Menal et al, IJRCCT, Vol 5, Issue- 5, May- 2016
ISSN (Online) 2278- 5841 ISSN (Print) 2320- 5156
Secured Bluetooth Authentication Using Artificial Neural Networks 1
Menal, Dr. 2Sumeet Gill Assistant Professor, Department Of Computer Science Maharaja Surajmal Institute, Janakpuri, Delhi 2 Assistant Professor ,Department Of Mathematics Maharashi Dayanand University,Rohtak
[email protected],
[email protected] of locking these devices or using static passwords for user’s authentication in these devices is not sufficient Abstract— Authentication in wireless networking is a and appropriate for such type of sensitive data. So, mechanism to proof identities and avoid masking. The each one of the mechanisms has its own advantages use of wireless devices and technology has made and disadvantages too. In the present work, we use rapid growth in the market. In all such devices, user Bluetooth authentication for our simulations. authentication is done once and is considered The present work shows one such procedure that authentic forever until it is revoked by the user. We secures such authentication mechanism. Section II present a model of authentication, where a Bluetooth depicts a brief of Bluetooth technology , Bluetooth enabled mobile phone and laptop is taken. In the security issues and highlight some research work present paper, we simulate the connection between related to our work. Section III gives detail about the artificial intelligence and cryptography. We take the mechanism of pattern recognition, storage and help of a pattern recognition technique of the Back recalling present in Artificial Neural Network. We Propagation Algorithm of Neural Network to store the simulate the patterns of password using the feed encrypted password as used in any of such devices. In forward Back propagation algorithm. Section IV the proposed mechanism the encrypted password is carries the simulation work using algorithms and this stored as network parameters. Since reverse tracking is followed by result and conclusion. from weight matrices is a difficult task, such systems I. ISSUES IN BLUETOOTH SECURITY become impossible to crack. Authentication is the process in which the communicating devices verify the identity of each Keywords: - Authentication, Back propagation, other and it is the basic step of all security systems. For every wireless system, the authentication process neural network, Bluetooth, wireless devices. is important. Mutual authentication is the first step in this process, where the system performs challenge Introductionresponse function between sender and receiver. Here, If we analyze last two decades, wireless antennas have we take a mobile device is a sender and laptop as a penetrated into almost all electronic devices, receiver. They authenticate each other based on a especially the cell phones, laptops and tablets [1]. public key system. The Session key is used to encrypt These devices are a lifeline to the outdoor world. the communication. Here the common module is Snoopers/crackers not only copy data from our implemented through UDP [8-10]. This module works devices, but render them useless or we are required to in a single slave Bluetooth piconet scenario. Bluetooth go for reformatting of devices after becoming a digital port is used in both laptop and mobile for attack victim [2-3]. These devices have penetrated and communication purpose as shown in figure 1. revolutionized our day to day life. All these cell phones and laptops, if configured are enabled not only for communication but are able to receive fund and transfers including stock trading, direct payments etc [4]. All the sensitive data of users is stored in these wireless devices. In such a wireless communication, data is transmitted through radio waves and the communication is open to everyone [5]. So it can be easily snooped. Thus the provision of security to the users is the main motive of most of the researchers, Worms and Man-in-the middle attacks are examples of threats where hacker could use one’s mobile device as the access point to the other [6-7]. Traditional method 1
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Figure: - 1. Connection at data link and physical layers Bluetooth device pairing permits two wireless devices to authenticate each other and establish a wireless connection [11]. When they transfer data amongst themselves, they do not wish that the contact data will be available to all Bluetooth devices in range. The Bluetooth specification supports the establishment of pairwise keys to allow two devices to securely communicate with each other. This pairing process comprises authentication, generation of Initialization key, generation of Link key. Bluetooth core specification relies on short, low entropy PINs for authentication. In order to protect the file or device authentication against unauthorized users, keys are used [12-14]. These keys are used to make the file more secure this means that we have to either memorize the keys or store them somewhere. For storage we use window registry, but window registry can also be edited by the intruders. In the present paper, we present a technique by which these keys are memorized by the system using Artificial Neural Network. Many researchers have conducted research on different glimpse in the field of wireless technology within the security architecture of Bluetooth and implemented impressive results with new alterations that enhance the security of the devices using Bluetooth technology. Malkani et al.[15] proposed the design of a device pairing simulator called “PSim”. This tool can be used to perform tests on different types of devices pairing methods and generate new protocols for increased security measures. Wang et al.[16] authors proposed a protocol which applies the authentication method of entering the same PIN number on both connecting devices. Patheja et al. [17] proposed a hybrid encryption technique to improve security of data in Bluetooth communication. They had used triple DES for encryption with tiger algorithm for exchanging data over short distances. III. SECURE ACCESS AUTHENTICATION BASED ON ARTIFICIAL NEURAL NETWORK Soft computing describes a set of techniques that are tolerant of impression and uncertainty. The basic soft computing techniques are Fizzy Logic (FL), Artificial Neural Network (ANNs), Probabilistic Reasoning (PR), and Genetic Algorithms (GAs) [18-22]. The ability of ANN dealing with complicated and partially true data makes this technique very much popular in intrusion detection. However ANNs are the mostly applied soft computing technique in authentication purpose in networking [23-25]. An ANN is an information processing system that is inspired by the way biological nervous systems, such as the brain process information. It is composed of a large number of highly interconnected processing elements called neurons working with each other to solve specific problems [20]. Each neuron is
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normally a summing element followed by an activation function. The output of each neuron (after applying the weight parameter associated with the connection) is fed as the input to all of the neurons in the next layer. The learning process is essentially an optimization process in which the parameters of the best set of connection coefficients (weights) for solving a problem are found [20]. Neural Networks process information in a similar way the human brain does. Neural Networks learn by example. They can’t be programmed to perform a specific task. Examples must be selected carefully otherwise useful time will be wasted or even worse, the network might be functioning incorrectly [21]. The learning methods used for Neural Network can be classified into supervised learning and unsupervised learning. Supervised learning works as an external teacher, so that each output unit is told what its desired response to input signals ought to be. Unsupervised learning uses no external teacher and is based upon only local information. In the present work we use one of the most well known Back Propagation algorithm of Neural Network to generate and memorize the identification parameters[26-27]. The commonest type of Artificial Neural Network consists of three groups or layers.Network begins with an input layer. The input layer must be connected to a hidden layer. Then this hidden layer connects to other hidden layers or directly to an output layer. A typical feed forward Neural Network is shown in Figure 2 below. Back Propagation is a feed forward neural network.
Figure: -2. A typical feed forward Neural Network with a single hidden layer. IV. EXPERIMENTAL SETUP Device authentication is a method where devices should be authenticate first and then starts communication. Here, laptop and mobile phone are
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two devices that authenticate each other using Bluetooth link keys. Both devices use 32 bit link key to authenticate each other we store these link key in the form of weights instead of storing them in windows registry. We train our system for the input given below using Back Propagation algorithm. Then, train the network so that the sample input pattern converges to the desired output pattern.
-1.8924 0.5193 0.6310 0.6482 0.736 0.7275 0.2197 0.6959 -1.3854 -1.8549 -1.9754 -1.8424 0.451 1.008 0.4323
Training of the system was carried out for following input pattern set: P=[1 1 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0;1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0] The process of training a neural network involves tuning the values of the weights and biases of the network to optimize network performances. Training is carried out according to the following parameters set for input, hidden and output layered Artificial Neural Network for complete training purpose. Table 1 shows the training parameters. 5.1 The chosen Training parameters Parameter Value Neurons in Input 32 Layer Number of 1 Hidden Layers Neurons in 25 Hidden Layer Neurons in 32 Output Layer Total no. of 11 Epochs Minimum Error 0.001 Exist in the Network Initial Weights Values between 0 and biased term and 1 values Table 1. Parameters used for Training of Network using Back Propagation Model Weights between input layer to hidden layer -1.7514 0.1405 0.6454 0.2364 1.0035 0.7028 0.6791 0.9473 0.3139 0.5127 -1.248 0.5633 -1.1116 -1.2893 -1.229 1.0002 1.0025 -1.7492 0.8958 0.2036
ISSN (Online) 2278- 5841 ISSN (Print) 2320- 5156 0.8282 0.531 -1.2458 -1.1261 0.7813 0.1626 0.6635 -1.056 -1.456 0.4775 -1.6448 0.0548 0.5585 1.0033 0.7606
Weights between hidden layer to output layer 0.3165 0.5040 0.3886 -0.1797 -0.3070 -0.4903 0.6457 0.7312 0.5967 0.6483 -0.3104 0.5045 -0.3134 -0.0825 0.4614 0.6404 -0.0443
0.2072
-0.5201
0.4643
0.6552 -0.5445 -0.1479 -0.3196 -0.3400 0.1741 0.7325 -0.3295 0.0203
0.1444
-0.8258
0.4331
-0.3787 -0.4301 0.3414 0.5131 -0.8565 -0.7887
0.2519 -0.0141 -0.5679 -0.6977 -0.1538 -0.7261
-1.0073 -0.0649 0.8662 0.9457 0.8072 0.0183
V. RESULT MATLAB Neural Network Toolbox was used for the simulation of the network. Using this tool one can define specifications like number of layers, number of neurons in each layer, activation functions of neurons in different layers, and number of training epochs. After simulation process, the performance of trained neural network is shown in figure 3. It shows that training and test performance overlap each other and the network trained successfully as the mean square error reduced to 0.001by using Back propagation algorithm which is updating the weights of hidden layer.
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[6] D. Ma and G. Tsudik, “Security and privacy in emerging Wireless Networks,” IEEE Wireless Communications,Vol. 17, No. 5, pp. 12-21, October 2010. Figure 3. Graph for Network Performance during memorized pattern VI. CONCLUSION As, Bluetooth uses wireless mode of transmission between devices, major security concerns have been raised. In the above implemented system, the link keys formed by using the back propagation neural network are in the form of network parameters and neuronal functions which is difficult to break. If the user deletes the encrypted and link keys that are generated through the exchange process between the devices after storing them in the system, they are safe enough and authentication takes place successfully.Existing methods and protocols uses extra computations and additional tool for authentication.So, it is a very tedious job to implement all the existing pairing methods using a common platform. However, the implemented system is a convenient, improved and automated analysis of Bluetooth technology that prevents wireless devices from unauthorized access. Both the network parameters and the architecture are needed for encryption and decryption. The advantages to this system are that it appears to be impossible to smash out the system without knowledge of the methodology behind it, as proved above. REFERENCES [1] W.Jansen, S.Gavrila, C.Seveillac and V.Korolev, “Smart Cards and Mobile Device Authentication: An Overview and Implementation”, NIST, NISTIR 7206, 2005. [2]
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