3A.3 CA proctype
4.6 Conclusion
1.5 2 2.5 3 3.5 4
−1
−0.8
−0.6
−0.4
−0.2 0 0.2 0.4
α
ξ
β = 1, Self-healing β = 2, Self-healing β = 1, Vaccine spreading β = 2, Vaccines preading
Figure 4.16: Relative difference of optimal control signal distribution time under different (α,β) configurations in IoT networks.N= 2000,L= 50,I0= 1/N,δ= 1.1, λinf=λpro= 0.05,ηinf= 6,ηpro= 3,κ= 0.1,Tf = 200,M= 1000,t′= 1, andc= 10−3.
4.5.3 Summary
The contributions of this section are twofold. First, with the aid of epidemic mod- eling, we provide an analytically tractable parametric plug-in model for malware propagation control regarding the time-dependent control capability, with the aim of determining the optimal control signal distribution time to minimize the accu- mulated network cost in real time via dynamic programming. Second, we demon- strate how to use our developed tools to control malware propagation in IoT net- works. Compared with the self-healing scheme, we show that vaccine spreading further mitigates the accumulated cost when the immune nodes participate in for- warding control signal. Consequently, this section provides novel mathematical tools for malware propagation with and without control over IoT networks.
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Chapter 5
A Solution-Based
Analysis of Attack Vectors on Smart Home Systems
Andreas Brauchli Depeng Li
CONTENTS
Abstract. . . 92 5.1 Introduction . . . 92 5.1.1 Smart world . . . 93 5.2 Related Work . . . 94 5.3 The digitalSTROM Environment . . . 95 5.4 Attack Vectors on SHS . . . 96 5.4.1 Central digitalSTROM server . . . 97 5.4.2 Smart control devices . . . 98 5.4.3 Smart home communication bus . . . 99 5.4.4 Remote third-party services . . . 100 5.4.5 Two attack scenarios . . . 100 5.5 SHS Hardening . . . 101 5.5.1 Central digitalSTROM server . . . 101 5.5.2 Smart control devices . . . 102 5.5.3 Smart home communication bus . . . 102 5.5.4 Remote third-party services . . . 103 91
5.6 Solution Analysis . . . 103 5.7 Conclusion . . . 104 Bibliography . . . 104