Quantifying Trust and Reputation for Defense against Adversaries in Multi-Channel Dynamic

208 Pages·2015·1.55 MB·English

by  Shameek Bhattacharjee

Quantifying Trust and Reputation for Defense against
Adversaries in Multi Channel Dynamic Spectrum Access
Networks
by
Shameek Bhattacharjee
B.Tech., West Bengal University of Technology, 2009, India
M.S., University of Central Florida, 2011, USA
A dissertation submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the Department of Electrical Engineering and Computer Science
in the College of Engineering and Computer Science
at the University of Central Florida
Orlando, Florida
Summer Term
2015
Major Professor: Mainak Chatterjee
(cid:13)c 2015 Shameek Bhattacharjee
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ABSTRACT
Dynamic spectrum access enabled by cognitive radio networks are envisioned to drive
the next generation wireless networks that can increase spectrum utility by opportunistically
accessing unused spectrum. Due to the policy constraint that there could be no interference
to the primary (licensed) users, secondary cognitive radios have to continuously sense for
primary transmissions. Typically, sensing reports from multiple cognitive radios are fused
as stand-alone observations are prone to errors due to wireless channel characteristics. Such
dependence on cooperative spectrum sensing is vulnerable to attacks such as Secondary
Spectrum Data Falsification (SSDF) attacks when multiple malicious or selfish radios falsify
the spectrum reports. Hence, there is a need to quantify the trustworthiness of radios that
share spectrum sensing reports and devise malicious node identification and robust fusion
schemes that would lead to correct inference about spectrum usage.
In this work, we propose an anomaly monitoring technique that can effectively cap-
ture anomalies in the spectrum sensing reports shared by individual cognitive radios during
cooperative spectrum sensing in a multi-channel distributed network. Such anomalies are
used as evidence to compute the trustworthiness of a radio by its neighbors. The proposed
anomaly monitoring technique works for any density of malicious nodes and for any physical
environment. We propose an optimistic trust heuristic for a system with a normal risk atti-
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tude and show that it can be approximated as a beta distribution. For a more conservative
system, we propose a multinomial Dirichlet distribution based conservative trust framework,
where Josang’s Belief model is used to resolve any uncertainty in information that might
arise during anomaly monitoring. Using a machine learning approach, we identify malicious
nodes with a high degree of certainty regardless of their aggressiveness and variations intro-
duced by the pathloss environment. We also propose extensions to the anomaly monitoring
technique that facilitate learning about strategies employed by malicious nodes and also
utilize the misleading information they provide.
We also devise strategies to defend against a collaborative SSDF attack that is
launched by a coalition of selfish nodes. Since, defense against such collaborative attacks is
difficult with popularly used voting based inference models or node centric isolation tech-
niques, we propose a channel centric Bayesian inference approach that indicates how much
the collective decision on a channels occupancy inference can be trusted. Based on the mea-
sured observations over time, we estimate the parameters of the hypothesis of anomalous and
non-anomalous events using a multinomial Bayesian based inference. We quantitatively de-
fine the trustworthiness of a channel inference as the difference between the posterior beliefs
associated with anomalous and non-anomalous events. The posterior beliefs are updated
based on a weighted average of the prior information on the belief itself and the recently
observed data.
Subsequently, we propose robust fusion models which utilize the trusts of the nodes
to improve the accuracy of the cooperative spectrum sensing decisions. In particular, we
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propose three fusion models: (i) optimistic trust based fusion, (ii) conservative trust based
fusion, and (iii) inversion based fusion. The former two approaches exclude untrustwor-
thy sensing reports for fusion, while the last approach utilizes misleading information. All
schemes are analyzed under various attack strategies. We propose an asymmetric weighted
moving average based trust management scheme that quickly identifies on-off SSDF at-
tacks and prevents quick trust redemption when such nodes revert back to temporal honest
behavior. We also provide insights on what attack strategies are more effective from the
adversaries’ perspective.
Through extensive simulation experiments we show that the trust models are effective
in identifying malicious nodes with a high degree of certainty under variety of network and
radio conditions. We show high true negative detection rates even when multiple malicious
nodes launch collaborative attacks which is an improvement over existing voting based ex-
clusion and entropy divergence techniques. We also show that we are able to improve the
accuracy of fusion decisions compared to other popular fusion techniques. Trust based fusion
schemes show worst case decision error rates of 5% while inversion based fusion show 4% as
opposed majority voting schemes that have 18% error rate. We also show that the proposed
channel centric Bayesian inference based trust model is able to distinguish between attacked
and non-attacked channels for both static and dynamic collaborative attacks. We are also
able to show that attacked channels have significantly lower trust values than channels that
are not– a metric that can be used by nodes to rank the quality of inference on channels.
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To my parents and teachers.
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ACKNOWLEDGMENTS
I am grateful to my advisor Dr. Mainak Chatterjee for guiding, supporting and believing in
me over the years of my Ph.D. Without his experienced mentoring, my dream of earning a
Ph.Dwouldnothavecometrue. Iwouldliketoexpresssincereappreciationtomycommittee
members Dr. Necati F. Catbas, Dr. Ratan Guha, Dr. Damla Turgut and Dr. Cliff Zou
for serving in my committee. Their constructive feedback and comments have helped me in
improving my dissertation. I would also like to thank Dr. Kevin Kwiat and Dr. Charles
Kamhoua of the Air Force Research Lab, Rome, NY for providing me with collaborative
researchopportunities. IwouldliketothankallmycolleaguesattheNetMoClaboratoryand
all my friends who have always inspired me while working as a Ph.D. student. In addition,
I would like to thank the Air Force Research Lab (AFRL) and Department of Electrical
Engineering and Computer Science at the University of Central Florida for partially funding
my studies. I would like to acknowledge the role of my parents and family members for
motivating and supporting my desire of pursuing higher studies.
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TABLE OF CONTENTS
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xviii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxii
CHAPTER 1: INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Cognitive Radio Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Vulnerabilities In Cooperative Spectrum Sensing . . . . . . . . . . . . . . . 3
1.3 Contributions of this Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.4 Benefits of this Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.5 Organization of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . 8
CHAPTER 2: CRNs AND VULNERABILITIES . . . . . . . . . . . . . . . . . . . . 9
2.1 Architectural Aspects and Operational Weaknesses . . . . . . . . . . . . . . 9
2.2 Main Operational Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
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2.3 Categories of Vulnerabilities in Cooperative Spectrum Sensing . . . . . . . . 12
2.3.1 Objective of adversarial attackers . . . . . . . . . . . . . . . . . . . . 13
2.3.2 Impact of attack on the victims . . . . . . . . . . . . . . . . . . . . . 14
2.3.3 Nature of manipulation . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.4 Secondary Spectrum Data Falsification (SSDF) or Byzantine attacks . . . . 17
2.5 SSDF Attack Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.5.1 Magnitude of attack . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.5.2 Collaborative vs. non-collaborative strategies . . . . . . . . . . . . . 19
2.5.3 Malicious vs. selfish nodes . . . . . . . . . . . . . . . . . . . . . . . . 20
2.5.4 Random on-off attack . . . . . . . . . . . . . . . . . . . . . . . . . . 20
CHAPTER 3: BACKGROUND AND RELATED WORK . . . . . . . . . . . . . . . 22
3.1 Background on Trust Reputation and Recommendation . . . . . . . . . . . 22
3.1.1 Trust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.1.2 Categories of formal trust metrics . . . . . . . . . . . . . . . . . . . 24
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3.1.3 Reputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
3.1.4 Applying trust and reputation for cooperative decisions . . . . . . . 27
3.2 Literature of SSDF Attack Remedies . . . . . . . . . . . . . . . . . . . . . . 28
3.2.1 CatchIt: Heuristic onion peeling approach . . . . . . . . . . . . . . . 28
3.2.2 Weighted sequential probability ratio test . . . . . . . . . . . . . . . 29
3.2.3 Abnormality detection using double sided neighbor distance algorithm 31
3.2.4 Two-tier optimal cooperation based secure spectrum sensing . . . . . 31
3.2.5 KL divergence based defense . . . . . . . . . . . . . . . . . . . . . . 32
3.2.6 Majority voting based exclusion with long term reputation . . . . . . 33
3.2.7 Bio inspired consensus based cooperative sensing scheme . . . . . . . 34
3.3 Motivation for this Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
CHAPTER 4: ANOMALY MONITORING TECHNIQUE FOR SSDF ATTACKS . 37
4.1 System Model and Assumptions . . . . . . . . . . . . . . . . . . . . . . . . 37
4.1.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
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