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Proactive and Dynamic Network Defense pp 191–221Cite as

The Role of Machine Learning and Radio Reconfigurability in the Quest for Wireless Security

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Part of the Advances in Information Security book series (ADIS,volume 74)

Abstract

Wireless networks require fast-acting, effective and efficient security mechanisms able to tackle unpredictable, dynamic, and stealthy attacks. In recent years, we have seen the steadfast rise of technologies based on machine learning and software-defined radios, which provide the necessary tools to address existing and future security threats without the need of direct human-in-the-loop intervention. On the other hand, these techniques have been so far used in an ad hoc fashion, without any tight interaction between the attack detection and mitigation phases. In this chapter, we propose and discuss a Learning-based Wireless Security (LeWiS) framework that provides a closed-loop approach to the problem of cross-layer wireless security. Along with discussing the LeWiS framework, we also survey recent advances in cross-layer wireless security.

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Notes

  1. 1.

    For a more detailed discussion on active attacks and their impact on wireless communications we refer the interested readers to [4, 85, 107], where an exhaustive analysis of active attacks and their corresponding defence mechanisms is provided.

  2. 2.

    The provided summary is not intended to be exhaustive, and a more detailed taxonomy of defense strategies can be found in [13, 96]. Furthermore, the same defence strategy (e.g., authentication, coding) can be implemented in different ways, and with possibly distinct outcomes, at multiple layers of the protocol stack.

References

  1. A.A. Aburomman, M.B.I. Reaz, A novel svm-knn-pso ensemble method for intrusion detection system. Appl. Soft Comput. 38, 360–372 (2016)

    CrossRef  Google Scholar 

  2. A.A. Ahmed, N.F. Fisal, Secure real-time routing protocol with load distribution in wireless sensor networks. Secur. Commun. Netw. 4(8), 839–869 (2011)

    CrossRef  Google Scholar 

  3. L. Akoglu, C. Faloutsos, Anomaly, event, and fraud detection in large network datasets, in Proceeding of the ACM International Conference on Web Search and Data Mining (WSDM) (2013), pp. 773–774

    Google Scholar 

  4. I.F. Akyildiz, X. Wang, A survey on wireless mesh networks. IEEE Commun. Mag. 43(9), S23–S30 (2005)

    CrossRef  Google Scholar 

  5. M.A. Alsheikh, S. Lin, D. Niyato, H.P. Tan, Machine learning in wireless sensor networks: algorithms, strategies, and applications. IEEE Commun. Surv. Tutorials 16(4), 1996–2018 (2014)

    CrossRef  Google Scholar 

  6. R.A.R. Ashfaq, X.Z. Wang, J.Z. Huang, H. Abbas, Y.L. He, Fuzziness based semi-supervised learning approach for intrusion detection system. Inf. Sci. 378, 484–497 (2017). https://doi.org/10.1016/j.ins.2016.04.019. http://www.sciencedirect.com/science/article/pii/S0020025516302547

  7. D. Balfanz, D.K. Smetters, P. Stewart, H.C. Wong, Talking to strangers: authentication in ad-hoc wireless networks, in NDSS (2002)

    Google Scholar 

  8. D.P. Bertsekas, Dynamic Programming and Optimal Control, vol. 1 (Athena Scientific, Belmont, MA, 1995)

    Google Scholar 

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

    CrossRef  Google Scholar 

  10. L. Busoniu, R. Babuska, B. De Schutter, A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. C 38(2), 156–172 (2008)

    CrossRef  Google Scholar 

  11. L. Buşoniu, B. De Schutter, R. Babuška, Approximate dynamic programming and reinforcement learning, in Interactive Collaborative Information Systems (Springer, New York, 2010), pp. 3–44

    CrossRef  Google Scholar 

  12. A. Cassola, W.K. Robertson, E. Kirda, G. Noubir, A practical, targeted, and stealthy attack against wpa enterprise authentication, in NDSS (2013)

    Google Scholar 

  13. X. Chen, K. Makki, K. Yen, N. Pissinou, Sensor network security: a survey. IEEE Commun. Surv. Tutorials 11(2), 52–73 (2009)

    CrossRef  Google Scholar 

  14. M. Chiang, Balancing transport and physical layers in wireless multihop networks: jointly optimal congestion control and power control. IEEE J. Sel. Areas Commun. 23(1), 104–116 (2005)

    CrossRef  Google Scholar 

  15. T.C. Clancy, Efficient ofdm denial: pilot jamming and pilot nulling, in IEEE International Conference on Communications (ICC) (IEEE, Kyoto, 2011), pp. 1–5

    Google Scholar 

  16. C. Clancy, J. Hecker, E. Stuntebeck, T. O’Shea, Applications of machine learning to cognitive radio networks. IEEE Wirel. Commun. 14(4), 47–52 (2007)

    CrossRef  Google Scholar 

  17. S. Convery, Network Security Architectures (Pearson Education, Chennai, 2004)

    Google Scholar 

  18. M. Crawford, T.M. Khoshgoftaar, J.D. Prusa, A.N. Richter, H.A. Najada, Survey of review spam detection using machine learning techniques. J. Big Data 2(1), 2–23 (2015)

    CrossRef  Google Scholar 

  19. M.L. Das, Two-factor user authentication in wireless sensor networks. IEEE Trans. Wirel. Commun. 8(3), 1086–1090 (2009)

    CrossRef  Google Scholar 

  20. T.R. Dean, A.J. Goldsmith, Physical-layer cryptography through massive mimo. IEEE Trans. Inf. Theory 63(8), 5419–5436 (2017). https://doi.org/10.1109/TIT.2017.2715187

    CrossRef  MathSciNet  MATH  Google Scholar 

  21. L. Deng, G. Hinton, B. Kingsbury, New types of deep neural network learning for speech recognition and related applications: an overview, in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2013), pp. 8599–8603

    Google Scholar 

  22. L. Deng, D. Yu et al., Deep learning: methods and applications. Found. Trends Signal Process. 7(3–4), 197–387 (2014)

    CrossRef  MathSciNet  MATH  Google Scholar 

  23. L. Ding, T. Melodia, S. Batalama, J. Matyjas, M. Medley, Cross-layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Trans. Veh. Technol. 59, 1969–1979 (2010)

    CrossRef  Google Scholar 

  24. S. D’Oro, L. Galluccio, G. Morabito, S. Palazzo, Efficiency analysis of jamming-based countermeasures against malicious timing channel in tactical communications, in 2013 IEEE International Conference on Communications (ICC) (2013), pp. 4020–4024. https://doi.org/10.1109/ICC.2013.6655188

  25. S. D’Oro, L. Galluccio, G. Morabito, S. Palazzo, L. Chen, F. Martignon, Defeating jamming with the power of silence: a game-theoretic analysis. IEEE Trans. Wirel. Commun. 14(5), 2337–2352 (2015)

    CrossRef  Google Scholar 

  26. S. D’Oro, E. Ekici, S. Palazzo, Optimal power allocation and scheduling under jamming attacks. IEEE/ACM Trans. Netw. 25(3), 1310–1323 (2017). https://doi.org/10.1109/TNET.2016.2622002

    CrossRef  Google Scholar 

  27. R.O. Duda, P.E. Hart, D.G. Stork, Pattern Classification, 2nd edn. (Wiley, New York, 2000)

    MATH  Google Scholar 

  28. E.F. Flushing, J. Nagi, G.A. Di Caro, A mobility-assisted protocol for supervised learning of link quality estimates in wireless networks, in International Conference on Computing, Networking and Communications (ICNC) (IEEE, Maui, 2012), pp. 137–143

    Google Scholar 

  29. A.B. Gershman, U. Nickel, J.F. Bohme, Adaptive beamforming algorithms with robustness against jammer motion. IEEE Trans. Signal Process. 45(7), 1878–1885 (1997)

    CrossRef  MATH  Google Scholar 

  30. P.K. Gopala, L. Lai, H. El Gamal, On the secrecy capacity of fading channels. IEEE Trans. Inf. Theory 54(10), 4687–4698 (2008)

    CrossRef  MathSciNet  MATH  Google Scholar 

  31. K. Hasan, S. Shetty, T. Oyedare, Cross layer attacks on gsm mobile networks using software defined radios, in 2017 14th IEEE Annual Consumer Communications Networking Conference (CCNC) (2017), pp. 357–360. https://doi.org/10.1109/CCNC.2017.7983134

  32. W. Hu, W. Hu, S. Maybank, Adaboost-based algorithm for network intrusion detection. IEEE Trans. Syst. Man Cybern. B Cybern. 38(2), 577–583 (2008)

    CrossRef  Google Scholar 

  33. J. Huang, A.L. Swindlehurst, Cooperative jamming for secure communications in mimo relay networks. IEEE Trans. Signal Process. 59(10), 4871–4884 (2011)

    CrossRef  MathSciNet  MATH  Google Scholar 

  34. J.Y. Huang, I.E. Liao, Y.F. Chung, K.T. Chen, Shielding wireless sensor network using Markovian intrusion detection system with attack pattern mining. Inf. Sci. 231, 32–44 (2013). https://doi.org/10.1016/j.ins.2011.03.014. http://www.sciencedirect.com/science/article/pii/S0020025511001435. Data Mining for Information Security

  35. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, T. Darrell, Caffe: convolutional architecture for fast feature embedding, in Proceedings of the ACM International Conference on Multimedia (2014), pp. 675–678

    Google Scholar 

  36. I. Jirón, I. Soto, R. Carrasco, N. Becerra, Hyperelliptic curves encryption combined with block codes for Gaussian channel. Int. J. Commun. Syst. 19(7), 809–830 (2006)

    CrossRef  MATH  Google Scholar 

  37. J.F.C. Joseph, B.S. Lee, A. Das, B.C. Seet, Cross-layer detection of sinking behavior in wireless ad hoc networks using svm and fda. IEEE Trans. Dependable Secure Comput. 8(2), 233–245 (2011)

    CrossRef  Google Scholar 

  38. L.P. Kaelbling, M.L. Littman, A.W. Moore, Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    CrossRef  Google Scholar 

  39. C. Karlof, D. Wagner, Secure routing in wireless sensor networks: attacks and countermeasures. Ad Hoc Netw. 1(2–3), 293–315 (2003)

    CrossRef  Google Scholar 

  40. C. Karlof, N. Sastry, D. Wagner, Tinysec: a link layer security architecture for wireless sensor networks, in Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems (ACM, New York, 2004), pp. 162–175

    Google Scholar 

  41. Kdd cup 1999 data (1999). http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html

  42. R.A. Kemmerer, G. Vigna, Intrusion detection: a brief history and overview. Computer 35(4), 27–30 (2002). https://doi.org/10.1109/MC.2002.1012428

    CrossRef  Google Scholar 

  43. D. Kreutz, F.M.W. Ramos, P.E. Verissimo, C.E. Rothenberg, S. Azodolmolky, S. Uhlig, Software-defined networking: a comprehensive survey. Proc. IEEE 103(1), 14–76 (2015). https://doi.org/10.1109/JPROC.2014.2371999

    CrossRef  Google Scholar 

  44. I. Krikidis, J.S. Thompson, S. McLaughlin, Relay selection for secure cooperative networks with jamming. IEEE Trans. Wirel. Commun. 8(10), 5003–5011 (2009)

    CrossRef  Google Scholar 

  45. P. Laskov, P. Düssel, C. Schäfer, K. Rieck, Learning intrusion detection: supervised or unsupervised?, in Image Analysis and Processing – ICIAP 2005, ed. by F. Roli, S. Vitulano (Springer, Berlin/Heidelberg, 2005), pp. 50–57

    CrossRef  Google Scholar 

  46. Y. LeCun, Y. Bengio, G. Hinton, Deep learning. Nature 521(7553), 436 (2015)

    Google Scholar 

  47. K. Lee, J. Kim, K.H. Kwon, Y. Han, S. Kim, Ddos attack detection method using cluster analysis. Exp. Syst. Appl. 34(3), 1659–1665 (2008)

    CrossRef  Google Scholar 

  48. Y. Li, L. Guo, An active learning based tcm-knn algorithm for supervised network intrusion detection. Comput. Secur. 26(7), 459–467 (2007)

    CrossRef  Google Scholar 

  49. M. Li, I. Koutsopoulos, R. Poovendran, Optimal jamming attacks and network defense policies in wireless sensor networks, in 26th IEEE International Conference on Computer Communications. INFOCOM 2007 (IEEE, Alaska, 2007), pp. 1307–1315

    Google Scholar 

  50. W. Li, P. Yi, Y. Wu, L. Pan, J. Li, A new intrusion detection system based on knn classification algorithm in wireless sensor network. J. Elect. Comput. Eng. 2014, Article ID 240217 (2014)

    Google Scholar 

  51. X. Lin, N.B. Shroff, R. Srikant, A tutorial on cross-layer optimization in wireless networks. IEEE J. Sel. Areas Commun. 24, 1452–1463 (2006)

    CrossRef  Google Scholar 

  52. W.C. Lin, S.W. Ke, C.F. Tsai, Cann: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowl. Based Syst. 78, 13–21 (2015)

    CrossRef  Google Scholar 

  53. G. Liu, Z. Yi, S. Yang, A hierarchical intrusion detection model based on the pca neural networks. Neurocomputing 70(7), 1561–1568 (2007). https://doi.org/10.1016/j.neucom.2006.10.146. http://www.sciencedirect.com/science/article/pii/S0925231206004644. Advances in Computational Intelligence and Learning

  54. S. Liu, Y. Hong, E. Viterbo, Unshared secret key cryptography. IEEE Trans. Wirel. Commun. 13(12), 6670–6683 (2014)

    CrossRef  Google Scholar 

  55. J. Lubacz, W. Mazurczyk, K. Szczypiorski, Principles and overview of network steganography. IEEE Commun. Mag. 52(5), 225–229 (2014)

    CrossRef  Google Scholar 

  56. W. Mao, Modern Cryptography: Theory and Practice (Prentice Hall, Upper Saddle River, 2003)

    Google Scholar 

  57. D. Martins, H. Guyennet, Steganography in mac layers of 802.15. 4 protocol for securing wireless sensor networks, in 2010 International Conference on Multimedia Information Networking and Security (MINES) (IEEE, Nanjing, 2010), pp. 824–828

    Google Scholar 

  58. T. Melodia, H. Kulhandjian, L.C. Kuo, E. Demirors, Advances in Underwater Acoustic Networking (Wiley, New York, 2013), pp. 804–852. https://doi.org/10.1002/9781118511305.ch23. http://dx.doi.org/10.1002/9781118511305.ch23

  59. R.F. Molanes, J.J. Rodríguez-Andina, J. Fariña, Performance characterization and design guidelines for efficient processor - fpga communication in cyclone v fpsocs. IEEE Trans. Ind. Electron. 65(5), 4368–4377 (2018). https://doi.org/10.1109/TIE.2017.2766581

    CrossRef  Google Scholar 

  60. L. Mucchi, L.S. Ronga, E. Del Re, A novel approach for physical layer cryptography in wireless networks. Wirel. Pers. Commun. 53(3), 329–347 (2010)

    CrossRef  Google Scholar 

  61. L. Mucchi, L.S. Ronga, E. Del Re, Physical layer cryptography and cognitive networks, in Trustworthy Internet (Springer, New York, 2011), pp. 75–91

    Google Scholar 

  62. A. Mukherjee, S.A.A. Fakoorian, J. Huang, A.L. Swindlehurst, Principles of physical layer security in multiuser wireless networks: a survey. IEEE Commun. Surv. Tutorials 16(3), 1550–1573 (2014)

    CrossRef  Google Scholar 

  63. M.D.V. Pena, J.J. Rodriguez-Andina, M. Manic, The internet of things: the role of reconfigurable platforms. IEEE Ind. Electron. Mag. 11(3), 6–19 (2017). https://doi.org/10.1109/MIE.2017.2724579

    CrossRef  Google Scholar 

  64. S. Pudlewski, N. Cen, Z. Guan, T. Melodia, Video transmission over lossy wireless networks: a cross-layer perspective. IEEE J. Sel. Top. Signal Process. 9, 6–22 (2015)

    CrossRef  Google Scholar 

  65. O. Puñal, I. Aktaş, C.J. Schnelke, G. Abidin, K. Wehrle, J. Gross, Machine learning-based jamming detection for ieee 802.11: design and experimental evaluation, in 2014 IEEE 15th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) (IEEE, Sydney, 2014), pp. 1–10

    Google Scholar 

  66. E. Rescorla, SSL and TLS: Designing and Building Secure Systems, vol. 1 (Addison-Wesley, Reading, 2001)

    Google Scholar 

  67. A. Saied, R.E. Overill, T. Radzik, Detection of known and unknown ddos attacks using artificial neural networks. Neurocomputing 172, 385–393 (2016)

    CrossRef  Google Scholar 

  68. A.L. Samuel, Some studies in machine learning using the game of checkers. IBM J. Res. Dev. 44(1–2), 206–226 (2000)

    CrossRef  Google Scholar 

  69. K. Sanzgiri, B. Dahill, B.N. Levine, C. Shields, E.M. Belding-Royer, A secure routing protocol for ad hoc networks, in 10th IEEE International Conference on Network Protocols, 2002. Proceedings (IEEE, Paris, 2002), pp. 78–87

    Google Scholar 

  70. N. Sastry, D. Wagner, Security considerations for ieee 802.15. 4 networks, in Proceedings of the 3rd ACM workshop on Wireless Security (ACM, New York, 2004), pp. 32–42

    Google Scholar 

  71. C. Shahriar, S. Sodagari, T.C. Clancy, Performance of pilot jamming on mimo channels with imperfect synchronization, in 2012 IEEE International Conference on Communications (ICC), pp. 898–902 (IEEE, Ottawa, 2012)

    Google Scholar 

  72. S. Shamshirband, A. Patel, N.B. Anuar, M.L.M. Kiah, A. Abraham, Cooperative game theoretic approach using fuzzy q-learning for detecting and preventing intrusions in wireless sensor networks. Eng. Appl. Artif. Intell. 32, 228–241 (2014)

    CrossRef  Google Scholar 

  73. S.S.S. Sindhu, S. Geetha, A. Kannan, Decision tree based light weight intrusion detection using a wrapper approach. Exp. Syst. Appl. 39(1), 129–141 (2012). https://doi.org/10.1016/j.eswa.2011.06.013. http://www.sciencedirect.com/science/article/pii/S0957417411009080

  74. K.J. Singh, D.S. Kapoor, Create your own internet of things: a survey of IoT platforms. IEEE Consum. Electron. Mag. 6(2), 57–68 (2017)

    CrossRef  Google Scholar 

  75. R. Sommer, V. Paxson, Outside the closed world: on using machine learning for network intrusion detection, in 2010 IEEE Symposium on Security and Privacy (2010), pp. 305–316

    Google Scholar 

  76. G. Stein, B. Chen, A.S. Wu, K.A. Hua, Decision tree classifier for network intrusion detection with ga-based feature selection, in Proceedings of the 43rd Annual Southeast Regional Conference - Volume 2, ACM-SE 43 (ACM, New York, 2005), pp. 136–141. https://doi.org/10.1145/1167253.1167288. http://doi.acm.org/10.1145/1167253.1167288

  77. M. Strasser, B. Danev, S. Čapkun, Detection of reactive jamming in sensor networks. ACM Trans. Sens. Netw. 7(2), 16:1–16:29 (2010)

    Google Scholar 

  78. R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction, vol. 1 (MIT Press, Cambridge, 1998)

    MATH  Google Scholar 

  79. K. Szczypiorski, Hiccups: hidden communication system for corrupted networks, in International Multi-Conference on Advanced Computer Systems (2003), pp. 31–40

    Google Scholar 

  80. A. Testolin, M. Zanforlin, M.D.F. De Grazia, D. Munaretto, A. Zanella, M. Zorzi, M. Zorzi, A machine learning approach to qoe-based video admission control and resource allocation in wireless systems, in 2014 13th Annual Mediterranean Ad Hoc Networking Workshop (MED-HOC-NET) (IEEE, Piran, 2014), pp. 31–38

    Google Scholar 

  81. G. Thamilarasu, A. Balasubramanian, S. Mishra, R. Sridhar, A cross-layer based intrusion detection approach for wireless ad hoc networks, in Proceedings of IEEE International Conference on Mobile Adhoc and Sensor Systems, Washington (2005)

    Google Scholar 

  82. A.D. Tijsma, M.M. Drugan, M.A. Wiering, Comparing exploration strategies for q-learning in random stochastic mazes, in 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (2016), pp. 1–8. https://doi.org/10.1109/SSCI.2016.7849366

  83. O. Ureten, N. Serinken, Wireless security through rf fingerprinting. Can. J. Elect. Comput. Eng. 32(1), 27–33 (2007)

    CrossRef  Google Scholar 

  84. S. Vadlamani, B. Eksioglu, H. Medal, A. Nandi, Jamming attacks on wireless networks: a taxonomic survey. Int. J. Prod. Econ. 172, 76–94 (2016)

    CrossRef  Google Scholar 

  85. J.P. Walters, Z. Liang, W. Shi, V. Chaudhary, Wireless sensor network security: a survey, in Security in Distributed, Grid, Mobile, and Pervasive Computing, vol. 1 (Auerbach, Boston, 2007), p. 367

    Google Scholar 

  86. Y. Wang, M. Martonosi, L.S. Peh, Predicting link quality using supervised learning in wireless sensor networks. ACM SIGMOBILE Mob. Comput. Commun. Rev. 11(3), 71–83 (2007)

    CrossRef  Google Scholar 

  87. X. Wang, J.S. Wong, F. Stanley, S. Basu, Cross-layer based anomaly detection in wireless mesh networks, in Proceedings of International Symposium on Applications and the Internet, Bellevue (2009), pp. 9–15

    Google Scholar 

  88. S.S. Wang, K.Q. Yan, S.C. Wang, C.W. Liu, An integrated intrusion detection system for cluster-based wireless sensor networks. Exp. Syst. Appl. 38(12), 15234–15243 (2011)

    CrossRef  Google Scholar 

  89. X. Wang, M. Tao, J. Mo, Y. Xu, Power and subcarrier allocation for physical-layer security in ofdma-based broadband wireless networks. IEEE Trans. Inf. Forensics Secur. 6(3), 693–702 (2011)

    CrossRef  Google Scholar 

  90. X. Wang, Q.Z. Sheng, X.S. Fang, L. Yao, X. Xu, X. Li, An integrated Bayesian approach for effective multi-truth discovery, in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management (ACM, New York, 2015), pp. 493–502

    Google Scholar 

  91. C.J. Watkins, P. Dayan, Q-learning. Mach. Learn. 8(3–4), 279–292 (1992)

    MATH  Google Scholar 

  92. D.J. White, Markov Decision Processes (Wiley, New York, 1993)

    MATH  Google Scholar 

  93. M. Wilhelm, I. Martinovic, J.B. Schmitt, V. Lenders, Short paper: reactive jamming in wireless networks: how realistic is the threat?, in Proceedings of the Fourth ACM Conference on Wireless Network Security (ACM, New York, 2011), pp. 47–52

    Google Scholar 

  94. M. Wilhelm, I. Martinovic, J.B. Schmitt, V, Lenders, Wifire: a firewall for wireless networks, in ACM SIGCOMM Computer Communication Review, vol. 41 (ACM, New York, 2011), pp. 456–457

    Google Scholar 

  95. G.J. Woeginger, Exact algorithms for np-hard problems: a survey, in Combinatorial Optimization – Eureka, You Shrink! (Springer, New York, 2003), pp. 185–207

    CrossRef  Google Scholar 

  96. B. Wu, J. Chen, J. Wu, M. Cardei, A survey of attacks and countermeasures in mobile ad hoc networks, in Wireless Network Security (Springer, New York, 2007), pp. 103–135

    Google Scholar 

  97. Y. Wu, B. Wang, K.R. Liu, T.C. Clancy, Anti-jamming games in multi-channel cognitive radio networks. IEEE J. Sel. Areas Commun. 30(1), 4–15 (2012)

    CrossRef  Google Scholar 

  98. F. Wu, R. Zhang, L.L. Yang, W. Wang, Transmitter precoding-aided spatial modulation for secrecy communications. IEEE Trans. Veh. Technol. 65(1), 467–471 (2016)

    CrossRef  Google Scholar 

  99. A.M. Wyglinski, D.P. Orofino, M.N. Ettus, T.W. Rondeau, Revolutionizing software defined radio: case studies in hardware, software, and education. IEEE Commun. Mag. 54(1), 68–75 (2016)

    CrossRef  Google Scholar 

  100. Z. Xi, L. Li, G. Shi, S. Wang, A comparative study of encryption algorithms in wireless sensor network, in Wireless Communications, Networking and Applications, ed. by Q.A. Zeng (Springer, New Delhi, 2016), pp. 1087–1097

    CrossRef  Google Scholar 

  101. L. Xiao, L. Greenstein, N. Mandayam, W. Trappe, Fingerprints in the ether: using the physical layer for wireless authentication, in IEEE International Conference on Communications, 2007. ICC’07 (IEEE, Glasgow, 2007), pp. 4646–4651

    Google Scholar 

  102. Q. Yan, H. Zeng, T. Jiang, M. Li, W. Lou, Y.T. Hou, Mimo-based jamming resilient communication in wireless networks, in 2014 Proceedings IEEE INFOCOM (IEEE, Toronto, 2014), pp. 2697–2706

    Google Scholar 

  103. Z. Yu, J.J. Tsai, A framework of machine learning based intrusion detection for wireless sensor networks, in IEEE International Conference on Sensor Networks, Ubiquitous and Trustworthy Computing, 2008. SUTC’08 (IEEE, Taichung, 2008), pp. 272–279

    Google Scholar 

  104. L. Zhang, T. Melodia, Hammer and anvil: the threat of a cross-layer jamming-aided data control attack in multihop wireless networks, in Proceedings of the IEEE Conference on Communications and Network Security (CNS), Florence (2015), pp. 361–369

    Google Scholar 

  105. L. Zhang, F. Restuccia, T. Melodia, S.M. Pudlewski, Learning to detect and mitigate cross-layer attacks in wireless networks: framework and applications, in Proceedings of the IEEE Conference on Communications and Network Security (CNS), Las Vegas (2017), pp. 361–369

    Google Scholar 

  106. L. Zhang, F. Restuccia, T. Melodia, S. Pudlewski, Taming cross-layer attacks in wireless networks: a Bayesian learning approach. IEEE Trans. Mobile Comput. (2018). https://doi.org/10.1109/TMC.2018.2864155

  107. J. Zheng, A. Jamalipour, Wireless Sensor Networks: A Networking Perspective (Wiley, New York, 2009)

    CrossRef  MATH  Google Scholar 

  108. P. Zhou, Y. Chang, J.A. Copeland, Reinforcement learning for repeated power control game in cognitive radio networks. IEEE J. Sel. Areas Commun. 30(1), 54–69 (2012)

    CrossRef  Google Scholar 

  109. L. Zhou, D. Wu, B. Zheng, M. Guizani, Joint physical-application layer security for wireless multimedia delivery. IEEE Commun. Mag. 52(3), 66–72 (2014). https://doi.org/10.1109/MCOM.2014.6766087

    CrossRef  Google Scholar 

  110. Y. Zou, J. Zhu, X. Wang, L. Hanzo, A survey on wireless security: technical challenges, recent advances, and future trends. Proc. IEEE 104, 1727–1765 (2016)

    CrossRef  Google Scholar 

  111. F. Restuccia, T. Melodia, Big data goes small: real-time spectrum-driven embedded wireless networking through deep learning in the RF loop, in Proceedings of IEEE International Conference on Computer Communications (INFOCOM), Paris (May 2019)

    Google Scholar 

  112. J. Jagannath, N. Polosky, A. Jagannath, F. Restuccia, T. Melodia, Machine learning for wireless communications in the Internet of things: a comprehensive survey. Preprint. arXiv:1901.07947

    Google Scholar 

  113. S. D’Oro, F. Restuccia, T. Melodia, Hiding data in plain sight: undetectable wireless communications through pseudo-noise asymmetric shift keying, in Proceedings of IEEE International Conference on Computer Communications (INFOCOM), Paris (May 2019)

    Google Scholar 

  114. F. Restuccia, S. D’Oro, T. Melodia, Securing the internet of things in the age of machine learning and software-defined networking. IEEE Internet Things J. 5(6) (2018)

    Google Scholar 

  115. F. Restuccia, N. Ghosh, S. Bhattacharjee, S.K. Das, T. Melodia, Quality of information in mobile crowd sensing: survey and research challenges. ACM Trans. Sensor Netw. 13(4), 34 (2017)

    Google Scholar 

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Acknowledgements

This work is based upon work supported in part by ONR grants 0014-16-1-2213 and N00014-17-1-2046, ARMY W911NF-17-1-0034, and NSF CNS-1618727.

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Authors and Affiliations

  1. Institute for the Wireless Internet of Things, Northeastern University, Boston, MA, USA

    Francesco Restuccia, Salvatore D’Oro, Liyang Zhang & Tommaso Melodia

Authors
  1. Francesco Restuccia
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  2. Salvatore D’Oro
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  3. Liyang Zhang
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  4. Tommaso Melodia
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Correspondence to Tommaso Melodia .

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Editors and Affiliations

  1. Computing and Information Science Division, Army Research Office, Durham, NC, USA

    Dr. Cliff Wang

  2. Department of Electrical Engineering, University of South Florida, Tampa, FL, USA

    Zhuo Lu

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Restuccia, F., D’Oro, S., Zhang, L., Melodia, T. (2019). The Role of Machine Learning and Radio Reconfigurability in the Quest for Wireless Security. In: Wang, C., Lu, Z. (eds) Proactive and Dynamic Network Defense. Advances in Information Security, vol 74. Springer, Cham. https://doi.org/10.1007/978-3-030-10597-6_8

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  • DOI: https://doi.org/10.1007/978-3-030-10597-6_8

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