Volume 5, Issue 12, December 2017 International Journal of Advance Research in Computer Science and Management Studies

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1 ISSN: (Online) e-isjn: A Impact Factor: Volume 5, Issue 12, December 2017 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online at: Identifying Reputation Based Attacks in Trust Management Service of Cloud Armor Dr. Syed Shabbeer Ahmad Professor in CSED Muffkham Jah College of Engineering and Technology Hyderabad India Abstract: Trust management is one of the most challenging issues for the adoption and growth of cloud computing. We are providing a platform for that identifies Reputation based attacks through the Trust management service and accessing the trust of the services through feedbacks.to manage the availability of the decentralized implementation of the trust management service. For our extension work, we implemented parallel processing concept to improve the performance optimization of the system. Keywords: Attacks, Trust, Armor, Reputation, Sybil. I. INTRODUCTION Cloud computing offers dynamic, scalable, shared resources (e.g., computing power, storage, software) over the internet from remote data centers to the users (e.g., business organizations, government authorities, individuals). Trust Management is getting significance because of its immense importance in cloud environments. An effective trust management helps the cloud service consumers and providers to reap the benefits of the cloud computing technologies. Suppose a trust management service which uses reputation to establish trust between cloud service consumers and providers. Different cloud service providers who provide one or several cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS) or a combination of them are publicly available on the Internet. Cloud Armor is a framework for reputation based trust management in cloud environments, where Trust is delivered as a service (TaaS) where TMS spans distributed nodes to manage feedbacks in a decentralized way. The salient features are: Zero-Knowledge credibility proof protocol, Credibility model, Availability model. II. EXISTING SYSTEM The highly dynamic, distributed and non-transparent nature of cloud services makes the trust management in cloud environments a significant challenge. SLA is inadequate to establish trust between cloud consumers and providers. There are key issues of trust management are cloud environments. 1. Consumer s privacy. 2. Cloud services protection. 3. Trust management service s (TMS) availability. III. PROPOSED SYSTEM Cloud Armor, is a framework for reputation-based trust management in cloud environments. Cloud Armor exploits techniques to identify credible feedbacks from malicious ones. 2017, IJARCSMS All Rights Reserved 65 P a g e

Features of Cloud Armor are: 1. Zero Knowledge credibility proof protocol 2. Credibility model- helps in detecting misleading feedback from collusion and sybil attacks. 3.

2 Features of Cloud Armor are: 1. Zero Knowledge credibility proof protocol 2. Credibility model- helps in detecting misleading feedback from collusion and sybil attacks. 3. Availability model-proposed to spread several distributed nodes to manage feedbacks given by users in a decentralized way. 4. Performance optimization of system. IV. ARCHITECTURE V. FEEDBACK COLLUSION DETECTION 1. Feedback Density To support determination of credible trust feedbacks. To reduce value of the multiple feedbacks which are given from the same user is decided by feedback volume collusion factor. 2017, IJARCSMS All Rights Reserved ISSN: (Online) Impact Factor: e-isjn: A P a g e

3 2. Occasional Feedback Collusion Some attacks occur sporadically A sudden change in the feedback behavior indicates an occasional feedback collusion because the change of the number of trust feedbacks given, happen abruptly in a short period of time. Here we use time as a factor. VI. SYBIL ATTACKS DETECTION 1. Multi-Identity Recognition he main goal is to protect cloud services from users who use multiple identities to manipulate the trust results. We aim to identify similar credentials in identity records, given by malicious users. The frequency of a particular credential value is calculated as number of times of appearance that the credential value appears in the credential attribute normalized by total number of identity records. 2. Occasional Sybil Attacks The sudden changes in the total number of established identities indicates a possible occasional Sybil attack, wherein the established identities give feedbacks to service during a time period. VII. AVAILABILITY MODEL To maintain the availability of trust management service, we consider operational power to allow TMS nodes to share workload. Operational power factor compares the workload for a particular node with the average workload of all the nodes. Based on the operational power factor TMS uses the workload threshold to automatically adjust the number of nodes N tms that host the instances by creating extra instance to maintain desired workload for each node. VIII. TRUST RESULT CACHING If the trust management service instance receives a trust assessment request from a user it uses the trust result in the cache instead of computing the trust result. The trust management service instance will update the cache based on the number of new trust feedbacks (i.e., since the last update) given by a particular consumer and the number of new trust feedbacks given to a particular cloud service. 2017, IJARCSMS All Rights Reserved ISSN: (Online) Impact Factor: e-isjn: A P a g e

4 Dr. Syed et al., 1. Input :f feedbacks 2. Count 3. If ( ) TMS instance counts the total number of new trust feedbacks given to particular cloud service ( ) ( ) then /*TMS determines whether a recalculation is required for trust result related to a cloud service 4. Compute trust_result; 5. End if IX. ALGORITHM Algorithm manages the instances of nodes that handles the requests of users. 1. Initialization: tmsid(0) computes Op(stms) for all trust management service nodes if any 2. Generation: tmsid(0) estimates Ntms and generates additional trust management service nodes if required 3. Prediction: tmsid(0) predicts new availability of all trust management service nodes 4. Caching: tmsid(0) starts caching trust results (consumer side) and tmsid(s) start caching trust results (cloud service side) 5. Update the node instance table. 6. Check Workload 1: tmsid(0) checks whether ew(stms) is triggered by any tmsid(s) before reallocation if Op(stms) ew(stms) and V(stms) V(meantms) then go to next step else go to step 3 end if 7. Reallocation: tmsid(0) asks tmsid(s) which triggered ew(stms) to reallocate all trust feedbacks of the cloud service that has the lowest V(s) to another tmsid(s) that has the lowest V(stms) perform step 5 8. Check Workload 2: tmsid(0) computes Op(stms) for all trust management service nodes and checks whether ew(stms) is triggered for any tmsid(s) after reallocation if Op(stms) ew(stms) and V(stms) V(meantms) then go to step 2 Else go to step 3 end if. X. EXPERIMENTAL SETUP Hardware Requirements 1. Processor Pentium-IV 2.Speed -1.1Ghz 3.RAM -256MB(min) 4.Hard Disk- 20GB 5.Monitor SVGA Software Requirements 1. Operating System- Windows 10 2.Programming Language-Java. 2017, IJARCSMS All Rights Reserved ISSN: (Online) Impact Factor: e-isjn: A P a g e

Sybil attacks detection Using parallel processing, calculating Collusion attack 2017, IJARCSMS

Trust values of services XII. RESULTS ANALYSIS TMS Trust before detection value Trust after detection value Cloud service 0.32 0.3 Cloud service 0.289 0.

8 Trust values of services XII. RESULTS ANALYSIS TMS Trust before detection value Trust after detection value Cloud service Cloud service It shows the time difference taken between the normal execution and the parallel execution in nano seconds. It clearly depicts that our proposed solution for performance optimization of trust management service helps in processing of faster results. As parallel execution takes less time compared to normal execution. 2017, IJARCSMS All Rights Reserved ISSN: (Online) Impact Factor: e-isjn: A P a g e

9 XIII. CONCLUSION AND FUTURE WORK Cloud service users feedback is a good source to assess the overall trustworthiness of cloud services. Cloud consumers can discover, add feedbacks and assess the trust of cloud services. It help protect and detect malicious users to protect the integrity of the cloud service against collusion attacks and Sybil attacks.for Performance optimization of system, parallel processing is used to get faster results. For our future work, we can recommendation techniques which will evaluate the user s preference and recommend cloud services according to the user s history and background. References 1. Talal H. Noor, Quan Z. Sheng, Lina Yao, Schahram Dustdar and Anne H.H. Ngu, CloudArmor: Supporting Reputation-based Trust Management for Cloud Services, IEEE Transactions on Parallel and Distributed Systems, vol. 27, no. 2, pp , M. Wanga, G. Wangb, J. Tianb, H. Zhanga and Y. Zhanga. An Accurate and Multi-faceted Reputation Scheme for Cloud Computing, The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC-2014) Procedia Computer Science, 2014, vol. 34, pp R. Ko, P. Jagadpramana, M. Mowbray, S. Pearson, M. Kirchberg,L. Qianhui, and L. B. Sung, TrustCloud: A framework for accountability and trust in cloud computing, in Proc. IEEE World Congr. Services, 2011, pp I. Brandic, S. Dustdar, T. Anstett, D. Schumm, F. Leymann, and R.Konrad, Compliant cloud computing (C3): Architecture and language support for user-driven compliance management in clouds, in Proc. 3rd Int. Conf. Cloud Comput., 2010, pp S. Habib, S. Ries, and M. Muhlhauser, Towards a trust management system for cloud computing, in Proc. 10th Int. Conf. Trust,Security Privacy Comput. Commun., 2011, pp K. Hwang and D. Li, Trusted cloud computing with secure resources and data coloring, IEEE Internet Comput., vol. 14, no. 5,pp , Sep./Oct , IJARCSMS All Rights Reserved ISSN: (Online) Impact Factor: e-isjn: A P a g e

Cloud services using supporting reputation based trust management

Cloud services using supporting reputation based trust management E. Prathipa #1, N. Geetha *1 Mailam Engineering College, Mailam #1,*1 Prathielumalai23@gmail.com #1 Abstract - In cloud computing growth,