![Integrated Intelligent Research (IIR) International Journal of Business Intelligents
Volume: 05 Issue: 01 June 2016 Page No.86-89
ISSN: 2278-2400
89
Fig 9. Node Login process
VII. CONCLUSION AND FUTURE ENHANCEMENT
In this paper, we propose a novel behavioral biometric-based
authentication approach called OpenSesame for smart phone.
We design four waving functions to fetch the unique pattern of
user’s handwaving actions. By applying the SVM classifier,
the smart phone can accurately verify the authorized user with
the pattern of handwaving action. Experiment results based on
200 distinct users’ handwaving actions show that the Open
Sesame reaches high level of security and robustness, and
achieves good user’s experience.
REFERENCES
[1] S. Abbas, M. Merabti, D. Llewellyn-Jones, and K. Kifayat,
“Lightweight sybil attack detection in manets,” IEEE Syst. J., vol. 7,
no. 2, pp. 236–248, Jun. 2013.
[2] S. Bansal and M. Baker, “Observation-based cooperation enforcement
in ad hoc networks” arXiv:cs.NI/0307012, 2003.
[3] S. Buchegger and J.-Y. Le Boudee, “Self-policing mobile ad hoc
networks by reputation systems,” IEEE Commun. Mag., vol. 43, no. 7,
pp. 101–107, Jul. 2005.
[4] L. Butty_an and J.-P. Hubaux, “Enforcing service availability in
mobile ad-hoc WANs,” in Proc. 1st Annu. Workshop Mobile Ad Hoc
Netw. Comput., 2000, pp. 87–96.
[5] L. Butty_an and J.-P. Hubaux, “Stimulating cooperation in
selforganizing mobile ad hoc networks,” Mobile Netw. Appl., vol. 8,
pp. 579–592, 2003.
[6] H. Cai and D. Y. Eun, “Crossing over the bounded domain: From
exponential to power-law intermeeting time in mobile ad hoc
networks,” IEEE/ACM Trans. Netw., vol. 17, no. 5, pp. 1578–1591,
Oct. 2009.
[7] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott,
“Impact of human mobility on opportunistic forwarding algorithms,”
IEEE Trans. Mobile Comput., vol. 6, no. 6, pp. 606–620, Jun. 2007.
[8] J. R. Douceur, “The sybil attack,” in Proc. Revised Papers 1st Int.
Workshop Peer-to-Peer Syst., 2002, pp. 251–260.](https://image.slidesharecdn.com/v5i12016paper20-230203103406-65cc8950/85/Detection-of-Node-Activity-and-Selfish-Malicious-Behavioral-Patterns-using-Watchdog-Chord-Monitoring-4-320.jpg)
Detection of Node Activity and Selfish & Malicious Behavioral Patterns using Watchdog � Chord Monitoring
- 1. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.86-89 ISSN: 2278-2400 86 Detection of Node Activity and Selfish & Malicious Behavioral Patterns using Watchdog – Chord Monitoring M.Vanishree1 ,R.Arun2 1 PG Scholar, Dept. of CSE,Archana Institute of Technology. 2 Assistant Professor,Dept. of ECE,Sri Venkateswaraa College of Technology Email:vanishreem30@gmail.com,arun_ece05@yahoo.co.in Abstract-Mobile ad-hoc networks(MANETs) assume that mobile nodes voluntary cooperate in order to work properly. This cooperation is a cost-intensive activity and some nodes can refuse to cooperate, leading to a selfish node behaviour. Thus, the overall network performance could be seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes. However, the detection process performed by watchdogs can fail, generating false positives and false negatives that can induce to wrong operations. Moreover, relying on local watchdogs alone can lead to poor performance when detecting selfish nodes, in term of precision and speed. This is especially important on networks with sporadic contacts, such as delay tolerant networks (DTNs), where sometimes watchdogs lack of enough time or information to detect the selfish nodes. Thus, We apply chord algorithm to identify behavior pattern of one shelf by two neighborhood nodes and themselves. Servers will finally categories nature of node. Keywords - Wireless networks, MANETs, opportunistic and delay tolerant networks, selfish nodes , chord algorithm. I. INTRODUCTION A mobile ad hoc network (MANET) is a continuously self- configuring, infrastructure-less network of mobile devices connected without wires. Ad hoc is Latin and means "for this" . MANETs are a kind of Wireless ad hoc network that usually has a routable networking environment on top of a Link Layer ad hoc network. MANETs consist of a peer-to-peer, self- forming, self-healing network. MANETs circa 2000-2015 typically communicate at radio frequencies (30 MHz - 5 GHz) 1.1 TYPES OF MANET: Vehicular Ad hoc Networks (VANETs) are used for communication between vehicles and roadside equipment. Intelligent vehicular ad hoc networks (InVANETs) are a kind of artificial intelligence that helps vehicles to behave in intelligent manners during vehicle-to-vehicle collisions, accidents. Smart Phone Ad hoc Networks (SPANs) leverage the existing hardware (primarily Bluetooth and Wi-Fi) in commercially available smart phones to create peer-to-peer networks without relying on cellular carrier networks, wireless access points, or traditional network infrastructure. SPANs differ from traditional hub and spoke networks, such as Wi-Fi Direct, in that they support multi-hop relays and there is no notion of a group leader so peers can join and leave at will without destroying the network. Internet based mobile ad hoc networks (iMANETs) are ad hoc networks that link mobile nodes and fixed Internet- gateway nodes. For example, multiple sub-MANETs may be connected in a classic Hub-Spoke VPN to create a geographically distributed MANET. In such type of networks normal ad hoc routing algorithms don't apply directly. One implementation of this is Persistent System's CloudRelay. Military / Tactical MANETs are used by military units with emphasis on security, range, and integration with existing systems. Common waveforms include the US Army's SRW,Harris's ANW2 and HNW, Persistent Systems' Wave Relay, Trellisware's TSM and Silvus Technologies' StreamCaster. A mobile ad-hoc network (MANET) is an ad-hoc network but an ad-hoc network is not necessarily a MANET. COOPERATIVE networking is currently receiving significant attention as an emerging network design strategy for future mobile wireless networks. Successful cooperative networking can prompt the development of advanced wireless networks to cost-effectively provide services and applications in contexts such as vehicular ad hoc networks (VANETs) or mobile social networks.Two of the basic technologies that are considered as the core for these types of networks are mobile ad-hoc networks (MANETs) and opportunistic and delay tolerant networks (DTNs).The cooperation on these networks is usually contact based. Mobile nodes can directly communicate with each other if a contact occurs (that is, if they are within communication range). Supporting this cooperation is a cost intensive activity for mobile nodes. Thus, in the real world, nodes could have a selfish behaviour, being unwilling to forward packets for others. Selfishness means that some nodes refuse to forward other nodes’ packets to save their own resources. The literature provides two main strategies to deal with selfish behaviour: a) motivation or incentive based approaches, and b) detection and exclusion. The first approach, tries to motivate nodes to actively participate in the forwarding activities. These approaches are usually based on virtual currency and/or game theory models. The detection and exclusion approach is a straight-forward way to cope with selfish nodes and several solutions have been presented.A more detailed study shows that a moderate concentration of node selfishness (starting from a 20 percent level) has a huge impact on the overall performance of MANETs, such as the average hop count, the number of packets dropped, the offered throughput, and the probability of reachability. In DTNs, selfish nodes can seriously degrade the performance of packet transmission.For example, in two-hop relay schemes, if a
- 2. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.86-89 ISSN: 2278-2400 87 packet is transmitted to a selfish node, the packet is not re- transmitted, therefore being lost. Therefore, detecting such nodes quickly and accurately is essential for the overall performance of the network. Previous works have demonstrated that watchdogs are appropriate mechanisms to detect misbehaving and selfish nodes. Essentially, watchdog systems overhear wireless traffic and analyse it to decide whether neighbour nodes are behaving in a selfish manner. When the watchdog detects a selfish node it is marked as a positive detection (or a negative detection, if it is detected as a non selfish node). Nevertheless, watchdogs can fail on this detection, generating false positives and false negatives that seriously degrade the behavior of the system. II. ARCHITECTURAL OVERIEW A selfish node usually denies packet forwarding in order to save its own resources. This behaviour implies that a selfish node neither participates in routing nor relays data packets . A common technique to detect this selfish behavior is network monitoring using local watchdogs. A node’s watchdog consists on overhearing the packets transmitted and received by its neighbours in order to detect anomalies, such as the ratio between packets received to packets being retransmitted . By using this technique, the local watchdog can generate a positive (or negative) detection in case the node is acting selfishly (or not). Fig 1. The above diagram is the architecture to identify the Selfish node. III. BASIC PRINCIPLES 3.1 SELFISH NODE ACTIVITY Every node will add their friends names in their list. Every node will spend some energy while transmitting the data in the network. This type of node will try to send the data only to its friend list nodes. If Node 1 adds Node 5 as its friend, node 1 will send the data if it transmits the data via its friend node 5 and it will not transmit the data via some other nodes. These types of nodes are named as Selfish Nodes. Due to the selfish nature and energy consuming, selfish nodes will not transmit the data to some other nodes which are not in their friends list. 2 CHORD ALGORITHM The Chord algorithm is implemented within a ChordNode class. ChordNode has methods corresponding to the primary algorithms of the Chord Protocol: • findSuccessor • findPredecessor • leave and other supporting methods such as updateOthers, initFingerTable, and so on. Fig 2. The number of messages required to join compared with the theoretically expected values Fig 3 The number of messages required to find a file with differing cloud sizes Using Chord provided an efficient, distributed naming service for our peer-to-peer system. Although problems were discovered in the algorithm’s published paper, the fundamental algorithm works and scales logarithmically as claimed. We found that by implementing balanced joins, we could achieve about a 20% increase in load balancing fairness based on the fact that files will be more evenly allocated in the chord. We suggest several improvements for the application and Chord, including security and distributed mutual exclusion, and offer various informal proofs for some of the suggested enhancements. 3.2.1 CONSTRUCTION OF CHORD ALGORITHM: Fig 4. Construction Of Chord Algorithm 3.3 WATCH DOG ACTIVITY MONITORING Every nodes selfish or malicious behavior is monitored and when a node detects a selfish node using its watchdog, it is marked as a positive, and if it is detected as a non selfish node, it is marked as a negative. Later on, when this node contacts another node, it can transmit this information to it; so, from that moment on, both nodes store information about these positive (or negative) detections. Therefore, a node can become aware about selfish nodes directly (using its watchdog) or indirectly. Our main logic is to identify the best nodes through the watchdog activity monitoring. IV. SYSTEM ANALYSIS 4.1 Existing System
- 3. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.86-89 ISSN: 2278-2400 88 In the EXISTING SYSTEM, the node could have a selfish behavior, being unwilling to forward packets for others. So the overall network performance could be seriously affected. Drawbacks of Existing System Waiting time is increased Unreliable Less data transmission rate Less effective Less security Poor network performance 4.2 PROPOSED SYSTEM In the PROPOSED SYSTEM, data nodes are distributed among the cluster. Every node assigned with set of friend and enemies by themselves. There are selfish nodes and normal nodes in the cluster. Selfish node will not transmit packet to enemy nodes. It transmitted data packet only to friends list. Selfish nodes are greedy in transmission. So that it accepts most of the data transmission by themselves in the network. However, the detection process performed by watchdogs can fail, generating false positives and false negative that can induce to wrong operation.In the MODIFICATION PROCESS, we add up another group called malicious. In the malicious node will drop the packet or transmit the packet to the wrong destination. Event it would add up extra data during transmission. We apply chord algorithm to identify behavior pattern of one shelf by two neighborhood nodes and themselves. Servers will finally categories nature of node. ADVANTAGES Waiting time is decreased Reliable High data transmission rate More effective Increase network performance High security V. SYSTEM DESIGN UML DIAGRAM: UML is simply anther graphical representation of a common semantic model. UML provides a comprehensive notation for the full lifecycle of object-oriented development. 5.1 Use Case Diagram Use case diagrams overview the usage requirement for system. they are useful for presentations to management and/or project stakeholders, but for actual development you will find that use cases provide significantly more value because they describe “the meant” of the actual requirements. A use case describes a sequence of action that provide something of measurable value to an action and is drawn as a horizontal ellipse New User Registrastion User Login Network Creation Source Node Encryption Technique Selfish Node Identification Server Malicious Node Destination Node Decryption Technique Fig 5. Use Case Diagram 5.2 COLLABORATION DIAGRAM Source Node Account Validation Encryption Technique Self fish Node Decryption Technique Destination Node Buffer Level 1: Sign to send data 2: Validate the user account 3: Data isencrypted 4: Check with self fish attack 5: Check the buffer level of the each node 6: Reach the destination and decrypt the data 7: Can see the data 8: Respons to the source node Fig 6 Collaboration Diagram VI. EXPERIMENTAL RESULT Fig 7. To select the nodes connected through network Fig 8. Used to Login Nodes.
- 4. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 05 Issue: 01 June 2016 Page No.86-89 ISSN: 2278-2400 89 Fig 9. Node Login process VII. CONCLUSION AND FUTURE ENHANCEMENT In this paper, we propose a novel behavioral biometric-based authentication approach called OpenSesame for smart phone. We design four waving functions to fetch the unique pattern of user’s handwaving actions. By applying the SVM classifier, the smart phone can accurately verify the authorized user with the pattern of handwaving action. Experiment results based on 200 distinct users’ handwaving actions show that the Open Sesame reaches high level of security and robustness, and achieves good user’s experience. REFERENCES [1] S. Abbas, M. Merabti, D. Llewellyn-Jones, and K. Kifayat, “Lightweight sybil attack detection in manets,” IEEE Syst. J., vol. 7, no. 2, pp. 236–248, Jun. 2013. [2] S. Bansal and M. Baker, “Observation-based cooperation enforcement in ad hoc networks” arXiv:cs.NI/0307012, 2003. [3] S. Buchegger and J.-Y. Le Boudee, “Self-policing mobile ad hoc networks by reputation systems,” IEEE Commun. Mag., vol. 43, no. 7, pp. 101–107, Jul. 2005. [4] L. Butty_an and J.-P. Hubaux, “Enforcing service availability in mobile ad-hoc WANs,” in Proc. 1st Annu. Workshop Mobile Ad Hoc Netw. Comput., 2000, pp. 87–96. [5] L. Butty_an and J.-P. Hubaux, “Stimulating cooperation in selforganizing mobile ad hoc networks,” Mobile Netw. Appl., vol. 8, pp. 579–592, 2003. [6] H. Cai and D. Y. Eun, “Crossing over the bounded domain: From exponential to power-law intermeeting time in mobile ad hoc networks,” IEEE/ACM Trans. Netw., vol. 17, no. 5, pp. 1578–1591, Oct. 2009. [7] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott, “Impact of human mobility on opportunistic forwarding algorithms,” IEEE Trans. Mobile Comput., vol. 6, no. 6, pp. 606–620, Jun. 2007. [8] J. R. Douceur, “The sybil attack,” in Proc. Revised Papers 1st Int. Workshop Peer-to-Peer Syst., 2002, pp. 251–260.