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Spoofing Attacks Detection and Localizing Multiple Adversaries in Wireless Networks

Pallavi D.Sontakke 1, Prof.Dr.C.A.Dhote 2
  1. PG Student, Dept. of I.T, Prof Ram Meghe Institute of Technology & Research Badnera, Amravati, India
  2. Professor, Dept. of I.T., Prof Ram Meghe Institute of Technology & Research Badnera, Amravati, India
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Wireless networks provide various advantages in real world. This can help businesses to increase their productivity, lower cost and effectiveness, increase scalability and improve relationship with business partners and attract customers. Communication in wireless network is critical and challenging issue. Wireless spoofing attacks are occurs easily and reduce the networks performance. Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. The flexibility and openness of wireless networks enables an adversary to masquerade as other devices easily. The traditional approach to detect spoofing attacks is to apply cryptographic authentication. Here using spatial information, a physical property of each node, so hard to falsify and not depend on cryptographic security, on the beginning for (1) detecting spoofing attacks; (2) determining the number of attackers when multiple node pretend as a same node identity, and (3) localizing multiple adversaries. Here using the correlation between a signal's spatial direction and the average received signal gain of received signal strength (RSS) inherited from wireless nodes to detect the spoofing attacks. Then the problem of determining the number of attackers as multiclass detection problem is formulated. Cluster-based mechanisms are developed to determine the number of attackers. When the training data is available, Support Vector Machines (SVM) method is used to further improve the accuracy of determining the number of attackers. The approach can both detects the presence of attacks as well as determine the number of adversaries, spoofing the same node identity, also that can localize any number of attackers and eliminate them.


Spoofing Attack, Attack Detection, Localization


A wireless network is any type of computer network that uses wireless data connections for connecting network nodes. Wireless network provide an inexpensive and easy way to share a single Internet connection among several computers.
The bases of wireless systems are radio waves, an implementation that takes place at the physical level of network structure. Wireless network are easy to add station as there are no cable required. There is less need for technical support signal can be sent through door and wall so station is mobile. Wireless networks are internet backbone for providing services to both mobile and stationary user.
Spoofing in IT world refers tricking or deceiving computer users. When any person or program masquerades as anotherby falsifying data, gaining the advantage, in network security is called spoofing. Types of spoofing attacks includes IP spoofing, E-Mail spoofing, Web Spoofing.
Adversaries are a malicious entity whose aim is to prevent user from achieving their goal. Due to the openness of the wireless transmission medium, adversaries are able to monitor any transmission. Due to the openness of wireless and sensor networks, they are especially vulnerable to spoofing attacks where an attacker forges its identity to masquerade as another device, or even creates multiple illegitimate identities.
Among various types of attacks, identity-based spoofing attacks are especially easy to launch and can cause significant damage to network performance.
During passive monitoring it is easy to get MAC address for the attackers and modify its MAC address by using ifconfig command to masquerade as another device. The traditional approach to detect spoofing attacks is to apply cryptographic authentication. Cryptographic authentication of devices introduces key management overheads that may not be practical for several commodity wireless networks.
Wireless spoofing attacks are easy to launch and can significantly impact the performance of networks. Therefore it is important to 1. Detect presence of the spoofing attacks. 2. Determine the number of attackers. 3. Localizing multiple adversaries .4. Eliminate them.
In practice, the channels between different antennas are often correlated and therefore the potential multi antenna gains may not always be obtainable. This is called spatial correlation as it can be interpreted as a correlation between a signal's spatial direction and the average received signal gain. To detect the attacks received signal strength (RSS) based special correlation which is physical property related to each wireless node will use. Here received signal strength (RSS) use to distinguish wireless devices for spoofing detection
The main contributions of our work are: 1) GADE: a generalized attack detection model (GADE) that can both detect spoofing attacks as well as determine the number of adversaries using cluster analysis methods grounded on RSS-based spatial correlations among normal devices and adversaries; and 2) IDOL: an integrated detection and localization system that can both detect attacks as well as find the positions of multiple adversaries even when the adversaries vary their transmission power levels.


P. Bahl et al.proposed the method RADAR( RAdio Detection And Ranging) for identifying the location of attacker in wireless sensor network[1]. Faria and Cheriton[2] proposed the use of matching rules of signal prints for spoofing detection.
The traditional approach to prevent spoofing attacks is the use cryptographic-based authentication. Mathias Bohge et al. proposed a framework, called TESLA certificate, for the scalability problems in hierarchical ad hoc sensor networks[3].Wu et al. (2005) presented a secure and efficient key management (SEKM) framework. In this the data communication is done between the client and server. They have introduced a secure and efficient key management (SEKM) framework [4]. A wool et al. presented Wired Equivalent Privacy (WEP), which provides key management with host revocation to existing IEEE 802.11 wireless LAN networks [5].
New approach is using physical property which is associated with wireless transmission. The MAC sequence number has also been used in [6] to perform spoofing detection. Liand Trappe [7] introduced a security layer that used forge resistant relationships based on the packet traffic, including MAC sequence number and traffic pattern, to detect spoofing attacks. V. Brik et al. (2008) invented the concept of PARADIS server which is able to find the frequency error, I/ Q offset, SYNC correlation, phase error and magnitude error. In this the concept of fingerprinting is evolved into the PARADIS server [10].RSS is the property closely correlated with location in physical space and is available in the exiting wireless network.
Y. Sheng et al. [11] describes that MAC spoofing attacks in 802.11 networks. They propose to use Gaussian Mixture Modeling (GMM) for RSS profiling, and show how to use it to detect spoofing attacks. The GMM is the mixture local statistics of a single AM, combining local results from AMs, and global multi-AM detection, respectively.
Sang et al. [12] proposed to use the node’s “spatial signature, “including Received Signal Strength Indicator (RSSI) and Link Quality Indicator (LQI) to authenticate messages in wireless networks. However, none of these approaches are capable of determining the number of attackers and they do not have the ability to localize the positions of the adversaries after attack detection.
Chen et al. [8] created a system that both detects spoofing attacks and localizes the attacker. Yang et al. [9] proposed to use the direction of arrival and received signal strength of the signals to localize adversary’s sensor nodes. Choosing a group of algorithms employing RSS to perform the task of localizing multiple attackers and evaluate their performance in terms of localization accuracy.
J.Yang et al., [13] proposed a technique DEtecting Mobile Spoofing aTtacks in wireless Environments (DEMOTE). They develop the DEMOTE system, which exploits Received Signal Strength (RSS) traces collected over time and achieves an optimal threshold to partition the RSS traces into classes for attack detection
In 2009 Gyathri Chandrasekaran et al. [14], proposed architecture to robustly detect identity spoofing attacks under varying operating conditions. In 2010, Jeong Heon Lee et al. [15] address issues associated with location spoofing attack detection by examining relative location error rather than its absolute value.
Liang Xiao et al.,[16] proposed a PHY-authentication protocol to detect spoofing attacks in wireless networks, exploiting the rapid-decorrelation property of radio channels with distance
F.A. Barbhuiya et al. [17] presented an IDS to detect ARP spoofing attacks using active state-transition framework called “active DES”.Ali Broumandan et al. [18] proposed a spoofing detection method based on a single moving antenna. Test measurements have been performed by combining authentic signals received from a rooftop antenna with spoofing signals radiated from an indoor directional antenna and received by a spatially translated single antenna
Jie Yang et al. [19] proposed to use received signal strength based spatial correlation, a physical property associated with each wireless device that is hard to falsify and not reliant on cryptography as the basis for detecting spoofing attacks in wireless networks.
Our work differs from the previous study in that we use the spatial information to assist in attack detection instead of relying on cryptographic-based approaches. Furthermore, our work is novel because none of the exiting work can determine the number of attackers when there are multiple adversaries masquerading as the same identity. Additionally, our approach can accurately localize multiple adversaries even when the attackers varying their transmission power levels to trick the system of their true locations.


Network contains in different clusters. Each cluster contains different nodes. Our aim is to detect the spoofing attack from particular node which belongs to any cluster. Energy optimizer is used to calculate the energy of particular node to detect the spoofing attack. Recently, new approaches utilizing physical properties associated with wireless transmission to combat attacks in wireless networks have been proposed. Based on the fact that wireless channel response decorrelates quite rapidly in space, a channel-based authentication scheme was proposed to discriminate between transmitters at different locations. When user sends the file to server then server will detect the attack & identify from which location it is by detection and localization of spoofing attack by Energy optimizer and the weight of the node
Since under a spoofing attack, the RSS readings from the victim node and the spoofing attackers are mixed together, this observation suggests that we may conduct cluster analysis on top of RSS-based spatial correlation to find out the distance in signal space and further detect the presence of spoofing attackers in physical space. The System Evolution is a new method to analyse cluster structures and estimate the number of clusters. The System Evolution method uses the twin-cluster model, which are the two closest clusters among K potential clusters of a data set. The twin-cluster model is used for energy calculation. The Partition Energy denotes the border distance between the twin clusters, whereas the Merging Energy is calculated as the average distance between elements in the border region of the twin clusters.

3.1 Generalized Attack Detection Model:

Generalized Attack Detection ModEl, which consists of two phases: attack detection, which detects the presence of an attack, and number determination, which determines the number of adversaries.
Cluster analysis method is used to perform attack detection. We formulate the problem of determining the number of attackers as a multiclass detection problem. We then applied cluster-based methods to determine the number of attacker.

3.2 Localization of Attackers:

Identify the positions of multiple adversaries even when the adversaries vary their transmission power levels. The main contribution is as follows
- To effectively detect the presence of spoofing attack
- To count the number of attackers
- To identify the location of multiple adversaries in the network
- To provide solution to identify adversaries in the network where in there is no additional cost or modification to the wireless devices themselves
- To avoid authentication key management
- To avoid overhead

3.3 Attack Detection Using Cluster Analysis:

The RSS-based spatial correlation inherited from wireless nodes to perform spoofing attack detection. It also showed that the RSS readings from a wireless node may fluctuate and should cluster together. In particular, the RSS readings over time from the same physical location will belong to the same cluster points in the n-dimensional signal space, while the RSS readings from different locations over time should form different clusters in signal space.
For RSS reading vectors of three landmarks (i.e., n = 3) from two different physical locations. Under the spoofing attack, the victim and the attacker are using the same ID to transmit data packets, and the RSS readings of that ID is the mixture readings measured from each individual node (i.e., spoofing node or victim node).


•The basic idea behind using the System Evolution method to determine the number of attackers is that all the rest of clusters are separated if the twin clusters are separable.
•The Hit Rate is lower when treating four attackers as errors than treating two attackers as errors. This indicates that the probability of misclassifying three attackers as four attackers is higher than that of misclassifying three attackers as two attackers.
•The proposed system validates the effectiveness, efficiency, and robustness of the scheme through analysis.
•The System Evolution method performs well under difficult cases such as when there exists slightly overlapping between clusters and there are smaller clusters near larger clusters.
Fig. 3.2 presents an example of using the System Evolution method to determine the number of attackers in the 802.11 network. It shows the energy calculation versus the number of clusters. The Koptimal is obtained when K = 4 with Ep(4) > Em(4) and Ep(5) < Em(5) indicating that there are four adversaries in the network using the same identity to perform spoofing attacks.

Determining The Number Of Attackers


Number of Attacker Determination:

A Silhouette Plot is a graphical representation of a cluster. To determine the number of attackers, we construct Silhouettes in the following way: the RSS sample points S= {s1,...,sN}(with N as the total number of samples) are the data set and we let C=(c1,...,cK)be its clustering into K clusters, as shown in Fig. 8. Let d(sk,sl)be the distance between skand sl. Let cj=(sj1,...,sjmj)be the jth cluster, j =1,...,K , where mj=|cj|.

4.2 Support Vector Machine Based Mechanism

Several statistic methods available to detect the number of attackers, such as System Evolution and SILENCE, we can combine the characteristics of these methods to achieve a higher detection rate. Using Support Vector Machines to classify the number of the spoofing attackers. The advantage of using SVM is that it can combine the intermediate results (i.e., features) from different statistic methods to build a model based on training data to accurately predict the number of attackers.

4.2.1 Experimental Evaluation

Table 1, shows experimental results of using SVM-based mechanism when the attacker number i ={2,3,4} for the 802.11.Here observation is that when the number of attackers equals to 2, the SVM-based method achieves the highest Hit Rate (above 99 percent) and the highest F-measure value, over 98 percent. In the case of four attackers achieves the highest Precision, above 99 percent, which indicates that the detection of the number of attackers is highly accurate, the Hit Rate decreases to about 90 percent.
By comparing the results of SVM to those of Silhouette Plot, System Evolution and SILENCE methods, here observation is that there is a significant increase of Hit Rate, Precision and F measure for all the cases of the number of attackers under study. These results demonstrate that SVM-based mechanism, a classification approach that combines training data and different statistic features is more effective in performing multiclass attacker detection when multiple attackers are present in the system.

4.3 Idol: Integrated Detection And localization Framework

IDOL: an Integrate DetectiOn and Localization system that can both detect attacks as well as find the positions of multiple adversaries even when the adversaries vary their transmission power levels.


This project proposed to use received signal strength mechanism and implement the clustering, SVM to identify the attack, a physical property associated with each wireless device that is hard to falsify and not reliant on cryptography as the basis for detecting spoofing attacks in wireless networks. It provided theoretical analysis of using the spatial correlation of RSS inherited from wireless nodes for attack detection. It derived the test statistic based on the cluster analysis of RSS readings. The approach can both detects the presence of attacks as well as determine the number of adversaries, spoofing the same node identity, also that can localize any number of attackers and eliminate them.


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