Improving robustness of a popular probabilistic clustering algorithm against insider attacks

Sayed M. Sayed; Tom La Porta; Simone Silvestri; Patrick McDaniel

doi:10.1007/978-3-030-63086-7_21

Improving robustness of a popular probabilistic clustering algorithm against insider attacks

Sayed M. Sayed, Tom La Porta, Simone Silvestri, Patrick McDaniel

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Many clustering algorithms for mesh, ad hoc and Wireless Sensor Networks have been proposed. Probabilistic approaches are a popular class of such algorithms. However, it is essential to analyze their robustness against security compromise. We study the robustness of EEHCA, a popular energy efficient clustering algorithm as an example of probabilistic class in terms of security compromise. In this paper, we investigate attacks on EEHCA through analysis and experimental simulations. We analytically characterize two different attack models. In the first attack model, the attacker aims to gain control over the network by stealing network traffic, or by disrupting the data aggregation process (integrity attack). In the second attack model, the inducement of the attacker is to abridge the network lifetime (denial of service attack). We assume the clustering algorithm is running periodically and propose a detection solution by exploiting Bernoulli CUSUM charts.

Original language	English
Title of host publication	Security and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings
Editors	Noseong Park, Kun Sun, Sara Foresti, Kevin Butler, Nitesh Saxena
Pages	381-401
Number of pages	21
DOIs	https://doi.org/10.1007/978-3-030-63086-7_21
State	Published - 2020
Event	16th International Conference on Security and Privacy in Communication Networks, SecureComm 2020 - Washington, United States Duration: Oct 21 2020 → Oct 23 2020

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	335
ISSN (Print)	1867-8211

Conference

Conference	16th International Conference on Security and Privacy in Communication Networks, SecureComm 2020
Country/Territory	United States
City	Washington
Period	10/21/20 → 10/23/20

Bibliographical note

Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

Funding

Acknowledgments. Research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.

Funders	Funder number
Army Research Laboratory	W911NF-13-2-0045

Keywords

Anomaly detection
CUSUM test
Probabilistic clustering algorithm

ASJC Scopus subject areas

Computer Networks and Communications

Access to Document

10.1007/978-3-030-63086-7_21

Cite this

Sayed, S. M., La Porta, T., Silvestri, S., & McDaniel, P. (2020). Improving robustness of a popular probabilistic clustering algorithm against insider attacks. In N. Park, K. Sun, S. Foresti, K. Butler, & N. Saxena (Eds.), Security and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings (pp. 381-401). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 335). https://doi.org/10.1007/978-3-030-63086-7_21

Sayed, Sayed M. ; La Porta, Tom ; Silvestri, Simone et al. / Improving robustness of a popular probabilistic clustering algorithm against insider attacks. Security and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings. editor / Noseong Park ; Kun Sun ; Sara Foresti ; Kevin Butler ; Nitesh Saxena. 2020. pp. 381-401 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

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abstract = "Many clustering algorithms for mesh, ad hoc and Wireless Sensor Networks have been proposed. Probabilistic approaches are a popular class of such algorithms. However, it is essential to analyze their robustness against security compromise. We study the robustness of EEHCA, a popular energy efficient clustering algorithm as an example of probabilistic class in terms of security compromise. In this paper, we investigate attacks on EEHCA through analysis and experimental simulations. We analytically characterize two different attack models. In the first attack model, the attacker aims to gain control over the network by stealing network traffic, or by disrupting the data aggregation process (integrity attack). In the second attack model, the inducement of the attacker is to abridge the network lifetime (denial of service attack). We assume the clustering algorithm is running periodically and propose a detection solution by exploiting Bernoulli CUSUM charts.",

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Sayed, SM, La Porta, T, Silvestri, S & McDaniel, P 2020, Improving robustness of a popular probabilistic clustering algorithm against insider attacks. in N Park, K Sun, S Foresti, K Butler & N Saxena (eds), Security and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 335, pp. 381-401, 16th International Conference on Security and Privacy in Communication Networks, SecureComm 2020, Washington, United States, 10/21/20. https://doi.org/10.1007/978-3-030-63086-7_21

Improving robustness of a popular probabilistic clustering algorithm against insider attacks. / Sayed, Sayed M.; La Porta, Tom; Silvestri, Simone et al.
Security and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings. ed. / Noseong Park; Kun Sun; Sara Foresti; Kevin Butler; Nitesh Saxena. 2020. p. 381-401 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 335).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N1 - Publisher Copyright: © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020.

PY - 2020

Y1 - 2020

N2 - Many clustering algorithms for mesh, ad hoc and Wireless Sensor Networks have been proposed. Probabilistic approaches are a popular class of such algorithms. However, it is essential to analyze their robustness against security compromise. We study the robustness of EEHCA, a popular energy efficient clustering algorithm as an example of probabilistic class in terms of security compromise. In this paper, we investigate attacks on EEHCA through analysis and experimental simulations. We analytically characterize two different attack models. In the first attack model, the attacker aims to gain control over the network by stealing network traffic, or by disrupting the data aggregation process (integrity attack). In the second attack model, the inducement of the attacker is to abridge the network lifetime (denial of service attack). We assume the clustering algorithm is running periodically and propose a detection solution by exploiting Bernoulli CUSUM charts.

AB - Many clustering algorithms for mesh, ad hoc and Wireless Sensor Networks have been proposed. Probabilistic approaches are a popular class of such algorithms. However, it is essential to analyze their robustness against security compromise. We study the robustness of EEHCA, a popular energy efficient clustering algorithm as an example of probabilistic class in terms of security compromise. In this paper, we investigate attacks on EEHCA through analysis and experimental simulations. We analytically characterize two different attack models. In the first attack model, the attacker aims to gain control over the network by stealing network traffic, or by disrupting the data aggregation process (integrity attack). In the second attack model, the inducement of the attacker is to abridge the network lifetime (denial of service attack). We assume the clustering algorithm is running periodically and propose a detection solution by exploiting Bernoulli CUSUM charts.

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Sayed SM, La Porta T, Silvestri S, McDaniel P. Improving robustness of a popular probabilistic clustering algorithm against insider attacks. In Park N, Sun K, Foresti S, Butler K, Saxena N, editors, Security and Privacy in Communication Networks - 16th EAI International Conference, SecureComm 2020, Proceedings. 2020. p. 381-401. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-63086-7_21

Improving robustness of a popular probabilistic clustering algorithm against insider attacks

Abstract

Publication series

Conference

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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