Social learning for resilient data fusion against data falsification attacks

Springer Science and Business Media LLC - Tập 5 - Trang 1-25 - 2018
Fernando Rosas1,2, Kwang-Cheng Chen3, Deniz Gündüz2
1Centre of Complexity Science and Department of Mathematics, Imperial College London, London, UK
2Department of Electrical and Electronic Engineering, Imperial College London, London, UK
3Department of Electrical engineering, University of South Florida, Tampa, USA

Tóm tắt

Internet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers. To tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network. We analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary. Our results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.

Tài liệu tham khảo

Kim K-D, Kumar PR. Cyber–physical systems: a perspective at the centennial. Proc IEEE. 2012;100(Special Centennial Issue):1287–308.

Response SS. What you need to know about the WannaCry Ransomware. https://www.symantec.com/blogs/threat-intelligence/wannacry-ransomware-attack

Veeravalli VV, Varshney PK. Distributed inference in wireless sensor networks. Philos Trans R Soc Lond A. 2012;370(1958):100–17.

Barbarossa S, Sardellitti S, Di Lorenzo P. Distributed detection and estimation in wireless. Academic Press library in signal processing: communications and radar signal processing. London: Academic Press; 2013. p. 329.

Hancke GP, Hancke GP Jr. The role of advanced sensing in smart cities. Sensors. 2012;13(1):393–425.

Difallah DE, Cudre-Mauroux P, McKenna SA. Scalable anomaly detection for smart city infrastructure networks. IEEE Internet Comput. 2013;17(6):39–47.

Lambrou TP, Panayiotou CG, Polycarpou MM. Contamination detection in drinking water distribution systems using sensor networks. In: Control Conference (ECC), 2015 European. New York: IEEE; 2015. p. 3298–303.

Lambrou TP, Anastasiou CC, Panayiotou CG, Polycarpou MM. A low-cost sensor network for real-time monitoring and contamination detection in drinking water distribution systems. IEEE Sens J. 2014;14(8):2765–72.

Perrig A, Stankovic J, Wagner D. Security in wireless sensor networks. Commun ACM. 2004;47(6):53–7.

Shi E, Perrig A. Designing secure sensor networks. IEEE Wirel Commun. 2004;11(6):38–43.

Pathan A-SK, Lee H-W, Hong CS. Security in wireless sensor networks: issues and challenges. In: The 8th international conference of advanced communication technology, 2006. ICACT 2006, vol. 2. New York: IEEE; 2006. p. 6.

Trappe W, Howard R, Moore RS. Low-energy security: limits and opportunities in the internet of things. IEEE Secur Priv. 2015;13(1):14–21. https://doi.org/10.1109/MSP.2015.7.

Marano S, Matta V, Tong L. Distributed detection in the presence of Byzantine attacks. IEEE Trans Signal Process. 2009;57(1):16–29.

Lamport L, Shostak R, Pease M. The Byzantine generals problem. ACM Trans Program Lang Syst (TOPLAS). 1982;4(3):382–401.

Vempaty A, Tong L, Varshney PK. Distributed inference with Byzantine data: state-of-the-art review on data falsification attack. IEEE Signal Process Mag. 2013;30(5):65–75.

Nadendla VSS, Han YS, Varshney PK. Distributed inference with M-Ary quantized data in the presence of Byzantine attacks. IEEE Trans Signal Process. 2014;62(10):2681–95. https://doi.org/10.1109/TSP.2014.2314072.

Zhang J, Blum RS, Lu X, Conus D. Asymptotically optimum distributed estimation in the presence of attacks. IEEE Trans Signal Process. 2015;63(5):1086–101. https://doi.org/10.1109/TSP.2014.2386281.

Kailkhura B, Han YS, Brahma S, Varshney PK. Distributed Bayesian detection in the presence of Byzantine data. IEEE Trans Signal Process. 2015;63(19):5250–63. https://doi.org/10.1109/TSP.2015.2450191.

Kailkhura B, Brahma S, Han YS, Varshney PK. Distributed detection in tree topologies with Byzantines. IEEE Trans Signal Process. 2014;62(12):3208–19.

Kailkhura B, Brahma S, Dulek B, Han YS, Varshney PK. Distributed detection in tree networks: Byzantines and mitigation techniques. IEEE Trans Inf Forensics Secur. 2015;10(7):1499–512. https://doi.org/10.1109/TIFS.2015.2415757.

Chen K-C, Lien S-Y. Machine-to-machine communications: technologies and challenges. Ad Hoc Netw. 2014;18:3–23.

Parno B, Perrig A, Gligor V. Distributed detection of node replication attacks in sensor networks. In: 2005 IEEE symposium on security and privacy (S&P’05). New York: IEEE; 2005. p. 49–63.

Lin S-C, Chen K-C. Improving spectrum efficiency via in-network computations in cognitive radio sensor networks. IEEE Trans Wirel Commun. 2014;13(3):1222–34.

Daniels BC, Ellison CJ, Krakauer DC, Flack JC. Quantifying collectivity. Curr Opin Neurobiol. 2016;37:106–13.

Brush ER, Krakauer DC, Flack JC. Conflicts of interest improve collective computation of adaptive social structures. Sci Adv. 2018;4(1):1603311.

Tsitsiklis JN. Decentralized detection. Adv Stat Signal Process. 1993;2(2):297–344.

Viswanathan R, Varshney PK. Distributed detection with multiple sensors I. Fundamentals. Proc IEEE. 1997;85(1):54–63.

Blum RS, Kassam SA, Poor HV. Distributed detection with multiple sensors I. Advanced topics. Proc IEEE. 1997;85(1):64–79.

Chen B, Tong L, Varshney PK. Channel aware distributed detection in wireless sensor networks. IEEE Signal Process Mag. 2006;23(4):16–26.

Chamberland J-F, Veeravalli VV. Wireless sensors in distributed detection applications. IEEE Signal Process Mag. 2007;24(3):16–25.

Tsitsiklis J, Athans M. On the complexity of decentralized decision making and detection problems. IEEE Trans Autom Control. 1985;30(5):440–6.

Warren D, Willett P. Optimum quantization for detector fusion: some proofs, examples, and pathology. J Franklin Inst. 1999;336(2):323–59.

Chamberland J-F, Veeravalli VV. Asymptotic results for decentralized detection in power constrained wireless sensor networks. IEEE J Sel Areas Commun. 2004;22(6):1007–15.

Easley D, Kleinberg J. Networks, crowds, and markets, vol. 1(2.1). Cambridge: Cambridge University Press; 2010. p. 2–1.

Acemoglu D, Ozdaglar A. Opinion dynamics and learning in social networks. Dyn Games Appl. 2011;1(1):3–49.

Banerjee AV. A simple model of herd behavior. Q J Econ. 1992;107:797–817.

Bikhchandani S, Hirshleifer D, Welch I. A theory of fads, fashion, custom, and cultural change as informational cascades. J Political Econ. 1992;100:992–1026.

Bikhchandani S, Hirshleifer D, Welch I. Learning from the behavior of others: conformity, fads, and informational cascades. J Econ Perspect. 1998;12(3):151–70.

Smith L, Sørensen P. Pathological outcomes of observational learning. Econometrica. 2000;68(2):371–98.

Bala V, Goyal S. Conformism and diversity under social learning. Econ Theory. 2001;17(1):101–20.

Banerjee A, Fudenberg D. Word-of-mouth learning. Games Econ Behav. 2004;46(1):1–22.

Gale D, Kariv S. Bayesian learning in social networks. Games Econ Behav. 2003;45(2):329–46.

Gill D, Sgroi D. Sequential decisions with tests. Games Econ Behav. 2008;63(2):663–78.

Acemoglu D, Dahleh MA, Lobel I, Ozdaglar A. Bayesian learning in social networks. Rev Econ Stud. 2011;78(4):1201–36.

Hsiao J, Chen KC. Steering information cascades in a social system by selective rewiring and incentive seeding. In: to Be included in 2016 IEEE international conference on communications (ICC) 2016.

DeMarzo PM, Zwiebel J, Vayanos D. Persuasion bias, social influence, and uni-dimensional opinions. In: Social Influence, and Uni-Dimensional Opinions (November 2001). MIT Sloan Working Paper (4339-01). 2001.

Golub B, Jackson MO. Naive learning in social networks and the wisdom of crowds. Am Econ J. 2010;2(1):112–49.

Acemoglu D, Ozdaglar A, ParandehGheibi A. Spread of (mis) information in social networks. Games Econ Behav. 2010;70(2):194–227.

Jadbabaie A, Molavi P, Sandroni A, Tahbaz-Salehi A. Non-Bayesian social learning. Games Econ Behav. 2012;76(1):210–25.

Lalitha A, Sarwate A, Javidi T. Social learning and distributed hypothesis testing. In: 2014 IEEE international symposium on information theory. New York: IEEE; 2014. p. 551–5.

Rhim JB, Goyal VK. Distributed hypothesis testing with social learning and symmetric fusion. IEEE Trans Signal Process. 2014;62(23):6298–308.

Huang SL, Chen KC. Information cascades in social networks via dynamic system analyses. In: 2015 IEEE international conference on communications (ICC); 2015. p. 1262–7. https://doi.org/10.1109/ICC.2015.7248496.

Castro R, Coates M, Liang G, Nowak R, Yu B. Network tomography: recent developments. Stat sci. 2004;19:499–517.

Viswanathan R, Thomopoulos SC, Tumuluri R. Optimal serial distributed decision fusion. IEEE Trans Aerospace Electron Syst. 1988;24(4):366–76.

Papastavrou JD, Athans M. Distributed detection by a large team of sensors in tandem. IEEE Trans Aerospace Electron Syst. 1992;28(3):639–53.

Swaszek PF. On the performance of serial networks in distributed detection. IEEE Trans Aerospace Electron Syst. 1993;29(1):254–60.

Bahceci I, Al-Regib G, Altunbasak Y. Serial distributed detection for wireless sensor networks. In: Proceedings. International symposium on information theory, ISIT 2005. New York: IEEE; 2005. p. 830–4.

Rosas F, Hsiao J-H, Chen K-C. A technological perspective on information cascades via social learning. IEEE Access. 2017;5:22605–33.

Rosas F, Chen K-C. Social learning against data falsification in sensor networks. In: International workshop on complex networks and their applications. New York: Springer; 2017. p. 704–16.

Rosas F, Oberli C. Modulation and SNR optimization for achieving energy-efficient communications over short-range fading channels. IEEE Trans Wirel Commun. 2012;11(12):4286–95.

Bertrand A. Applications and trends in wireless acoustic sensor networks: a signal processing perspective. In: 2011 18th IEEE symposium on communications and vehicular technology in the Benelux (SCVT); 2011. p. 1–6. https://doi.org/10.1109/SCVT.2011.6101302.

Kam M, Zhu Q, Gray WS. Optimal data fusion of correlated local decisions in multiple sensor detection systems. IEEE Trans Aerospace Electron Syst. 1992;28(3):916–20.

Chen J-G, Ansari N. Adaptive fusion of correlated local decisions. IEEE Trans Syst Man Cyberne Part C (Appl Rev). 1998;28(2):276–81.

Willett P, Swaszek PF, Blum RS. The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise. IEEE Trans Signal Process. 2000;48(12):3266–79.

Chamberland J-F, Veeravalli VV. How dense should a sensor network be for detection with correlated observations? IEEE Trans Inf Theory. 2006;52(11):5099–106.

Sundaresan A, Varshney PK, Rao NS. Copula-based fusion of correlated decisions. IEEE Trans Aerospace Electron Syst. 2011;47(1):454–71.

Loeve M. Probability theory, vol. 1. New York: Springer; 1978.

Karl H, Willig A. Protocols and architectures for wireless sensor networks. Chichester: Wiley; 2007.

Sundararaman B, Buy U, Kshemkalyani AD. Clock synchronization for wireless sensor networks: a survey. Ad hoc Netw. 2005;3(3):281–323.

Rosas F, Brante G, Souza RD, Oberli C. Optimizing the code rate for achieving energy-efficient wireless communications. In: Wireless communications and networking conference (WCNC), 2014 IEEE. New York: IEEE; 2014. p. 775–80.

Karyotis V, Khouzani M. Malware diffusion models for modern complex networks: theory and applications. Cambridge: Morgan Kaufmann; 2016.

Poor HV. An introduction to signal detection and estimation. Berlin-Heidelberg: Springer; 2013.

Smith P, Hutchison D, Sterbenz JP, Schöller M, Fessi A, Karaliopoulos M, Lac C, Plattner B. Network resilience: a systematic approach. IEEE Commun Mag. 2011;49(7):88–97.

Shiller RJ. Conversation, information, and herd behavior. Am Econ Rev. 1995;85(2):181–5.

Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. Boca Raton: CRC Press; 2014.

Cover TM, Thomas JA. Elements of information theory. New Jersey: Wiley; 2012.

Rosas F, Ntranos V, Ellison CJ, Pollin S, Verhelst M. Understanding interdependency through complex information sharing. Entropy. 2016;18(2):38.

Dieudonne J. Treatise on analysis, vol. II. New York: Associated Press; 1976.

McKenna SA, Wilson M, Klise KA. Detecting changes in water quality data. J Am Water Works Assoc. 2008;100(1):74.

Thông tin
Thông tin xuất bản

Springer Science and Business Media LLC

Tập 5

1-25

Thông tin tác giả