INTRODUCTION
Wireless network technology is one of the hottest topic in in network fundamentals. Wireless networks serves many features. In various cases they uses cable replacements, where in other cases they are used to provide access to corporate data from remote location. The main four categories of wireless networks are wireless personal area network (WPAN), wireless local area networks (WLAN), wireless wide area networks (WWANs), and satellite networks. These networks are now commercially available in most of the region.
The wireless network are categorized into two broad segments: short-range and long-range. Short-range wireless applicable to networks that are confined to a limited area, this are applicable to local area networks (LANs). The same as Wireless local area network are used in building or campuses; typically 100 meters is the coverage area; the function is extension or alternative to wired LAN, associated cost is Low-Medium, typical through-put is 1-54Mpps. The standards used in WLAN is 802.11 a, b, g, HIPERLAN/2. IEEE 802.11 is a combination of media access control (MAC) and physical layer (PHY) specifications for implementing wireless local area network (WLAN) computer communication in the 2.4 – 60 GHz frequency bands.
Wireless networks turns vulnerable to Sybil attacks, in which Sybil node poses as many identities in order to gain disproportionate influence. Various defenses based on spatial variability of wireless channels exist, but something not exposed on commodity 802.11 devices. There introduces numerous security concern to defense against the attack, since participants are not vetted this assumption is easily broken by a Sybil attack. Defenses which are proposed falls into categories like trusted certification, social network based technique, misbehavior detection, resource testing, localization techniques. The trusted certification used access point or certification to vet participants, thus not useful in open nature of wireless network. Resource testing method are most easily defeated in ad-hoc network of resource limited mobile devices by attackers with access to greater resources.
The localization technique, supports defense mechanism against open ad-hoc network without trusted certification. RSSI (Received Signal Strength Indication) is a localization technique uses the spatial correlation between the signal strength and physical location of a node to find out the presence of a Sybil node. It is important to note RSSI does not relay on the quality of signal and usually an action is required for mapping RSSI distance values.
In Figure 1, (a) represents the RSSI observation from trusted APs used to identifies the Sybil’s, where S is a Sybil presented by attacker M. Trusted RSSI observations, which are not generally available in open ad-hoc networks. In Figure 1, (b) represents the participant themselves act as observers. The observation are untrusted, coming from possible lying neighbors. In Figure 1, (c) represents I believes S1 and S2 are falsified observation and incorrectly accept them and reject A and B as Sybil.
A Signalprint is used, as its direction stays unchanged, as RSSI can be changed by varying transmit power. Signalprint are hard to spoof and strongly correlated with physical location of nodes. Signalprints allow a control over WLAN to reliably single out clients. Instead of identifying clients based on MAC addresses or other data, Signalprints allow the system to recognize them based on how clients look like in terms of signal strength levels.
Murat Demirbas and Oguejiofor O.S noted that RSSI is a robust and lightweight solution for Sybil attack issue based client position in both indoor and outdoor environment. The framework naturally evaluates the distance between node hubs by measuring the RSSI (got signal quality marker) at a suitable number of node hubs.
The harmful attack against ad hoc networks is known as the Sybil attack. Sybil nodes refer to a malicious device’s additional identities. Open nature of wireless network need a defense against Sybil attack, something exposed on commodity 802.11 devices. Without requiring trust in any other node or authority, RSSI is inherent use true or false RSSI observation reported by one-hop neighbors. The method prior round reveals RSSI information is used to reduce the computation time by comparing the RSSI prior round values. Performing Mason Test protocol with two components: collection of RSSI observations and Sybil classification. The protocol classifies non-Sybil and Sybil by vetting participants without using trusted authority.
RELATED WORK
3 METHOD AND BACKGROUND
In this segment, we summarize the problem, solution framework and briefly discus RSSI and Signalprint methods.
3.1 PROBLEM STATEMENT
We extent the signalprint and RSSI based Sybil detection and classification methods to work without any prior detection or observation of participants to determine which of its one-hop neighbor are non-Sybil in open wireless network. The framework that formed allows us to identify the truthful subset selection of nodes for secure safe and trustful protocol.
The framework formed, Figure 2 illustrates truthful subset selection in three steps:
Step 1: First participant takes turn of broadcasting probe packet and other nodes record observed RSSI
Step 2: All the participant share their observation with their one-hop neighbors, i.e. each and every participant holds the RSSI observation of their one-hop neighbors.
Step 3: Finally each and every participant individually select a truthful subset for signalprint base Sybil classification.
3.2 RSSI (Received Signal Strength Indication)
Received Signal Strength Indication is a term of measuring the relative quality of the signal of the client nodes. The strength is based on the nodes signal as seen on receiving device, e.g. a smartphone. The strength of the signal is based on the distance and broadcasting power value, at maximum broadcasting power the RSSI ranges from 40-50 m distance.
Deploying one node to transmit “hello” messages with constant power (0 dBm) and another acts like receiver and capture RSSI then transmit them. The transmitter sends message over 1000 times by setting distance of 15 cm between the transmitter and receiver. But this deployment results to non-uniform nature of RSSI and poor correlation of RSSI value makes it unsuitable for Sybil detection. So, we deploy two receiver to compare ratio of RSSI instead of absolute value of RSSI and observe the time varying of RSSI. By comparing the ratio, RSSI can take care of varied transmission power at sender. By using different transmitting power the sender broadcast 1000 messages. RSSI values are recorded by two receivers and transmit them to base station.
The base station analysis and compute the ratio of two RSSI values it received from the two receiver at time t1 and t2. The difference of RSSI ratio is calculated and logs this value. This results in uniform distribution of values by following Gaussian Probability Distribution with standard distribution of 0.066 and 0.106. If D1 and D2 is the difference of RSSI ratio in same location and I1, I2, I3 and I4 are the node identity with a threshold.
((R_I1^D1)/(R_I2^D1 )-(R_I1^D2)/(R_I2^D2 ))<σ,((R_I1^D1)/(R_I3^D1 )-(R_I1^D2)/(R_I3^D2 ))<σ,
((R_I1^D1)/(R_I4^D1 )-(R_I1^D2)/(R_I4^D2 ))<σ (1)
ρ=0.000,μ=0.000,σ=0.100
Figure 3 Comparing ratio of RSSI
It is safe to set σ as 0.1 and threshold to 0.5 to detected Sybil node 99.999%, i.e. the threshold to be 5σ, more specifically 0.1.
3.3 SIGNALPRINT