Natraj Jaganmohan (njaganm) Sandeep A Rao (sarao)

Slides:



Advertisements
Similar presentations
Numbers Treasure Hunt Following each question, click on the answer. If correct, the next page will load with a graphic first – these can be used to check.
Advertisements

Scenario: EOT/EOT-R/COT Resident admitted March 10th Admitted for PT and OT following knee replacement for patient with CHF, COPD, shortness of breath.
1 ZonicBook/618EZ-Analyst Resonance Testing & Data Recording.
Variations of the Turing Machine
Angstrom Care 培苗社 Quadratic Equation II
AP STUDY SESSION 2.
1
Copyright © 2003 Pearson Education, Inc. Slide 1 Computer Systems Organization & Architecture Chapters 8-12 John D. Carpinelli.
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 11 Information.
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 10 User.
Cognitive Radio Communications and Networks: Principles and Practice By A. M. Wyglinski, M. Nekovee, Y. T. Hou (Elsevier, December 2009) 1 Chapter 12 Cross-Layer.
1 Copyright © 2013 Elsevier Inc. All rights reserved. Chapter 4 Computing Platforms.
Processes and Operating Systems
Copyright © 2011, Elsevier Inc. All rights reserved. Chapter 6 Author: Julia Richards and R. Scott Hawley.
Author: Julia Richards and R. Scott Hawley
Properties Use, share, or modify this drill on mathematic properties. There is too much material for a single class, so you’ll have to select for your.
1 Hyades Command Routing Message flow and data translation.
David Burdett May 11, 2004 Package Binding for WS CDL.
1 RA I Sub-Regional Training Seminar on CLIMAT&CLIMAT TEMP Reporting Casablanca, Morocco, 20 – 22 December 2005 Status of observing programmes in RA I.
Local Customization Chapter 2. Local Customization 2-2 Objectives Customization Considerations Types of Data Elements Location for Locally Defined Data.
Custom Statutory Programs Chapter 3. Customary Statutory Programs and Titles 3-2 Objectives Add Local Statutory Programs Create Customer Application For.
1 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt BlendsDigraphsShort.
1 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt Wants.
Supported by 1 1 kids learn from people who care welcome! velkomin!
1 Click here to End Presentation Software: Installation and Updates Internet Download CD release NACIS Updates.
The 5S numbers game..
1 Daily ATM/Debit Maintenance through CU*BASE A Preview of ATM and Debit Card Maintenance Screens Prepared June 24, 2009.
© SafeNet Confidential and Proprietary Administering SafeNet StorageSecure Smart Card Module 3: Lesson 5 SafeNet StorageSecure Storage Security Course.
Break Time Remaining 10:00.
Turing Machines.
Table 12.1: Cash Flows to a Cash and Carry Trading Strategy.
PP Test Review Sections 6-1 to 6-6
1 Atomic Routing Games on Maximum Congestion Costas Busch Department of Computer Science Louisiana State University Collaborators: Rajgopal Kannan, LSU.
Bright Futures Guidelines Priorities and Screening Tables
EIS Bridge Tool and Staging Tables September 1, 2009 Instructor: Way Poteat Slide: 1.
Chi-Square and Analysis of Variance (ANOVA)
Outline Minimum Spanning Tree Maximal Flow Algorithm LP formulation 1.
Health Artifact and Image Management Solution (HAIMS)
Bellwork Do the following problem on a ½ sheet of paper and turn in.
CS 6143 COMPUTER ARCHITECTURE II SPRING 2014 ACM Principles and Practice of Parallel Programming, PPoPP, 2006 Panel Presentations Parallel Processing is.
Exarte Bezoek aan de Mediacampus Bachelor in de grafische en digitale media April 2014.
Sample Service Screenshots Enterprise Cloud Service 11.3.
Copyright © 2012, Elsevier Inc. All rights Reserved. 1 Chapter 7 Modeling Structure with Blocks.
1 RA III - Regional Training Seminar on CLIMAT&CLIMAT TEMP Reporting Buenos Aires, Argentina, 25 – 27 October 2006 Status of observing programmes in RA.
Basel-ICU-Journal Challenge18/20/ Basel-ICU-Journal Challenge8/20/2014.
1..
CONTROL VISION Set-up. Step 1 Step 2 Step 3 Step 5 Step 4.
Adding Up In Chunks.
1 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt 10 pt 15 pt 20 pt 25 pt 5 pt Synthetic.
Subtraction: Adding UP
1 hi at no doifpi me be go we of at be do go hi if me no of pi we Inorder Traversal Inorder traversal. n Visit the left subtree. n Visit the node. n Visit.
Analyzing Genes and Genomes
Speak Up for Safety Dr. Susan Strauss Harassment & Bullying Consultant November 9, 2012.
McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 12 View Design and Integration.
Essential Cell Biology
Converting a Fraction to %
Numerical Analysis 1 EE, NCKU Tien-Hao Chang (Darby Chang)
Clock will move after 1 minute
PSSA Preparation.
Essential Cell Biology
Immunobiology: The Immune System in Health & Disease Sixth Edition
Physics for Scientists & Engineers, 3rd Edition
Energy Generation in Mitochondria and Chlorplasts
Select a time to count down from the clock above
4/4/2015Slide 1 SOLVING THE PROBLEM A one-sample t-test of a population mean requires that the variable be quantitative. A one-sample test of a population.
User Security for e-Post Applications Dr Chandana Gamage University of Moratuwa.
1 Decidability continued…. 2 Theorem: For a recursively enumerable language it is undecidable to determine whether is finite Proof: We will reduce the.
Where Are You From? Confusing Location Distinction Using Virtual Multipath Camouflage Song Fang, Yao Liu Wenbo Shen, Haojin Zhu 1.
Overcoming the Sensing-Throughput Tradeoff in Cognitive Radio Networks ICC 2010.
Presentation transcript:

Natraj Jaganmohan (njaganm) Sandeep A Rao (sarao) Mitigation of Primary User Emulation Attack using Time of Emission Estimation Natraj Jaganmohan (njaganm) Sandeep A Rao (sarao) CSC774 - NCSU ADVANCED NETWORK SECURITY

Agenda of the presentation: Background about Cognitive Radio Networks Primary User Emulation Attack (PUEA) Existing approaches to solve PUEA. PUEA attack model with Directional antennas. Attack mitigation using TOE estimation. Simulation results. Limitations of the approach. Future directions of research. CSC774 - NCSU ADVANCED NETWORK SECURITY

It all started here: “All consumers . . . deserve a new spectrum policy paradigm that is rooted in modern-day technologies and markets. We are living in a world where demand for spectrum is driven by an explosion of wireless technology and the ever-increasing popularity of wireless services. Nevertheless, we are still living under a spectrum 'management' regime that is 90 years old. It needs a hard look, and in my opinion, a new direction.” Michael K. Powell (Chairman FCC Spectrum Policy Task Force) CSC774 - NCSU ADVANCED NETWORK SECURITY

Spectrum Scarcity: Cognitive Networks help us solve the problem. CSC774 - NCSU ADVANCED NETWORK SECURITY

Background: Cognitive Radio Networks. Wireless spectrum is very scarce leading to spectrum crisis. FCC recommends use of opportunistic or cognitive networks to increase spectrum utilization. This technology would put unused and under-used spectrum assets to work – without impacting  primary users within those bands. It is a bold, yet workable solution. Spectrum is now being considered as a resource and a very scarce resource. Hence re-using the spectrum is a very good option which FCC recommends. Cognitive Radio Networks have the spectrum sensing technology and hence, they sense the spectrum for its usage. If its not being used, then it can be used to transfer information. But, the secondary transmissions should not affect the primary licensed user. CSC774 ADVANCED NETWORK SECURITY

Background: Cognitive Radio Networks. “A Cognitive Radio is a radio frequency transmitter/receiver that is designed to intelligently detect whether a particular segment of the radio spectrum is currently in use, and to jump into (and out of, as necessary) the temporarily-unused spectrum very rapidly, without interfering with the transmissions of other authorized users.” http://www.ieeeusa.org/forum/POSITIONS/cognitiveradi o.html CSC774 - NCSU ADVANCED NETWORK SECURITY

Cognitive Radio networks operation: PU-Tx PU-RX PU-RX SU SU PU-RX CSC774 - NCSU ADVANCED NETWORK SECURITY

What makes Cognitive Networks possible? Key enablers of CRNs: Radio manufacturers have started to create flexible software-defined radios. Research funding and support for spectrum re- use. Support for Dynamic Channel selection, channel scanning and adjustable transmission power. Radio manufacturers have started to create flexible software-defined radios that reveal the low-level radio parameters and functionalities, and support the dynamic reconfiguration of the complete protocol stack. Many companies have invested heavily in Cognitive network research. FCC also recommends and supports Cognitive networks. CSC774 - NCSU ADVANCED NETWORK SECURITY

Some terminologies used in this presentation: CRN: Cognitive Radio Network PU: Primary User (licensed user) SU: Secondary user (CRN node) PUEA: Primary User Emulation Attack FC: Fusion Center TOE: Time of Emission TOA: Time of Arrival. CSC774 - NCSU ADVANCED NETWORK SECURITY

Most important attacks on CRNs Spectrum data falsification attacks: In this case, one or more SUs are compromised and hence report wrong sensing values to FC. This makes the FC make incorrect decision about the presence of PU. The most preferred way to mitigate the attack is to collect sensing values from a group of SUs and remove the outlier values. CSC774 ADVANCED NETWORK SECURITY

Primary User Emulation Attack: Primary Transmitter PU1 PU2 SU2 SU1 PU3 CSC774 - NCSU ADVANCED NETWORK SECURITY

Primary User Emulation Attack: Primary Transmitter PU1 PU2 In this attack, the attacker impersonates the primary user. The attacker tries to emulate the wireless signal characteristics of the primary user in his absence. The secondary nodes need some way to distinguish the signals sent by the malicious PU emulator. Attacker SU2 SUs cannot access channel as they think PU is transmitting SU1 PU3 CSC774 - NCSU ADVANCED NETWORK SECURITY

Why are we facing this attack : Secondary users cannot authenticate the PU transmission. FCC states that PU cannot be modified to support security. Hence regular authentication schemes don’t work. CSC774 - NCSU ADVANCED NETWORK SECURITY

General approaches to defeat this attack: Solution 1 RSSI based PU localization: Decision is made based on all received sensing reports (x,y) FC RSSI values are measured at all SUs and calculate the location of PU. Ideal case of a PU transmitting, all RSSI values will be correct w.r.t distance CSC774 - NCSU ADVANCED NETWORK SECURITY

Solution 1 proposed by: Zhou Yuan et al, suggested the use of localization schemes to estimate and authenticate the location of PU. Scheme based on Received signal power. Pr = Pt + a 10 log (do/d) + w It can be defeated by attacker by using Antenna arrays with different power levels. CSC774 - NCSU ADVANCED NETWORK SECURITY

General approaches to defeat this attack: Solution 2 Dr. Peng Ning et al proposed integrating cryptographic signatures and wireless link signatures to enable primary user detection. Essential to the approach is a helper node placed physically close to a primary user. CSC774 - NCSU ADVANCED NETWORK SECURITY

General approaches to defeat this attack: Solution 2 Working with helper nodes. Helper Node (x,y) Helper node transmits signals identical to PU SUs can try to verify the PU authenticity by verifying the Wireless Link signature of Helper node CSC774 - NCSU ADVANCED NETWORK SECURITY

General approaches to defeat this attack: Solution 2 This technique is very effective in terms of authenticating primary user. We exploit the proximity of Helper node with PU. Problem is the authentication of wireless link signature of the helper node. Also if attackers are placed near helper nodes, then it causes problems. CSC774 - NCSU ADVANCED NETWORK SECURITY

General approaches to defeat this attack: Solution 3 IRIS model proposed by Alexander et al, has a secure attack detection by verifying the consistency of system state (Transmit power and path loss). This technique is very effective and it defeats both Data Falsification attacks and PUEA. But, it fails in the case of attacker with antenna arrays and directional antenna. CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Assumptions : All nodes are loosely time synchronized. Location of PU is fixed and known to all SUs. Fusion Center is used to make decision about presence of PU. All SUs are connected to FC using a secure link. There is a LOS path between every SU and PU. CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model : Motivation This attack model fails all the localization based solutions for PUEA which have been proposed previously. Attacker uses a multi antenna array or MIMO technology with directional antennas to send PU-TX like signals to different SUs with various power levels faking the presence of PU. CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Representation The power levels at different nodes are expected with respect to the distance from the PU-TX. CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Antenna array – multiple antenna transmitter CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: This attack is possible because: 1. Antenna arrays are low cost and easy to setup 2. Attacker can manipulate the power levels in each directional beam from every antenna element to make sure every SU calculates the RSSI equal to the RSSI when PU transmits. CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Validation We have simulated the attack model to verify whether such an attack is really possible. Modeler: Opnet Network modeler 16 CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Directional Antenna pattern formation in Opnet CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Directional Antenna pattern formation in Opnet CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Directional Antenna pattern formation in Opnet CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: A sample scenario proving the possibility of attack CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Throughput graphs. PU-TX (antenna 1) SU-1 SU-2 CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Multiple antenna array simulation. Ref: http://fens.sabanciuniv.edu/telecom/eng/comnet/cisco/smart.htm CSC774 - NCSU ADVANCED NETWORK SECURITY

Attack model: Validation Hence if the attacker can configure each antenna element with the appropriate power levels to produce required RSSI values at each SU, then attack is achieved. Regular localization based methods cannot defeat this attack. This forms the motivation for our solution. CSC774 - NCSU ADVANCED NETWORK SECURITY CSC774 DVANCED NETWORK SECURITY

Time of Emission Estimation Based Approach : Our solution to PUEA CSC774 - NCSU ADVANCED NETWORK SECURITY

Model SU PU SU Fusion SU Center PUE SU CSC774 - NCSU ADVANCED NETWORK SECURITY

Assumptions Secondary Users and Fusion Center Fusion Center are loosely Synchronized have secure communication Fusion Center cannot be compromised knows locations of all users (secondary as well as primary) has good computational power and storage CSC774 - NCSU ADVANCED NETWORK SECURITY

Attacker Capabilities Can use antenna array But transmitting with a beam formation at different locations at different times is restricted. Multiple Attackers can coordinate They can be synchronized among themselves Attacker knows location of all nodes SU may be compromised CSC774 - NCSU ADVANCED NETWORK SECURITY

Proposed Approach Sensors measure Time of Arrival Fusion Center estimates Time of Emission Robust against, Multiple, coordinated attackers Multiple compromised secondary users Node with Antenna Array! CSC774 - NCSU ADVANCED NETWORK SECURITY

Design TOE estimated for every sensor must be Estimate TOA! PU TOA SU Estimate TOA! TOA SU PUEA result Fusion Center PUEA result TOE estimated for every sensor must be almost same in an ideal scenario Estimate TOE! In the presence of an attack there will be deviations in some TOE estimations CSC774 - NCSU ADVANCED NETWORK SECURITY

Intuition Time CSC774 - NCSU ADVANCED NETWORK SECURITY

Procedure FOR EACH NODE MEASURE TOE! TOEi = TOAi – Dist/c + ξ FC TOA TOA TOA TOA TOA FOR EACH NODE MEASURE TOE! TOEi = TOAi – Dist/c + ξ COMPUTE MEAN  TOEmean CSC774 - NCSU ADVANCED NETWORK SECURITY

Procedure FOR EACH NODE, MEASURE DEVIATION! δi = TOEAVG ~ TOEi If δi > μ Increment C μ -> Maximum allowable deviation! C -> number of deviated values If C > k then PUEA! k -> Maximum no. of allowable deviated reports CSC774 - NCSU ADVANCED NETWORK SECURITY

Parameters! Determining μ Determining k The maximum deviation in the measurement by a node under a non-attack scenario! Determining k Too small? Increase in false negative! Too large? Increase in false alarm! Tradeoff needed! CSC774 - NCSU ADVANCED NETWORK SECURITY

Simulation Results CSC774 - NCSU ADVANCED NETWORK SECURITY

Limitation If an attacker is capable of compromising almost every node! Attacker too powerful! Note: We have a threshold which is used to tolerate certain number of configured node compromises. But, if almost all nodes in network are compromised, then the network is not useful. CSC774 - NCSU ADVANCED NETWORK SECURITY

Future work FCC may relax rule “no modification to the incumbent (primary) system should be required to accommodate opportunistic use of the spectrum by secondary users” Already relaxed for wireless microphones Removing Fusion Center May decrease latency and increase performance of system. CSC774 - NCSU ADVANCED NETWORK SECURITY

Summary An Attack Model against the approaches using RSSI is proposed and simulated A Novel approach to mitigate PUEA is proposed using Time of Emission Estimation and simulated Approach is compared with a similar RSSI based approach CSC774 - NCSU ADVANCED NETWORK SECURITY

Thank you! CSC774 - NCSU ADVANCED NETWORK SECURITY