A SECURE BARRIER COVERAGE SCHEDULING FRAMEWORK FOR WSN-

BASED IOT APPLICATIONSD I YA T H O M A S, R A J A N S H A N K A R A N

Presenter: Diya ThomasDepartment of ComputingMacquarie UniversitySydney, Australia

11/20/2020 MSWIM 2020 1

MSWiM 2020

OVERVIEWMotivation

Contribution

Literature review

Limitation

Proposed framework

Problem formulation

Proposed approach

Performance analysis

Conclusion

11/20/2020 MSWIM 2020 2

MOTIVATION

Wireless sensor networks (WSNs) are network enabling technology for a varietyof WSN-based IoT surveillance applications.

The essential Quality of Service (QoS) requirements of these applicationsinclude extended network lifetime, coverage, and connectivity.

11/20/2020 MSWIM 2020 3

BARRIER COVERAGE SCHEDULING(ENERGY CONSERVATION SCHEME)

Barrier scheduling is a flexible energy conservation scheme that can be designed toguarantee these QoS requirements.

Only a subset of sensor nodes called barriers are activated to conserve energy.

11/20/2020 MSWIM 2020 4

A B C D

E

INTENTIONAL FAULT-SECURITY ATTACKS

The failures of sensor nodes in the barrier can significantly impact the efficacy ofbarrier scheduling to meet QoS requirements.

Faults that cause these failures can be unintentional (software or hardware failures)or intentional (security attacks).

WSNs are vulnerable to different types of active security attacks.

One particular class of active attack that targets explicitly on the depletion of thebattery power of the sensor node is Depletion-of-Battery attack (DoB).

A secure barrier coverage scheduling approach should be able to effectivelyidentify and thwart such types of security attacks, which impedes it in achieving itsprimary objective of extending the network lifetime.

11/20/2020 MSWIM 2020 5

NOVELTY AND CONTRIBUTION

The paper proposes a secure barrier coverage scheduling framework that can detectlogical intrusions in the network.

The framework utilizes a novel cluster ensemble approach called KSH to detectlogical intrusions.

In contrast to the existing graph-based intrusion detection approaches that make use ofa simple static graph model, we use a novel fully weighted dynamic graph model thatencodes information at lower levels of granularity and changes its structure with thedynamics of the network.

Due to the lack of an efficient graph model, most of the existing intrusion detectionapproaches focus more on finding anomalies in operational or measurement data.Unlike those approaches, our proposed KSH approach fully exploits the structuralinformation that is captured dynamically in the graph to detect intrusion.

11/20/2020 MSWIM 2020 6

LITERATURE REVIEW

Intrusion is defined as a set of activities that compromise the security principles suchas availability, confidentiality, integrity of the network.

In an anomaly-based IDS, a normal operation profile is created by monitoring thenetwork operations. An anomaly is flagged when there is any deviation from theobserved normal profile.

The anomaly-based IDS can be broadly classified into the parametric and non-parametric approaches

11/20/2020 MSWIM 2020 7

11/20/2020 MSWIM 2020 8

LIMITATIONS

o Current barrier coverage scheduling schemes cannot detect logical intrusions

o The schemes operates on a graph model that encodes network data at a highlevel of granularity (network structure and network dynamics).

oChanges in network structure such as the appearance of new nodes/links or dis-appearance of new nodes/link, changes in the vertex or edge weights caused due toattack is not completely exploited by any of the intrusion detection approaches forWSN

11/20/2020 MSWIM 2020 9

PROPOSED SECURE BARRIER COVERAGE SCHEDULING FRAMEWORK

11/20/2020 MSWIM 2020 10

[1] Diya Thomas et.al., ‘‘Energy efficient military surveillance: Coverage meets connectivity,’’ IEEE Sensors Journal, vol. 19, no. 10, pp.

3902–3911, May 2019.

[2] Diya Thomas et.al., "A Graph-Based Fault-Tolerant Approach to modelling QoS for IoT-based Surveillance Applications," in IEEE

Internet of Things Journal, doi: 10.1109/JIOT.2020.3022941.

DYNAMIC WEIGHTED GRAPH MODEL

11/20/2020 MSWIM 2020 11

GRAPH CLEANSING

oGraph cleansing is a process of detecting and removing malicious nodes in thenetwork.

oIt is the logical intrusion detection stage of the barrier coverage schedulingframework.

11/20/2020 MSWIM 2020 12

PROBLEM FORMULATION:DETECTION OF ANOMALOUS GRAPH SNAPSHOTS

11/20/2020 MSWIM 2020 13

Problem:

EXAMPLE

11/20/2020 MSWIM 2020 14

FEATURE SELECTION

Features Selected

The number of cliques (NC) is calculated by modifying the Bron-Kerbosch algorithm.

11/20/2020 MSWIM 2020 15

CONSTRUCTING SIMILARITY MATRIX

11/20/2020 MSWIM 2020 16

APPROACH-CLUSTER ENSEMBLE

11/20/2020 MSWIM 2020 17

CALCULATING ANOMALY SCORE

11/20/2020 MSWIM 2020 18

PROBLEM FORMULATION:DETECTION OF ANOMALOUS VERTICES

11/20/2020 MSWIM 2020 19

Problem

THRESHOLDING BASED APPROACH

11/20/2020 MSWIM 2020 20

A

B

C

0.1

0.1

2

D

0.2

EXPERIMENTS-ROC CURVE

11/20/2020 MSWIM 2020 21

ACCURACY VS NUMBER OF GRAPH SNAPSHOTS

11/20/2020 MSWIM 2020 22

CLASSIFICATION AND RMSE ERROR

11/20/2020 MSWIM 2020 23

CONCLUSION

This paper proposes a secure fault-tolerant barrier coverage scheduling frameworkbased on a fully weighted dynamic graph model.

The framework incorporates a graph cleansing stage to detect and removeanomalous vertices and edges in the graph.

A cluster ensemble approach called KSH based on well know K-means, Spectral,and Hierarchical clustering techniques is employed to accurately and preciselyidentify and isolate the anomalies in the dynamic graph.

The experiment result shows the scalability and stability of the ensemble approachin classifying and predicting the anomalies with better accuracy and minimalclassification error.

11/20/2020 MSWIM 2020 24

FUTURE SCOPE

To modify our proposed graph model to represent heterogeneous sensor network.

To extend our security framework to operate for barrier coverage scheduling schemes for heterogeneous network.

To test our model over a real testbed.

11/20/2020 MSWIM 2020 25

REFERENCES[1] J. A. Khan, H. K. Qureshi, and A. Iqbal, “Energy management in wire-less sensor networks:A survey,” Computers & Electrical Engineering, vol. 41, pp. 159–176, 2015.

[2] H. Kim, J. A. Cobb, and J. Ben-Othman, “Maximizing the lifetime of reinforced barriers inwireless sensor networks,” Concurrency and Computation: Practice and Experience, vol. 29, no.23, p. e4070, 2017.

[3] D. Thomas, R. Shankaran, M. Orgun, M. Hitchens, and W. Ni, “Energy-efficient militarysurveillance: Coverage meets connectivity,” IEEE Sensors Journal, vol. 19, no. 10, pp. 3902–3911, 2019.

[4] V. Shakhov and I. Koo, “Depletion-of-battery attack: Specificity, modelling andanalysis,”Sensors, vol. 18, no. 6, p. 1849, 2018.

[5] Y. Zhu, M. Mei, and Z. Zheng, “Scheduling algorithms for k-barrier coverage to improvetransmission efficiency in wsns,”Multimedia Toolsand Applications, vol. 79, no. 15, pp. 10 505–10 518, 2020.

[6] H. Kim and J. Ben-Othman, “On resilient event-driven partial barriers in mobile sensornetworks,” in2016 IEEE International Conference on Communications (ICC). IEEE, 2016, pp. 1–6.

[7] D. Kim, H. Kim, D. Li, S.-S. Kwon, A. O. Tokuta, and J. A. Cobb, “Maximum lifetimedependable barrier-coverage in wireless sensornetworks,”Ad Hoc Networks, vol. 36, pp. 296–307, 2016.11/20/2020 MSWIM 2020 26

[8] A. Shan, X. Xu, Z. Cheng, and W. Wang, “A max-flow based algorithmfor connected target coverage with probabilistic sensors,”Sensors,vol. 17, no. 6, p. 1208, 2017

[9] R. Han, W. Yang, and L. Zhang, “Achieving crossed strong barriercoverage in wireless sensor network,”Sensors, vol. 18, no. 2, p. 534,2018.

[10] Z. Wang, J. Liao, Q. Cao, H. Qi, and Z. Wang, “Achieving k-barriercoverage in hybrid directional sensor networks,”IEEE Transactions onMobile Computing, vol. 13, no. 7, pp. 1443–1455, 2013.

[11] Y. Zhang, X. Sun, and Z. Yu, “Solving-barrier coverage problem usingmodifiedgravitational search algorithm,”Mathematical Problems inEngineering, vol. 2017, 2017.

[12] J. Chang, X. Shen, W. Bai, R. Zhao, and B. Zhang, “Hierarchy graphbased barrier coverage strategy with a minimum number of sensors forunderwater sensor networks,”Sensors, vol. 19, no. 11, p. 2546, 2019

11/20/2020 MSWIM 2020 27

QUESTIONS?

11/20/2020 MSWIM 2020 28