Enhanced Pue Detection and Selfish Secondary User Detection Method in Cognitive Radio Networks

: In cognitive radio networks (CRNs), cognitive radio (CR) nodes adaptively access the spectrum aiming to maximize the utilization of the scarce resource. A new security threat known as the Primary User Emulation attack raises a great challenge to CRNs. In the proposed method an innovative technique is introduced which is called as Enhanced PUE detection method and selfish secondary user detection method. In the Enhanced PUE detection, PUE detection approach can be extended to address the scenario with multiple classes of PUs that have different SAP features. The Selfish Secondary User detection method, attacks are detected by the cooperation of other legitimate neighboring Secondary Users.


INTRODUCTION
Cognitive Radio Network (CRN) is an innovative approach to wireless engineering in which radios are designed with an unparalleled level of intelligence and alertness.This advanced technology enables radio devices to use spectrum (i.e., radio frequencies) in entirely different and stylish ways.Cognitive radios have the ability to monitor, analyze, and detect the conditions of their operating atmosphere, and dynamically alter their own characteristics to best match those conditions.In a cognitive radio network (CRN), secondary users (SUs), i.e., unlicensed users, are envisioned to be able to sense and analyze their environment, learn from the environment variations, and access the licensed bands to achieve highly reliable communications without interference.Specifically, the main functions of CR technology in CRNs include: (1) spectrum sensing, i.e., to determine the available spectrum and detect the presence of PUs; (2) spectrum management, i.e., to select the best available channel spectrum sensing to meet users' communication requirements; (3) spectrum sharing, i.e., to coordinate access to this channel with other users; and (4) spectrum mobility, i.e., to vacate the channel when a PU is detected.• Email: editor@ijfmr.com

RELATED WORK
The existing system discussed in [3] proposed a novel PUE Detection system termed Signal Activity Pattern Acquisition and Reconstruction System (SPARS), which collects the signal activity pattern (SAP) of a transmitter as a series of ON and /or OFF period of a transmitter along the time.Existing method solves the primary user emulation attack but the problem is this method only consider one class of PUs that have similar SAP features, i.e., with similar distributions for the ON or OFF periods.The multiple classes of primary users are not considered.
Furthermore, the selfish users act as secondary users and occupy the more number of channels.The defense against the Primary User Emulation Attack is studied in [6] using the scenario of unknown channel statistics.The technique adopted in [6] is a passive defense policy and modeled the dogfight in spectrum as a zero -sum game.Author designed a good defending strategy for the honest Secondary Users using the theories of game and learning.A DECLOAK, is presented to identify the PUE attacks in [9] which utilizes a nonparametric Bayesian approach for detecting PUE attacks.The authors in [10][11][12] proposed different methods to detect the Primary User Emulation attacks using different techniques.

EXISTING METHODOLOGY
In the existing system, in order to detect primary user emulation attacks, a novel PUE detection system, termed Signal activity Pattern Acquisition and Reconstruction System (SPARS) is presented.In the ensuing discussion, if not otherwise noted, an attacker refers to a PUE attacker, a signal refers to a PU signal, and a transmitter refers to a PU signal transmitter, which may be a PU or an attacker.A Signal Activity Pattern of a transmitter is defined as a series of ON and or OFF periods of the transmitter along the time.An ON period refers to the duration of a busy period of PUs.It acquires the SAP of a transmitter through spectrum sensing, and compares it with SAPs of PUs through a SAP reconstruction model.If the observed SAP is not 'like' the SAPs of PUs, which is measured by the reconstruction error, then the transmitter is an attacker.The major drawback of the existing system is that which does not consider multiple classes of PUs, and also selfish secondary users attack detection.It concentrates on only a single class of PUs that have similar SAP features, i.e., with similar distributions for the ON/OFF periods.

PROPOSED METHODOLOGY
In the proposed system, an innovative technique is introduced which is called Enhanced PUE detection method and selfish secondary user detection method.In the Enhanced PUE detection, PUE detection approach can be extended to address the scenario with multiple classes of PUs that have different SAP features.Also, the secondary users also sometimes misbehave in the network.So, selfish secondary user detection method is also considered.
The contributions are: For detecting multiple classes of primary user attackers, the SPARS is extended for the PUE detection in which different classes of PUs have different signal activity patterns.Specifically, SPARS is extended to classify an observed SAP to see if it belongs to a certain class of PUs.If yes, then this SAP is from a PU.Otherwise, it is from an attacker.To achieve this objective we have to examine the structure of the weights in the reconstruction of a SAP, in addition to the reconstruction error.
For the selfish secondary user detection, this method will detect the attacks of selfish SUs by the cooperation of other legitimate neighboring SUs.All neighboring SUs exchange the channel allocation information both received from and sent to the target SU, which will be investigated by all of its neighboring SUs.The selfish attacks of SUs are focused toward multiple channel access in cognitive radio ad-hoc networks.Assume that an individual SU accommodates multiple channels.Each SU will regularly broadcast the current multiple channel allocation information to all of its neighboring SUs, including the number of channels in current use and the number of available channels, respectively.The selfish SU will broadcast fake information on available channels in order to preoccupy them.The selfish SU will send a larger number of channels in current use than real in order to reserve available channels for later use.

B. Selfish Secondary User Detection Method
In a cognitive radio network, the common control channel (CCC) is used to broadcast and exchange managing information and parameters to manage the CR network among secondary ad-hoc users.The CCC is a channel dedicated only to exchanging managing information and parameters.A list of current channel allocation information is broadcast to all neighboring SUs.In reality, a list is broadcast once, and it contains the channel allocation information on all of the neighboring nodes.The SU will use the list information distributed through CCC to access channels for transmission.A selfish secondary node will use CCC for selfish attacks by sending fake current channel allocation information to its neighboring SUs.In addition, simultaneously all of the neighboring nodes sum the numbers of currently used cannels sent by the target node, T Node.Individual neighboring nodes will compare the summed numbers sent by all neighboring nodes to the summed numbers sent by the target node to check if the target SU is a selfish attacker.Thus, all neighboring nodes will know if the target SU is a selfish attacker or not.This detection mechanism is carried out through the cooperative behavior of neighboring nodes.Once a neighboring SU is chosen as a target node and the detection action for it is completed, another neighboring SU will be selected as a target node for the next detection action.

Distributed Reaction Mechanism:
In this mechanism, consider with N nodes where all nodes are genuine, i.e. they correctly follow BEB.A lower bound on the channel access probability of a node is derived (and thus its throughput).Every time a node chooses a back off value uniformly at random from [0….CW-1], it could choose CW-1 with probability An adaptive and distributed reaction algorithm is designed for the genuine nodes to react against mildly selfish misbehaviors.Each genuine node measures its throughput degradation with respect to its saturation throughput share T 0 given.The reaction aggressiveness is made proportional to the level of suspected selfishness, and in most cases, the reaction is not as strong so as to lower the overall network throughput tremendously.Let us consider the saturation throughput scenario with N nodes.Using Bianchi's analysis let the individual fair throughput of each node under saturation conditions equal T 0 .Let us consider that one of the nodes is misbehaving.This would lower the throughput observed by the genuine nodes.Clearly, T 0 o <T 0 .

RESULTS AND DISCUSSION
In this section the performance of the existing and the proposed system is compared.In the existing system, Signal activity Pattern Acquisition and Reconstruction System (SPARS) is used in the existing system.In the proposed system, Selfish Attacks Detection (SAD) method is used to identify the selfish secondary users and enhanced SPARS system is used to identify the multiple classes of Pus attack in the cognitive radio network.The performance is evaluated in terms of false alarm probability, true positive rate and miss-detection probability.

Description of Output Parameter False alarm Probability
False alarm probability is defined as the probability of detecting the selfish nodes falsely.

True positive rate
It is the proportion of positive cases that were correctly identified.

Miss detection probability
Misdetection probability is defined as the probability of not detecting misbehaviors.

Graph Comparisons False alarm probability Fig 3: False alarm probability
In the X-axis the batch index is taken.In the Y-axis false alarm probability is taken.In the existing system, Signal activity Pattern Acquisition and Reconstruction System (SPARS) is used in the existing system.In the proposed system, Selfish Attacks Detection (SAD) method is used to identify the selfish secondary users in the cognitive radio network.When Compared to the existing system, there is less false alarm probability in the proposed system.

True Positive rate Fig 4: True Positive rate
In the X-axis the batch index is taken.In the Y-axis true positive rate is taken.In the existing system, Signal activity Pattern Acquisition and Reconstruction System (SPARS) is used but in the proposed system, Selfish Attacks Detection (SAD) method is used to identify the selfish secondary users in the cognitive radio network.When Compared to the existing system, there is high true positive rate in the proposed system.

Miss detection Probability Fig 5: Miss detection Probability
In the X-axis the batch index is taken.In the Y-axis true misdetection probability is taken.In the existing system, Signal activity Pattern Acquisition and Reconstruction System (SPARS) is used in the existing system.In the proposed system, Selfish Attacks Detection (SAD) method is used to identify the selfish secondary users in the cognitive radio network.When Compared to the existing system, there is less misdetection probability in the proposed system.

CONCLUSION
The drawback of the existing system is that it does not detect the selfish secondary user attack and multiple classes of primary user attack.In the proposed system, Selfish Attacks Detection (SAD) is used to identify the selfish secondary users in the cognitive radio network.This method will detect the attacks of selfish SUs by the cooperation of other legitimate neighboring SUs.Furthermore, the distributed reaction mechanism is used to detect more than one selfish node.In this method the two reaction mechanisms are proposed based entirely upon local information, to prevent selfish misbehaviors in cognitive radio adhoc networks.Additionally, the multiple classes of primary users are detected in which the weight is allocated for each application.Based on the weights, the multiple classes of primary users are detected.The numerical results show that the proposed method achieves better performance than the existing system.

Fig 1 :
Fig 1: Architecture diagram of Cognitive Radio Network

Architecture View of the proposed system A. Enhanced PUE detection Method
(Modified SPARS)In the Enhanced PUE detection method, the multiple classes of primary users are detected.The existing SPARS method is extended to detect the multiple and different classes of the primary users.Particularly, SPARS is extended to classify an observed SAP to see if it belongs to a certain class of PUs.If yes, then this SAP is from a PU.Otherwise, it is from an attacker.To achieve this objective, we have to examine the structure of the weights in the reconstruction of a SAP, in addition to the reconstruction error.