DETECTION AND ISOLATION OF PRIMARY USER EMULATOR IN COGNITIVE RADIO NETWORK USING HYBRID OF ANGLE OF ARRIVAL AND RECEIVED SIGNAL STRENGTH

ABSTRACT

Cognitive Radio (CR) technology is the candidate panacea to the problem of spectrum scarcity in the wireless world. However, this emerging technology is faced with security challenges. The most severe among these security challenges is Primary User Emulation Attack (PUEA).   One of the methods to detect Primary User Emulator (PUE) is via localisation,  of  which  there  are  two  major  categories:  range-free  and  range-based. Range-free localisation  is cost effective, less  computationally complex  and easy to deploy. However, it is less accurate when compared with range-based category. Since accuracy is fundamental in localisation, range-based localisation scheme was adopted in this work. The range-based category is reported to be more accurate although with higher complexity. Among this category are Angle of Arrival (AOA), which utilises angular measurements to localise the PUE, and the Received Signal Strength (RSS), which employs only distance to localise the PUE. To improve performance of range- based methods, this research hybridised AOA and RSS techniques to localise PUEs in television (TV) white spaces. This scheme determines the angle at which the Primary User’s (PU’s) signal arrives at the Secondary Users (SUs) and the distance between the PU and SUs in the Cognitive Radio Network (CRN). Because in a TV white space, the location of PU is known, the computed AOA and the distance obtained from the RSS are therefore used to determine the position of a PU’s signal transmitter. This position is compared with the location of the PU to ascertain the true source of the signal, thus detecting  the  PUE.  Computer  simulations  demonstrated  that  the  hybrid  scheme estimated the position of the PUE much faster and with a much lower Root Mean Square Error (RMSE) of 5.00×10-3  after 20 iterations. This greatly outperformed RSS and AOA methods that estimated the position of PUE after 50 iterations with RMSE of 2.00×10-1  and 1.00×10-2  respectively when considered individually. Furthermore, investigation was made on the selection of the best pair of SUs to be used in the detection processes. It was discovered that a pair of SUs from the same communication environment whose RSS values are very close, detected PUE better (with RMSE of 4.7×10-3  after 20 iterations) than a pair of SUs whose RSS values are higher but in different communication environments as they localised PUE with RMSE of  6.0×10-3 after  70  iterations.  The  significance  of  this  result  is  appreciated  especially  when attention is given to the fact that speed, accuracy and energy efficiency are essential in the efficient operation of cognitive radios. Energy-efficient operations are essential in the current global energy crises that wireless systems face. Moreover, by isolating the detected PUE from Cognitive Radio Network (CRN), there is availability of more spectrum holes that will accommodate newer wireless technologies for effective communication. Furthermore, Secondary Users (SUs) have more transmission time, improved quality of service (QoS), connection reliability, higher throughput and improvement in the overall general performance of the entire cognitive radio network.

CHAPTER ONE

1.0   INTRODUCTION

1.1   Background to the Study

There is an increase in the deployment of newer wireless technologies leading to more demand for radio spectrum (Anandakumara & Umamaheswarib, 2017; Gupta & Onumanyi, 2019). The radio spectrum is referred to as the portion or band of the electromagnetic  continuum  that  conveys  radio  waves.    The  most  desired  band  for various wireless communications is 30MHz-3GHz (Lin  et al.,  2018;  Khaliq et al., 2018). Initially, radio spectrum was assigned in the order of request. But with increased deployment rate of newer wireless technologies, there is an increased pressure on the available radio spectrum. This has indicated possibility of spectrum  scarcity (Zeng et al., 2008; Zina & Noureddine, 2015; Vasanthareddy & Sanjeev, 2021).

The causes of spectrum  scarcity are several as  postulated in literature.  One of the reasons for spectrum scarcity is emergence of newer wireless technologies (Balieiro et al., 2014; Anandakumara & Umamaheswarib, 2017). Another cause of spectrum scarcity is the inept permanent frequency allocation policy (Subhedar & Birajdar, 2011; Maninder et al., 2016). Similarly, Jin et al. (2015) identified inefficient spectrum usage as potential basis for spectrum scarcity. These reasons indicate that spectrum is not scarce, but inadequately utilized as there are still licensed bands which are not fully utilized (Malik  et al., 2010; Subhedar & Birajdar, 2011; Yuan et al., 2012).

Akyildiz et al. (2006) and Rehman, (2019) further corroborated this assertion of  actual spectrum utilization as Figure 1.1shows that some portions of the licensed frequency band in the electromagnetic radio spectrum are heavily used while others either experience sparse use or medium use with less than 6% occupancy.

Because  of  underutilization  of  the  allocated  spectrum  bands  and  the  attendant difficulties in retrieving them from those allocated to, it becomes needful to develop new and dynamic methods for spectrum management and efficient utilization using cognitive radio (CR) technology (Goyal et al., 2016; Rharras et al., 2020).

CR technology is identified in literature as candidate panacea to spectrum underutilization and scarcity occasioned by static spectrum allocation       (Amer et al., 2016; Verma et al., 2018; Ali et al., 2019). CR refers to Secondary User (SU) which is able to identify its communication environment by fine-tuning its radio parameters and opportunistically uses spectrum licensed to Primary Users (PUs) when the band is inactive without causing interference to the PU. This inactive spectrum through which CR transmits is called spectrum hole or white space (Nilesh & Patil, 2014; Sultana & Hussain, 2018). According to Arthy and Periyasamy (2015), a given radio spectrum with spectrum holes can be envisioned as depicted in Figure 1.2 where spectrum in use and spectrum holes are clearly indicated and CR dynamically accesses the spectrum holes.

(Arthy & Periyasamy, 2015)

From  the  foregoing,  it  is  obvious  that  CR  could  actually enhance  spectrum  usage efficiency and alleviate the challenge of spectrum scarcity (Gupta et al., 2016; Anandakumara  & Umamaheswarib, 2017; Akbari & Jamshid, 2018). Several processes are  involved  in  the  development  of  a  cognitive  radio  (CR).  These  processes  are described in the Figure 1.3

1.1.1   Spectrum sensing

Spectrum Sensing which is the most significant component of CR operations is the process by which a CR detects the incumbent signals (Deng et al., 2012; Maninder et al., 2016). Since CR can only utilize idle portion of the spectrum, it must observe the spectrum bands to detect unused spectrum. To ensure a trustworthy spectrum sensing process, the problem of attacks on the CRNs (which is the focus of this research) needs to  be  addressed  by  distinguishing  PU  signals  from  SU  signals  because  uncertain, falsified or corruptly sensed data can alter the entire sensing result. Hence, the spectrum decision becomes inaccurate. This leads to false alarm and interference to PU signals (Kanti et al., 2015; Alhumud et al., 2019).

1.1.2   Spectrum decision

As soon as spectrum holes are detected, it is necessary that the CR selects the best band based on their Quality of Service (QoS) requirements. Prior information about activity of the PU is required in order to devise a spectrum decision algorithm that incorporates dynamic spectrum characteristics. As a requirement, the spectrum bands should be characterized in radio and statistical behaviours (Kanti et al., 2015; Giral & Hern, 2020).

1.1.3   Spectrum sharing

Since multiple CRs in the CRN compete for available spectrum holes, there will be collision in overlapping portions of the spectrum holes. To prevent this, their transmission should be coordinated. Through spectrum sharing, spectrum resources can be opportunistically allocated to multiple CRs (Gelabert et al., 2010). It also involves prevention of interference with the primary network through resource allocation. Moreover, this function enables a CR Medium Access Control (MAC) that enables the sensing control to allot sensing task among the cooperating nodes as well as spectrum access to determine transmission time (Alhumud et al., 2019).

1.1.4   Spectrum mobility

CR should vacate the particular portion of spectrum in use as soon as PU is detected and continues its transmission in any vacant portion of the spectrum. Therefore, spectrum mobility enables a spectrum hand off scheme to identify the link failure and shift to a new route from the current transmission or switch to a fresh spectrum band with less quality degradation. This involves collaboration of spectrum sensing, neighbour discovery in the link layer and routing protocols. Moreover, this functionality requires connection management scheme to sustain the performance of the upper layer protocols by alleviating effects of spectrum switching (Kanti et al., 2015; Alhumud et al., 2019).

Although the various operations of CR cycle in cognitive radio system are distinct, they depend on each other for the successful operations of CRN. Therefore, failure on any of the operation will lead to failure of CR operation. For example, the dynamic spectrum access (DSA) of CR will not be achieved if spectrum sensing operation fails.

CR operation is possible due to its reconfigurability and cognitive capability (Kumar & Singh, 2016; Sultana & Hussain, 2018).  Reconfigurability allows a CR to adjust to its environment by regulating certain parameters like carrier frequency, bandwidth and transmission power (Liang et al., 2008). This becomes important because CRs must utilize the fallow bands opportunistically and vacates the bands whenever PU signal is detected.  Cognitive capability of CRs (also referred to as SUs) prepares them to sense their radio environment and choose the most suitable transmission mode available in the fallow bands. This is achievable by the spectrum management process where different parameters like power, modulation type and frequency are estimated (Kaur et al., 2010).

1.1.5   Spectrum security

Traditional wireless communication technology faces several security threats among which are forgery, masquerading attacks and eavesdropping. These threats can easily interrupt communication during transmission (Alhakami et al., 2014). Although Cognitive Radio (CR) system is threatened by various security issues that traditional wireless communication system faces, it faces several other security threats among which  Primary  User  Emulation  Attack  (PUEA)  is  the  most  challenging.  When  an impish secondary user masquerades itself as the primary user for unscrupulous reasons, PUEA is said to have occurred. Except it is addressed, PUEA can cripple the whole cognitive radio network (CRN). In order to avoid this, Primary User Emulator (PUE) should be segregated from the Cognitive Radio Network (CRN) upon detection. This will not only ensure availability of spectrum for newer wireless technologies, it will also guarantee cheaper and secured communication (Alahmadi et al., 2014).

1.2   Statement of the Research Problem

The task of detecting the PUEs remains a difficult process because of the lack of features to distinguish between PUEs and actual PUs (Chen et al., 2008a; Saeed et al., 2019). Consequently, inability to localise PUEs often leads to denial of service (DoS) and inefficient use of spectrum for CR purpose. Moreover, PUEA is continuous as long as it is in CRN. If detected but left in CRN, PUE will continue to launch Primary User Emulation Attack (PUEA). Existing techniques for detecting PUEs such as Angle of Arrival, Received Signal Strength, Time of Arrival and Time Difference of Arrival, have their short comings when applied individually (Bouabdellah et al., 2019). Thus, there is need to investigate the effects of a hybridised scheme for detecting PUEs in CRNs and isolate the detected PUEs from CRN. The outcome of this research work will be used for the development of economically viable and efficient method of detecting PUEs in CRN. This will lead to availability of more spectrum holes that will accommodate newer wireless technologies for effective communication.

1.3   Aim and Objectives of the Study

This research work aimed at developing an improved localisation scheme for detecting Primary User Emulator (PUE) in CRNs and isolating the detected PUE from the cognitive radio network (CRN). To achieve this aim, the objectives are to:

i.      develop a hybrid of Angle of Arrival (AOA) and Received Signal Strength (RSS) schemes for localizing Primary User Emulator (PUE) in Cognitive Radio Networks (CRNs).

ii.      evaluate  the  effects  of  cooperative  sensing  on  the  detection  of  Primary  User Emulator (PUE) using the developed hybrid scheme in i.

iii.      develop a technique for isolating detected Primary User Emulators (PUEs).

iv.       evaluate the performance of the overall developed scheme.

1.4   Significance of the Study

Cognitive Radio Technology (CRT) is the panacea to the current spectrum scarcity posed by spectrum underutilization.  However, there are many challenges to realizing its concept in practice. The most critical among the many challenges is the one posed by PUE.  PUE  mimics  the  spectral  characteristics  of  the  PU  for  selfish  or  malicious purpose. If PUEA is not dealt with, realizing CR concept remains a mirage. Hence, this study developed a technique to detecting PUE as well as eliminating it from the network for availability of more radio spectrum and efficient operation of CR devices. The outcome of this research work will benefit researchers as it will be used for the development of economically viable and efficient method of detecting PUEs in CRN. Availability of more spectrum holes will accommodate newer wireless technologies for effective  communication.  This  without  doubt  leads  to  cheaper  call  and  data  rates, reliable connection, improved quality of service and elimination of denial of service which are beneficial to individuals and the society.

1.5   Scope of the Study

The focus of this study is on the development of a hybrid localisation method using AOA and RSS to detect the PUE and isolate it from CRN. It also investigates the effect of cooperative sensing in the localisation of PUE.

1.6   Thesis outline

The remaining chapters of this thesis are structured in this order: The review of related literatures in the domain of Cognitive Radio (CR), Primary User Emulation Attacks (PUEAs) and classifications of primary user emulators are given in chapter two. It further reviews different methods for detecting Primary User Emulator (PUE) and various spectrum sensing techniques. Chapter three presents the research methodology. It describes detection of PUE with the aid of Hybrid Localisation Method (HLM) and the effects Cooperative Spectrum Sensing (CSS) has on HLM. It further presents a method for isolating the detected PUE from the CRN. The results obtained from the developed techniques in chapter three as well as comparative analysis of the results with existing techniques are presented in chapter four while the conclusion based on the achievements of this study and the challenges encountered as well as recommendation for further work are presented in the fifth chapter.

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