Security Threats in Cognitive Radio

Presented by:

Nagashree NDept. of TCE.

Introduction. Emerging Issues-Spectrum Management. Characteristics of Cognitive Radio.

• Cognitive Capability.• Reconfigurability.

Attacks and Detection Techniques.• Incumbent Emulation attack.• Spectrum Sensing Data Falsification attack.

Advantages of CRN. Applications of CRN. Conclusion. References.

CONTENTS

A number of wireless applications have been growing over the last decade. Most of the frequency spectrum has already been licensed by government agencies, such as Federal Communications Commission (FCC).

Therefore, there exists an apparent spectrum scarcity for new wireless applications and services. Cognitive radio can efficiently utilize the unused spectrum for secondary usage without interfering a primary licensed user.

Introduction

A Cognitive radio is a fully reconfigurable device which can observe and change or adapt its communication parameters for enabling secondary usage of the spectrum and yield an efficient usage of the spectrum.

The key motivation behind this technology is to increase spectral utilization and to optimize the use of radio resources.

The concept of cognitive radio was first proposed by Joseph Mitola III in a seminar at KTH(the Royal Institute of Technology) in 1998.

Depending on transmission and reception parameters, there are two main types of cognitive radio:

• Full Cognitive Radio(Mitola Radio).• Spectrum-Sensing Cognitive Radio.

Cognitive Radio Scenario.

Determine which portions of the spectrum are available: Spectrum Sensing.

Select the best available channel: Spectrum Decision. Coordinate access to this channel with other users: Spectrum

Sharing. Vacate the channel when a licensed user is detected:

Spectrum Mobility.

Emerging Issues-Spectrum Management

Cognitive Radio have two main characteristics:• Cognitive Capability.• Reconfigurability.

Cognitive Capability: Identify the unused spectrum at a specific time or location (Spectrum Holes/ White Spaces)

Reconfigurability: Transmit and Receive on a variety of frequencies. Use different access technologies.

Characteristics of Cognitive Radio

Spectrum hole.

CRN Architecture.

We define an attack on cognitive networks as any activity that results in (a) unacceptable interference to the licensed primary users

or (b) missed opportunities for secondary users

Here we describe the attacks against CRs and CRNs Incumbent Emulation attacks. Spectrum Sensing Data Falsification attacks. Cross-layer attacks.

Attacks and Detection Techniques

We have identified and discussed two security threats to CR networks: IE attacks and SSDF attacks. Both attacks potentially pose a great threat to CR networks. There are other types of attacks that can disrupt operations in a CR network.

For instance, simple jamming attacks may be very effective in interfering with the spectrum sensing process. However, we have limited our discussions to security issues that are unique to CR networks, with particular focus on security threats to DSS.

When an incumbent is detected in a given band, all secondaries avoid accessing that band.

In an incumbent emulation (IE) attack, a malicious secondary tries to gain priority over other secondaries by transmitting signals that emulate the characteristics of an incumbent’s.

There may be “selfish” IE attack or “malicious” IE attack.

Incumbent Emulation Attack

Malicious secondary users may take advantage of the cooperative spectrum sensing and launch SSDF attacks by sending false local spectrum sensing results to others, resulting in a wrong spectrum sensing decision.

Three attack models are presented as follows:• Selfish SSDF.• Interference SSDF.• Confusing SSDF

Spectrum Sensing Data Falsification attack

Detection of SSDF attacks assume a model where a number of SUs sense the environment and report their findings to a FC.

Fusion Centre fuses the reports provided by the rest of nodes, uses several fusion rules to evaluate the reports.

Furthermore, reports are provided by SUs can be of two types :

• Binary.• Continuous.

Detection of SSDF Attack.

Binary type of reporting:o In the proposed detection algorithm the Trust Values

of SUs and Consistency Values of each user is computed.• If both the values falls below a threshold the SU is

characterized as an outlier.• A drawback of this work is that only one adversary has been

considered.

o In an another model proposed a Reputation Metric is used to detect and isolate attackers from legitimate SUs.• For the computation of this metric the output of each SU is

compared to the decision made by the FC.o E. Noon and H. Li study a specific case of an

attacker, the “hit-and-run” attacker.• Deviates between an honest mode and a lying mode.• The detection scheme combines a point system approach.

Continuous type of reporting:o A detection method using statistics is described.• Here a grid of sensors, divided into clusters, send

information about their RSS, along with their location to the FC.• This approach has two phases.• This approach does not restore the reputation of SU that

temporarily misbehaves as it increases a blacklist counter each time if the filter’s output does not lie between the defined thresholds.

o F. Yu, M. Huang, Z. Li and P. Mason propose a scheme to defend against SSDF attack in a distributed fashion for Cognitive ad-hoc radio networks.• A key difference of this work is that no FC is used.• SUs exchange information and decide independently upon

the presence of the primary transmissions.• Each SU computes the max deviation of received from the

mean value.• The simulation results show that distributed consensus

approach gives the best results.

Unused spectrum are determined and made use of them automatically.

Improves the spectrum utilization by neglecting the over occupied spectrum channels and filling the unused spectrum channels

Automatically improves and accomplishes its progress and minimize interference.

Advantages of CRN

Applications of CRN

Spectrum Awareness concept. Spectrum sharing techniques can help us fill the

regulatory “gaps” in a particular interference environment.

A great deal of research still needs to be done on simulating and explore these intelligent network ideas.

Cognitive radio technology can solve the problem of spectrum underutilization.

Conclusions

Simulation framework for security threats in cognitive radio networks

-E. Romero A. Mouradian J. Blesa J.M. Moya A. Araujo

ETSI Telecomunicación´ n, Universidad Polite´ cnica de Madrid, 28040 Madrid, Spain.

Security Aspects in Software Defined Radio and Cognitive Radio Networks: A Survey and A Way Ahead

-Gianmarco Baldini, Member, IEEE, Taj Sturman, Member, IEEE, Abdur Rahim Biswas, Member, IEEE, Ruediger

Leschhorn, Member, IEEE, Gy¨oz¨o G´odor, Member, IEEE, and Michael Street.

A Survey on Security Threats and Detection Techniques in Cognitive Radio Networks

-Alexandors G. Fragkiadakis,Elias Z.Tragos, Ioannis G. Askoxylakis.

Reference

Contd…

International Journal of Computer Applications (0975 – 8887) Volume 30– No.1, September 2011 31 : Cognitive Radios: Need, Capabilities, Standards, Applications and Research Challenges

-Prabhjot Kaur Department of Electronics and Communications ITM University Gurgaon, India.Moin Uddin Delhi Technological University Delhi, India.Arun Khosla , Department of Electronics and Communications National Institute of Technology, Jalandhar, India.

Attack prevention for collaborative spectrum sensing in cognitive radio networks.

-Lingjie Duan, Alexander W. Min†, Jianwei Huang, Kang G. Shin

† Network Communications and Economics Lab, Dept. of Information Engineering, The Chinese University of Hong Kong, Hong Kong

†Real-Time Computing Laboratory, Dept. of EECS, The University of Michigan, Ann Arbor, MI 48109-2121.

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