Prevention of Spoofing using ZKP in Wireless Networks

Prevention of Spoofing using ZKP in Wireless Networks

Prevention of Spoofing using ZKP in Wireless Networks

Rashmi T V

Department of Information Science and Engg BNM Institute of Technology Bangalore,India

rashmitv@bnmit.in

Mona

Department of Information Science and Engg BNM Institute of Technology Bangalore,India

mona@bnmit.in

KeywordsElliptic Curve Cryptography (ECC), Spoofing Attacks, Wireless Networks, Zero Knowledge Protocol (ZKP).

1. INTRODUCTION

   The research is going on to optimize the routing protocols in wireless networks and security is a factor that has to be given more importance in a wireless network but majority of the protocols haven't taken security into consideration. It is believed that routing protocols should be designed by keeping security in mind. Also, the nodes in wireless networks are constrained nodes with limited capabilities and the attacker may be with higher capabilities. But encrypting techniques have perpetually difficult calculation that requires more processing power. so, not fit for all forms of networks. Lately, to detect spoofing attacks, received signal strength (RSS) has been used. RSS represents the physical characteristics of wireless nodes and stations those are unique to each device and it represents the 6 signal strength at which the frame is received by the antenna. RSS value cannot be spoofed and it has correlation with the site of message sender, closer the sender is signal strength is high, far is the sender from the receiver signal strength will be low.

   Attacks that spoof traffic can also result in other traffic injection attacks, including Denial-of-Service (DoS) attacks, attacks on access control lists, and attacks on rogue access points [10], [11]. [12] and [13] provide a thorough overview of

   potential spoofing attacks. Additionally, in a tidy network, multiple attackers may adopt the same identity and work together to launch risky attacks, like a denial-of-service attack and an attack on network resource utilisation, more quickly. As a result, it is critical to spot the presence of spoofing assaults, count the number of attackers, identify the various attackers, and locate and eradicate them.

   The remainder of the essay is structured as follows: Section 2 describes a literature review and various spoofing attack techniques; The technique for the proposed system is presented in Section 3, which also covers network initialization, cluster formation, fingerprint generation, data encryption, data transmission, and decryption. The experimental findings made utilizing the suggested procedures are presented in Section 4. Section 5 offers conclusions and discusses potential future improvements.

2. RELATED WORK

   We have examined many ways that have been used in this field by various researchers. Seyedeh Zahra Rajabi et al. [5] presented a unique received signal strength (RSS)- 1 based fuzzy logic methodology for identifying spoofing attacks in wireless networks.

   Using four 802.11 access points, this technique collects RSS values from nodes.Fuzzy approaches are utilised to distinguish between various signal strength levels and to determine the location of each node in 11 wireless networks. Sushil Yadav et al. [6] have developed a technique to identify spoofing attacks that makes use of the spatial correlation of the received signal strength (RSS) inherited from numerous wireless nodes. Following the development of a multiclass detection and a Cluster-based approach, it determines the number of attackers.

   An approach that uses a different authentication method in addition to MAC address filtering and regularly re- authenticates the client has been proposed by Wesam S. Bhaya and Samraa A. AlAsady [7]. This is supported by two factors. Using the computer name, CPU ID, and the current time as the inputs to a hash function (one-way function), this hash value is

   then inserted in the slack fields of the header of the frame (steganography) in the first step.

   V Bharath Srinivas and Dr Syed Umar [11] have proposed a technique that does both the tasks of detecting spoofing attacks, as well as locating the positions of attackers forming the attacks. In this, they first use an attack detector for checking wireless spoofing that utilizes K-means cluster analysis. Next, they have described how attack detector mechanism is integrated into a real time indoor localization system, which is also capable of localizing the positions of the different attackers.

   P. Kiruthika Devi and Dr. R. Manavalan have 5 offered a complete review of the various spoofing attack detection techniques utilised in wireless sensor networks [8]. B.VidhyaM.E [9] has proposed a method for detecting spoofing attacks, when more attackers use the identicalness of the same node, and localising multiple attackers that uses spatial information, the physical property corresponding with each wireless node that is difficult to falsify but is not based on cryptographic authentication schemes. The suggested solution in this scenario uses spatial correlation of the received signal strength (RSS), inherited from numerous wireless nodes, to detect the spoofing assault.

3. PROPOSED SYSTEM

   In the proposed approach, we guard against spoofing attacks using the zero knowledge protocol (ZKP). We additionally protect data using elliptic curve cryptography (ECC). If the link between the source and the destination is long, any node can be compromised, and there is a higher probability of attacks, we construct clusters in the network and transfer data directly between nodes and their cluster leaders. To identify if a node is valid or not, every node in the network has a fingerprint established. In this case, attacks are less likely and communication node verification comes before data transmission.

   communication, the ECC algorithm will be applied to avoid the data access from unauthorized user present in the network.

   1. For choosing the cluster head, Weighted Clustering Algorithm will be used. In this algorithm, the neighbors will verify the weights of nodes on the basis of distance among the neighbor, movement angle and the speed and chooses the best possible cluster head.

      For (every node in the network) Calculate the Angle difference Av Calculate the Degree difference Dv Calculate the Speed difference Sv

      Calculate the weight Wv = C1*Dv+C2*Av+C3*Sv If (Wv > Weight of neighbor node)

      Select Wv as the cluster head Else

      Neighbor node is the cluster head

   The Zero Knowledge protocol is the method employed in this case to detect the spoofing attempt. In this protocol, each node will initially receive a key, after which it can be validated by answering a series of questions based on the key without ever disclosing the key. The node is a legitimate node if it responds to every query; otherwise, it is a fake node.

   The verifier and prover are the two persons involved. The verifier will check to see if the node can be trusted. Prover will demonstrate its reliability. The following is the pseudo code for the ZKP and ECC algorithms. Verifier

   In the Verifier side initialize the state as false. Verifier Step1:

   If (ChallengeSoved1)

   Proceed for the next step of challenge

   Else

   Reject the packets from that particular node

   Verifier Step2:

   If (ChallengeSolved1 is correct)

   Gives the challenge based on the step1 challenge If (ChallengeSolved2)

   If (ChallengeSolved1==1 && ChallengeSolved2) Accept the node as an original node

   Else

   Reject the packets from that particula node

   Nodes will begin communicating through the cluster heads once the nodes have been placed in the network and the clusters have formed. The ZKP technique will be used to authenticate whether a node is a legitimate node or a faked node after the nodes begin talking. If it is a legitimate node, the message may be sent throughout the network; otherwise, the packets from that specific node would be dropped. following the provision for

   It will answer for the verifier challenges. Prover Step1:

   Receive the challenge If (ChallengeAccepted)

   Send Proof to the Verifier by avoiding the communication with key

   Prover Step2 :

   If ( NextChallengeAccepted)

   Generate a proof Return to Verifier

   In ECC algorithm, it defines the curve in the finite field Fp and generate a point on the curve which will be consisting of two elements in the Fp. The ECC algorithm will be used for data encryption and decryption to avoid the data to be stolen by third party. Pseudo code The range used here is 0 to p-1 and the equation is y2 mod p=(x3 +ax+b) mod p.

   For (Each value of x)

   Choose the value of y within 0 to p-1 which satisfies the equation Display the required point

   Generate the public key Q and private P by using the point on the curve

   If (keys generated) Distribute the keys

   If (Sender starts sending message)

   Encrypt the message using the equations, C1 = k*P and

   C2 = M + k*Q

   Where, C1 & C2 are Cipher texts k is generated randomly within the range 0 to p-1 M is a point on the curve with the message.

   If (Message received at the destination) Decrypt the message M = C2 r1* C1

   Where r1 is the random number within the range 0 to p-1

4. RESULTS & DISCUSSION

   The proposed security approach discussed in the preceding part is tested using simulation to determine its viability. How to assess the proposed system's performance in terms of (a) latency, (b) packet delivery ratio, (c) routing overhead, and (d) throughput is described in this section. The attack is compared before and after the solution is applied to create a performance graph, which is then evaluated using the aforementioned metrics.

5. CONCLUSION & FUTURE WORK

The suggested approach is an effective network security system that focuses on protecting against spoofing attacks, potential DoS assaults, and Man-In-The-Middle attacks in wireless networks while also offering data protection. The findings demonstrate that the adopted technique offers increased protection against spoofing attacks with little delay and minimal routing overhead. And the suggested technique uses less memory and operates effectively.

The suggested method offers protection in small networks against spoofing, potential DoS assaults, and Man-In-The- Middle attacks. It may be improved for medium- and large- scale networks as well as other comparable attacks including phishing, jamming, and sybil attacks, among others.

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