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Abstract: In this paper, an image fusion scheme based on Hilbert vibration decomposition (HVD) is proposed. In this proposed technique, the images to be fused are first enhanced and then converted into 1-D signals which are decomposed using the HVD technique into different components called energy components. These energy components are fused by taking the average of corresponding energy components except the last component having least energy. Simulation results of the proposed technique are carried out in MATLAB and its performance is compared with other existing techniques using some commonly used performance metrics. It is seen that the proposed technique gives better visual appearance of the fused image than other existing techniques and the values of the several performance metrics are also better/comparable with other techniques. The simulation results obtained for color images show that the proposed algorithm works well for color images in HSI color space also.

Keywords: Image Fusion; Image Enhancement; Hilbert Vibration Decomposition.

References: 

1. Li, Shutao, Bin Yang, and Jianwen Hu. "Performance comparison of different multi-resolution transforms for image fusion." Information Fusion, vol.12, no. 2, pp. 74-84, 2011.
2. Pohl, Christine, and John van Genderen. “Structuring contemporary remote sensing image fusion.” International Journal of Image and Data Fusion, vol. 6, no. 1, pp. 3-21, 2015.
3. Zhijun Wang, "A comparative Analysis of Image fusion Methods." IEEE Trans. on GeoscienceAnd Remote SenSing,vol. 43, pp. 1391-1402, 2005.
4. M. Tu, S.c. Su, H.C. Shyu, and P. S. Huang, "A new look at HIS likeimage fusion methods, " Information Fusion J, vol. 2, pp. 177-186, 2001.
5. Bai, Xiangzhi, Yu Zhang, Fugen Zhou, and Bindang Xue. "Quadtree-based multi-focus image fusion using a weighted focus-measure." Information Fusion, 22, pp. 105-118, 2015.
6. Toet, Alexander. "Image fusion by a ratio of low-pass pyramid." Pattern Recognition Letters,  vol. 9, no. 4, pp. 245-253, 1989.
7. Liu, Zheng, Kazuhiko Tsukada, Koichi Hanasaki, Yeong-Khing Ho, and Y. P. Dai. "Image fusion by using steerable pyramid." Pattern Recognition Letters,  vol. 22, no. 9, pp. 929-939, 2001.
8. Li, Hui, B. S. Manjunath, and Sanjit K. Mitra. "Multisensor image fusion using the wavelet transform." Graphical models and image processing,  vol. 57, no. 3, pp. 235-245, 1995.
9. Zhang, Zhong, and Rick S. Blum. "A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application." IEEE Proceedings, vol. 87, no. 8, pp. 1315-1326, 1999.
10. Pajares, Gonzalo, and Jesus Manuel De La Cruz. "A wavelet-based image fusion tutorial." Pattern recognition, vol. 37, no. 9,pp. 1855-1872, 2004.
11. Unser, Michael. "Texture classification and segmentation using wavelet frames." IEEE Trans. on Image Processing, vol. 4, no. 11, pp. 1549-1560, 1995.
12. Li, Shutao, James T. Kwok, and Yaonan Wang. "Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images."Information Fusion,  vol. 3, no. 1, pp. 17-23, 2002.
13. Forster, Brigitte, Dimitri Van De Ville, Jesse Berent, Daniel Sage, and Michael Unser. "Complex wavelets for extended depth-of-field: A new method for the fusion of multichannel microscopy images." Microscopy Research and technique, vol. 65, no. 1-2, pp. 33-42, 2004.
14. Ray, Lee A., and Reza R. Adhami. "Dual tree discrete wavelet transform with application to image fusion." IEEE Proceeding of the Thirty-Eighth Southeastern Symposium on System Theory, pp. 430-433, 2006.
15. Nencini, Filippo, Andrea Garzelli, Stefano Baronti, and Luciano Alparone. "Remote sensing image fusion using the curvelet transform." Information Fusion, vol. 8, no. 2, pp. 143-156, 2007.
16. Tessens, Linda, Alessandro Ledda, Aleksandra Pizurica, and Wilfried Philips. "Extending the depth of field in microscopy through curvelet-based frequency-adaptive image fusion.", 2007. ICASSP 2007. IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. I-861, 2007.
17. Do, Minh N., and Martin Vetterli. "The contourlet transform: an efficient directional multiresolution image representation." IEEE Trans. on Image Processing, vol. 14, no. 12, pp. 2091-2106, 2005.
18. Da Cunha, Arthur L., Jianping Zhou, and Minh N. Do. "The nonsubsampled contourlet transform: theory, design, and applications."  IEEE Trans. on Image Processing, vol. 15, no. 10, pp. 3089-3101, 2006.
19. Naidu, V. P. S., and Bindu Elias. "A Novel Image Fusion Technique using DCT based Laplacian Pyramid." International Journal of Inventive Engineering and Sciences (IJIES), pp. 2319-9598, 2013.
20. Shaohui, Renhua Zhang, Hongbo Su, et al. “SAR and multispectral image fusion using generalized IHS transform based on a trous wavelet and EMD decompositions.”IEEE Sensors Journal, vol. 10, no. 3, pp. 737-745, 2010.
21. Dong, Weihua, Xian'en Li, Xiangguo Lin, et al. “A Bidimensional Empirical Mode Decomposition Method for Fusion of Multispectral and Panchromatic Remote Sensing Images.” Remote Sensing, vol. 6, no. 9, pp. 8446-8467, 2014.
22. Ehsan, Shoaib, Syed Muhammad Umer Abdullah, et al. “Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition.”Sensors, vol. 15, no. 5, pp. 10923-10947, 2015.
23. Feldman, Michael. "Time-varying vibration decomposition and analysis based on the Hilbert transform." Journal of Sound and Vibration, vol. 295, no. 3, pp. 518-530, 2006.
24. Feldman, Michael. "Hilbert transform in vibration analysis." Mechanical Systems and Signal Processing, vol. 25, no. 3, pp. 735-802, 2011.
25. Sharma, H., and K. K. Sharma. "Baseline wander removal of ECG signals using Hilbert vibration decomposition." Electronics Letters, vol.51, no. 6, pp. 447-449, 2015.
26. Chen, Soong-Der, and Abd Rahman Ramli. "Minimum mean brightness error bi-histogram equalization in contrast enhancement."  IEEE Trans. on Consumer Electronics, vol. 49, no. 4,pp. 1310-1319, 2003.
27. Naik, Sarif Kumar, and C. A. Murthy. "Hue-preserving color image enhancement without gamut problem." IEEE Trans. on Image Processing, vol. 12, no. 12, pp. 1591-1598, 2003.
28. Haghighat, Mohammad Bagher Akbari, Ali Aghagolzadeh, and Hadi Seyedarabi. "A non-reference image fusion metric based on mutual information of image features." Computers & Electrical Engineering 37, vol. 5, pp. 744-756, 2011.
29. Xydeas, C. S., and V. Petrović. "Objective image fusion performance measure." Electronics Letters, vol. 36, no. 4, pp. 308-309, 2000.

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Abstract: For cloud computing systems, Virtual Machines (VM) were conceived as the basic component. However, VMs give effective computing resources; they were prone to lots of security threats. Whereas few threats can be easily rectified, but few attacks like co-resident attacks were tedious process to identify. So, to reduce the co-resistance DOS attacks by creating it as tedious for attackers to initiate attacks, two-player game approach based defense mechanism is suggested in our work. The attacker behavior variations among the attacker and normal users under PSSF VM allocation policy, is examined initially in the proposed mechanism. EDBSCAN (Enhanced Density-based Spatial Clustering of Applications with Noise), is utilized to do the clustering analysis process. Based on the clustering algorithm, the Partial labeling is performed, to partially comprehend the users as legal or malicious. In order to classify the nodes, the semi-supervised learning using Deterministic Annealing Semi-supervised SVM (DAS3VM) optimized by branch and bounds method is performed. The two-player security game approach helps to raise the cost of introduction new VMs therefore reducing the probability of initiating co-resident DOS attack, once after the user accounts were classified. Therefore, the security threats can be avoided effectively with the help of the proposed defense mechanism. Experimental result confirms that the suggested co-resident DOS attack defense mechanism makes a desirable involvement to avoid the security threats.

Keywords: Co-Resident DOS Attack, PSSF, EDBSCAN, DAS3VM, Branch and Bound Method. 

References: 

1. Armbrust, M., Fox, A., Griffith, R., Joseph, A., Katz, R., et al.: Above the Clouds: A Berkeley View of Cloud Computing. Technical Report UCB/EECS-2009-28, University of California, Berkeley (2009)
2. Amazon Elastic Compute Cloud (EC2). http://aws. amazon.com/ec2/
3. Ristenpart, T., Tromer, E., Shacham, H., Savage, S.: Hey, You, Get off of my cloud: exploring information leakage in third-party compute clouds. In: CCS’09: Proceedings of 16th ACM Conference on Computer and Communications Security, Chicago (2009)
4. Xu, Y., Bailey, M., Jahanian, F., Joshi, K., Hiltunen, M., Schlichting R.: An exploration of L2 cache covert channels in virtualized environments. In: Proceedings of 3rd ACM Workshop on Cloud Computing, Security (CCSW’11) (2011)
5. Zhang, Y., Juels, A., Oprea, A., Reiter, M.K.: HomeAlone: Co-Residency Detection in the Cloud via Side-Channel Analysis. In: Proceedings of 2011 IEEE Symposium on Security and Privacy, Berkeley (2011)
6. Keller, E., Szefer, J., Rexford, J., Lee, R.B.: Eliminating the hypervisor attack surface for a more secure cloud. In: Proceedings of ACM Conference on Computer and Communications, Security (CCS’11) (2011)
7. Raj, H., Nathuji, R., Singh, A., England, P.: Resource management for isolation enhanced cloud services. In: Proceedings of 2009 ACM Workshop on Cloud Computing Security, CCSW ’09, Chicago (2009)
8. Bier, V. M., & Azaiez, M. N. (Eds.). (2008). Game theoretic risk analysis of security threats(Vol. 128). Springer Science & Business Media.
9. Han, Y., Chan, J., Alpcan, T., & Leckie, C. (2014, June). Virtual machine allocation policies against co-resident attacks in cloud computing. In 2014 IEEE International Conference on Communications (ICC)(pp. 786-792). IEEE.
10. Han, Y., Alpcan, T., Chan, J., Leckie, C., & Rubinstein, B. I. (2016). A game theoretical approach to defend against co-resident attacks in cloud computing: Preventing co-residence using semi-supervised learning. IEEE Transactions on Information Forensics and Security, 11(3), 556-570.
11. Yinqian Zhang, Ari Juels, Alina Oprea (2011) “Home Alone: Co-Residency Detection in the Cloud via Side-Channel Analysis” 2011 IEEE Symposium on Security and Privacy.
12. Adam Bates, Benjamin Mood, Joe Pletcher, Hannah Pruse, Masoud Valafar (2010) “Detecting Co-Residency with Active Traffic Analysis Techniques”.
13. Han Y, Tansu Alpcan, Jeffrey Chan, Christopher Leckie, (2011) “Security Games for Virtual Machine Allocation in Cloud Computing”.
14. Yu, S.: Distributed Denial of Service Attack and Defense. Springer, 2014.
15. Lenon, M.: Cloudare infrastructure hit with 400gbs ntp-based ddos attack, 2014. http://www.securityweek.com/cloudflare-infrastructure-hit-400gbs-ntp-based-ddos-attack
16. Kumar, N., Sharma, S.: Study of intrusion detection system for ddos attacks in cloud computing. In: Wireless and Optical Communications Networks (WOCN), 2013 Tenth International Conference on, pp. 1{5. IEEE, 2013.
17. Ismail, M.N., Aborujilah, A., Musa, S., Shahzad, A.: Detecting ooding based dos attack in cloud computing environment using covariance matrix approach. In: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, p. 36. ACM, 2013.
18. Liu, H.: A new form of dos attack in a cloud and its avoidance mechanism. In: Proceedings of the 2010 ACM workshop on Cloud computing security workshop, pp. 65{76. ACM, 2010.
19. Bedi, H.S., Shiva, S.: Securing cloud infrastructure against co-resident dos attacks using game theoretic defense mechanisms. In: Proceedings of the International Conference on Advances in Computing, Communications and Informatics, pp. 463{469. ACM, 2012.
20. Zunnurhain, K.: Fapa: a model to prevent ooding attacks in clouds. In: Proceedings of the 50th Annual Southeast Regional Conference, pp. 395{396. ACM, 2012.
21. Ester, H.-P. Kriegel, J. Sander, and X. Xu, “A density-based algorithm for discovering clusters in large spatial databases with noise,” in Proc. 2nd Int. Conf. Knowl. Discovery Data Mining (KDD), 1996, pp. 226–231.
22. Phung, D., Adams, B., Tran, K., Venkatesh, S. and Kumar, M. (2009) High Accuracy Context Recovery using Clustering Mechanisms, In proceedings of the seventh international conference on Pervasive Computing and Communications, PerCom Galveston, USA, Pp. 122-130
23. Chapelle, O., Sindhwani, V., & Keerthi, S. S. (2006). Branch and bound for semi-supervised support vector machines. In Advances in neural information processing systems(pp. 217-224).
24. Azar, S. Kamara, I. Menache, M. Raykova, and B. Shepard, “Co-location-resistant clouds,” in Proc. 6th ACM Workshop Cloud Comput. Secur, 2014, pp. 9–20.

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