rath

  2. KIIT University, Bhubaneswar, India

    Sushruta Mishra

Authors
  1. Mamata Rath
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sushruta Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mamata Rath .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Information Technology Department, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  2. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  3. Center of Excellence in Earth Systems Modeling and Observations, Schmid College of Science and Technology, Chapman University, Orange, CA, USA

    Hesham El-Askary

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Rath, M., Mishra, S. (2020). Security Approaches in Machine Learning for Satellite Communication. In: Hassanien, A., Darwish, A., El-Askary, H. (eds) Machine Learning and Data Mining in Aerospace Technology. Studies in Computational Intelligence, vol 836. Springer, Cham. https://doi.org/10.1007/978-3-030-20212-5_10

Download citation

Publish with us

Policies and ethics

Search

Navigation