The main intention of developing cognitive radio technology is to solve the spectrum deficiency problem by allocating the spectrum dynamically to the unlicensed clients. An important aim of any wireless network is to secure communication. It is to help the unlicensed clients to utilize the maximum available licensed bandwidth, and the cognitive network is designed for opportunistic communication technology. Selfish attacks cause serious security problem because they significantly deteriorate the performance of a cognitive network. In this paper, the selfish attacks have been identified using cooperative neighboring cognitive radio ad hoc network (COOPON). A novel technique has been proposed as ICOOPON (improvised COOPON), which shows improved performance in selfish attack detection as compared to existing technique. A comparative study has been presented to find the efficiency of proposed technique. The parameters used are throughput, packet delivery ratio and end to end delay.
The significant growth in wireless networks leads the spectrum to an extreme usage. Cognitive radio is one of the intelligent wireless based communication technologies [
In Cognitive Radio (CR) most of the spectrum bands are allocated to the licensed users [
The main challenges faced by cognitive radio are:
1) Topology discovery: The use of non-uniform channels by different cognitive radio users makes the topology discovery difficult [
2) Optimization of cooperative sensing: By requesting the sensing information from several cognitive radio users, the user that initiates the cooperative sensing, improves the accuracy but also increases the network traffic. However, this also results in higher latency in collecting this information due to channel contention and packet re-trans- missions. These factors must be optimized for correct and efficient sensing in the case of cognitive ad hoc network [
Minho J. et al. [
Major work has been done for primary user identification and resource allocation. The security aspect of channel allocation is not addressed thoroughly in the literature. It is also essential to identify the attacker because of which the legitimate user lacks the opportunity of using the channel. In the above papers this issue has not been addressed except [
To detect selfish attacks in cognitive network, an efficient attack detection technique is proposed, namely Improvised COOPON (ICOOPON) in this paper. The COOPON technique is not efficient to find out selfish nodes that are present far from the network, whereas, the ICOOPON technique can detect such nodes. In the proposed technique Hidden Markov Model (HMM) has been used to improve the efficiency of the attack detection mechanism. The efficiency has been computed using parameters such as Throughput, Packet delivery ratio and End to end delay.
Selfish attacks are classified as Type 1, Type 2 and Type 3 described as below:
Selfish attack depends on how it will try to pre-occupy the spectrum resources. The type 1 attack is known as signal fake selfish attack. It prohibits a legitimate secondary user (LSU) from sensing available spectrum bands by sending faked PU signals. The selfish secondary user emulates the characteristics of primary user and this will be overheard by the legitimate secondary user and thinks that the primary user is active. This attack is usually built when there is a transmission between two selfish secondary users, regardless of the number of channels.
Here, the secondary users will be emulating the characteristics of primary users but it is generally carried out in dynamic multiple channel access. In the dynamic signal access process, the secondary users will periodically sense the current operating channel condition in order to know if the primary user is active or not, and if it is so then the secondary user will immediately switch to use other available channels. An attacker can effectively limit the legitimate secondary user performance, by sending fake signal information in a round -robin type [
Type 3 attack is also known as channel pre occupation attack. Such attack occurs in the communication environment which is used to broadcast the current available channel information to all other neighboring nodes for transmission. The selfish SU will broadcast fake free channel lists to its neighboring SUs. Thus the usage of available channels is banned to the legitimate secondary user [
CR network is having a dynamic characteristic due to which it is difficult to detect the selfish attack in the wireless network. The COOPON technique is used for detection of selfish attack. Here it concentrates on narrow minded assaults of SUs towards different direct access in subjective radio specially appointed systems. It accepts that a single secondary user obliges different channels. Each secondary user will frequently telecast the current station distribution data to the majority of its neighboring secondary users. It includes the quantity of stations in use and the quantity of accessible stations [
Among auxiliary ad hoc system CCC (common control channel) has been utilized to show and watch data. Type 3 selfish attack can be found out using the COOPON technique. The secondary users will use the information distributed through CCC to access channel for transmission. In this technique, it considers one node as target node and the other as neighboring node, and it will check the selfish attack of the target node.
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broadcasting on the dedicated channel. When the Target-Node reports that there are two channels currently in use, while Neighboring-Node 3 reports that only one channel is currently in use, creates a mismatch between the target and the neighboring node. N-node 4 also receives the fake channel information. On the other hand T-node and N-node 1, T-node and N-node 4 are correct. Thus all the 1-hop neighboring nodes can easily decide that the target node is the selfish attacker [
The detection algorithm for ICOOPON is as shown in the following section:
・ Among the secondary nodes consider one as target node.
・ Discover all the neighboring nodes for the current target node.
・ Generate the initial probability of all the nodes in the present scenario.
・ Store the Probability sequence.
・ Find the nodes with states 0 and 1.
・ Store Transitions in an array.
・ The process repeats for all the nodes present in the network.
・ Node/s with high occurrence and probability, identify the selfish attacker.
In this case, all the nodes initially follow the discovery mode, in which all its neighboring nodes are found. One or more selfish attackers are present in the network. Let one of the nodes is target node and all one hop neighboring nodes are found. Then initial probability of channel occupancy will be generated. Initial probability will be generated for the nodes present in the network. A uniform number generator is used for this process. The nodes with 0 probability will be neglected because it is not using any channel. Then the probability will be set as 0 if the probability is less than 0.6, and if it is greater than 0.6 then it will be set as 1. The node having more probability in number of occurrence will be detected as selfish node or attacker. The base station monitors this
channel allocation process. When base station detects, that one or more number of nodes are having more occurrences of high probability of channel occupancy, then those nodes will be declared as selfish nodes.
In HMM, until the end state is reached the sequence of state is generated by moving from state to state according to the state transition probabilities. In the HMM, the state themselves are not directly observed instead we assume that there are some sequence of state in Markov chain that cannot be observed directly, and these states generate observations. Each state may have different distribution. The HMM model falls in a subclass of Bayesian network.
The selfish attacks have been simulated in NS-2. The wireless scenario of 1000 × 1000 square meter area has been considered. Number of nodes has been varied from 10 to 100 at the interval of 20. The simulation results in the presence of selfish nodes and in the absence of selfish nodes has been presented. The parameters that have been analyzed are packet delivery ratio, throughput and end to end delay. In simulation scenario maximum 20 nodes are considered as selfish nodes.
Number of selfish nodes is varied from 2 to 20 and performance of COOPON and ICOOPON is observed. For communication all the nodes use Omni-directional antenna (
The parameters such as Throughput and Packet delivery ratio has been considered for evaluating the performance of the algorithm.
1) Throughput: It is measured in data packets per second. It shows the rate of information or message delivered over a communication channel.
2) Packet Delivery Ratio: It shows the ratio of number of packet transmitted to number of packet that has been received.
3) End to end delay: I t is the total delay that a packet experience as it is travelling through a network.
The following
Antenna | Omni directional antenna |
---|---|
Routing protocol | AODV (ad hoc on demand vector protocol) |
Network size | 1000 m × 1000 m |
No. of secondary users | 10, 20, 30, 40, 50, 60 up to 100 |
No. of selfish secondary users | 2, 4, 6, 8, 10, 12, 20 |
nodes that are marked in blue. The nodes are continuously move and packet transmission takes place among the nodes.
From the graph the performance of packet delivery ratio is more for ICOOPON as compared to COOPON and AODV protocol technique. As, ICOOPON technique uses two algorithms, it checks with the probability condition with all the nodes present over there. The main drawback of COOPON technique is that it does not detect selfish attack to search beyond one hop network because of this reason the efficiency is less compared with ICOOPON technique.
The following table summarizes the performance study of AODV, COOPON, and ICOOPON.
S. No | No. of nodes | Packet delivery ratio (kbps) (5 selfish nodes) |
---|---|---|
AODV [ | 50 | 0.69 |
100 | 0.59 | |
COOPON [ | 50 | 0.88 |
100 | 0.77 | |
ICOOPON (proposed technique) | 50 | 0.92 |
100 | 0.82 |
Technique | No. of nodes | Throughput (5 selfish nodes) |
---|---|---|
AODV [ | 50 | 11.37 |
100 | 9.58 | |
COOPON [ | 50 | 12.79 |
100 | 10.18 | |
ICOOPON (proposed technique) | 50 | 13.5 |
100 | 11.38 |
Technique | No. of nodes | End to end delay |
---|---|---|
AODV [ | 50 | 0.263 |
100 | 0.27 | |
COOPON [ | 50 | 0.208 |
100 | 0.22 | |
ICOOPON (proposed technique) | 50 | 0.1884 |
100 | 0.2 |
From the above tables it can be summarized that when number of nodes increases the efficiency decreases for both the case of packet delivery Ratio, Throughput and end to end delay. However, the ICOOPON is least affected with increase in selfish node attacks as compared to COOPON and ICOOPON.
The cognitive radio is currently attracting numerous research efforts, where the major problem is of security. The novel technique, namely ICOOPON is proposed in this paper which has improved performance as compared to COOPON and AODV. For simulation 50 and 100 numbers of nodes have been used with four and five numbers of selfish nodes. The ICOOPON technique provides the improved performance due to HMM. The proposed technique is proved to be more efficient by 32% than the COOPON technique in various parameter performances such as packet delivery ratio, end to end delay and throughput. For future work cryptographic model and game theory can be applied to check the selfish attack detection analysis. The existing parameters can be taken into account for performance analysis and can be analyzed using jitter.
Patil, S.C. and Janardhanan, A. (2016) Efficient Selfish At- tack Detection in Cognitive Radio Network. Wireless Engineering and Technology, 7, 105-117. http://dx.doi.org/10.4236/wet.2016.74011