Chin-Yi University of Technology Institute of Electronic Engineering Network E.M.U Application Lab Reporter : Huang-Wei Liu Advisor : Tsung-Hung Lin 1

Network E.M.U Application Lab  IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 7, NO. 6, JUNE 2008  Yun Wang, Student Member, IEEE Xiaodong Wang, Student Member, IEEE Bin Xie, Senior Member, IEEE Demin Wang, Student Member,IEEE Dharma P. Agrawal, Fellow, IEEE 2

Network E.M.U Application Lab  Introduction  Intrusion detection model and definitions  Intrusion detection in a homogeneous wireless sensor network  Intrusion detection in a heterogeneous wireless sensor network  Network connectivity and broadcast reachability in a heterogeneous wireless sensor network  Simulation and Verification  Conclusion  Reference 3

Network E.M.U Application Lab  The intrusion detection application concerns how fast the intruder can be detected by the WSN.  A high-density deployment policy increases the network investment and may be even unaffordable for a large area.  A network with small and scattered void areas will also be able to detect a moving intruder within a certain intrusion distance. 4

Network E.M.U Application Lab  In this paper, the authors derive the expected intrusion distance and evaluate the detection probability in different application scenarios. 5

Network E.M.U Application Lab  The main contributions of this paper:  Developing an analytical model for intrusion detection in WSNs, and mathematically analyzing the detection probability with respect to various network parameters.  Applying the analytical model to single-sensing detection and multiple-sensing detection scenarios for homogeneous and heterogeneous WSNs. 6

Network E.M.U Application Lab  Defining and examining the network connectivity and broadcast reachability in a heterogeneous WSN. 7 Fig. Intrusion detection in a WSN

Network E.M.U Application Lab  Our intrusion detection model includes :  Network Model  Detection Model  Intrusion Strategy Model 8

Network E.M.U Application Lab  Network Model  Homogeneous (Fig.1(a))  Heterogeneous (Fig.1(b)) 9 sensor type I sensor type II Fig.1 Network model type : (a)Homogeneous WSNs(b)Heterogeneous WSNs (a) (b) intruder Sensor that has a smaller sensor range r s2 as well as a shorter transmission range r x2 Sensor that has a large sensor range r s1 as well as a longer transmission range r x1 L L λ1λ1 λ2λ2 λ : node density of the WSN AA A : square area L*L

Network E.M.U Application Lab  Detection Model  The detection model defines how the intruder can be detected.  Two detection models : ▪ single-sensing detection model ▪ multiple-sensing detection model  Three metrics (Fig.2) : ▪ Intrusion distance ▪ Detection probability ▪ Average intrusion distance 10

Network E.M.U Application Lab 11 D D : Intrusion detection distance Ρ % : detection probability Ρ % E(D): Average intrusion distance E(D) the expected distance that the intruder travels before it is detected by the WSN for the first time the probability that an intruder is detected within a certain intrusion distance the distance that the intruder travels before it is detected by a WSN for the first time. Fig.2 three metrics of WSNs

Network E.M.U Application Lab  Intrusion Strategy Model  The intrusion strategy illustrates the moving policy of the intruder.  Two intrusion strategies for the movement of the intruder in a WSN: the intrusion path is a straight line(Fig.3) and the intrusion area accordingly is a curved band(Fig.4). 12

Network E.M.U Application Lab 13 Fig. 3. Intrusion strategy 1. Fig. 4. Intrusion strategy 2. If D 1 =D 2, the corresponding intrusion detection areas approximately satisfy S 1 =S 2.

Network E.M.U Application Lab 14 Calculate S D : Fig. 5. The intruder starts from the boundary of the WSN. Fig. 6. The intruder starts form a random point in the WSN.

Network E.M.U Application Lab 15 Instrusion detection analytical items

Network E.M.U Application Lab  Single-Sensing Detection ( homogeneous ) 16 Thorem 1. Thorem 3. Thorem 2. Possion distributed: (1) (2) (3)

Network E.M.U Application Lab  K-Sensing Detection ( homogeneous ) 17 Thorem 4. Thorem 5. Thorem 6. (4) (5) (6)

Network E.M.U Application Lab  Detection Model: 18 Fig. 7. Intrusion detection at the start point (D h =0). Fig. 8. Intrusion detection in the heterogeneous WSN (D h = ξ). Intruder I Intruder II Intruder I

Network E.M.U Application Lab  Single-Sensing Detection ( heterogeneous ) 19 Thorem 7. Thorem 8. Thorem 9. (7) (8) (9)

Network E.M.U Application Lab  K-Sensing Detection ( heterogeneous ) 20 Thorem 12. Thorem 10. Thorem 11. (10) (11) (12)

Network E.M.U Application Lab  Incorporating Node Availability  RIS(Random Independent Sleeping) scheme.  The authors assume all sensors have the same availability probability, denoted by p a.  Therefore, the above analysis can be extended to incorporate RIS scheme with a node availability rate p a by replacing the previous node densities, λ, λ 1, and λ 2 with λp a, λ 1 p a1, and λ 2 p a2, respectively. 21

Network E.M.U Application Lab  A WSN must provide satisfactory connectivity so that sensors can communicate for data collaboration and reporting to the administrative center.  In a heterogeneous WSN, the deployment of sensors with different capability complicates the network operation with the asymmetric links. 22

Network E.M.U Application Lab  In a heterogeneous WSN, sensors mainly use a broadcast paradigm for communication and high-capacity sensors usually undertake more important tasks. 23

Network E.M.U Application Lab  Two fundamental characteristics of a heterogeneous WSN. The definitions are listed below:  Network connectivity ▪ The probability that a packet broadcasted from any sensor can reach all the other sensors in the network.  Broadcast reachability ▪ The probability that a packet broadcasted from any Type I sensor can reach all the other sensors in the network. 24 (13) (14)

Network E.M.U Application Lab  The results indicate that for a given heterogeneous WSN, the network connectivity and broadcast reachability is enhanced with the increase of node density and transmission range. 25

Network E.M.U Application Lab  The analytical and simulation results are compared by varying the sensing range, transmission range, node density, and node availability.  In the simulation, sensors are deployed in accordance with a uniform distribution in a squared network domain. 26

Network E.M.U Application Lab  The intruder moves into the network domain from a randomly selected point on the network boundary.  The simulation includes that:  Verification for Homogeneous WSNs  Verification for Heterogeneous WSNs  Verification for Network Connectivity and Broadcast Reachability 27

Network E.M.U Application Lab  Verification for Homogeneous WSNs 28 Simulation Parameters

Network E.M.U Application Lab 29 Fig. 10. Intrusion detection probability (single and three-sensing) in the homogeneous WSN. Fig. 11. Average intrusion distance (single and three-sensing in the homogeneous WSN.) Simulation results(Verification for Homogeneous WSNs) Sensing range : 0~30 meter r s, P r s, E(D)

Network E.M.U Application Lab  Verification for Heterogeneous WSNs 30 Simulation Parameters

Network E.M.U Application Lab 31 Fig. 12. Intrusion detection probability under heterogeneous case. Simulation results(Verification for Heterogeneous WSNs) Heter[k=1] Homo[k=1] Heter[k=3] Homo[k=3] Type I, P

Network E.M.U Application Lab  Verification for Network Connectivity and Broadcast Reachability 32 Simulation Parameters

Network E.M.U Application Lab 33 Fig. 14. Effects of Type I sensors on the broadcast reachability in heterogeneous WSN. Fig. 13. Effects of transmission range on the broadcast reachability in heterogeneous WSN. Simulation results(Verification for Network Connectivity and Broadcast Reachability) br(heter) con(heter) con(homo) con(heter) r x, P br, P con Type I, P br, P con

Network E.M.U Application Lab  This paper analyzes the intrusion detection problem in both homogeneous and heterogeneous WSNs by characterizing intrusion detection probability with respect to the intrusion distance and the network parameters. 34

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