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Software furnishes administrations that may accompany a few vulnerabilities or risks. Attackers perform activities that break the security of framework through dangers and cause disappointment. To dodge security helplessness, there are numerous security- explicit ideas that ought to be resolved as prerequisites amid software improvement life cycle so as to convey solid and secure software. This paper first, studies various existing procedures, systems required for creating secure software dependent on the related distributed works. It begins by displaying the most important Secure Software Development Lifecycle, a correlation within the primary security highlights for each procedure is proposed. The consequences of the examination will give the software engineer with a rule which will help in choosing the best secured process. Second, the paper lists a lot of the most broadly utilized determination dialects with the points of interest and impediments for each

Software, Security, Security evaluation, vulnerabilities, secure architecture etc

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[14] I. Flechais, M.A. Sasse, and S.M.V. Hales, “Bringing Security Home: A Process for Developing Secure and Usable Systems,” In Proc. of the New Security Paradigms Workshop (NSPW’07), Ascona, Switzerland, ACM Press, 2003, pp. 49- 57.
[15] J. Gregoire, K. Buyens, B. De Win, R. Scandariato, and W. Joosen, “On the Secure Software Development Process: CLASP and SDL Compared,” In Proc. of the 3rd International Workshop on Software Engineering for Secure Systems (SESS’07), Minneapolis, Minnesota, USA, IEEE CS Press, 2007, pp. 1-1.
[16] M. Graves and M. Zulkernine, “Bridging the Gap: Software Specification Meets Intrusion Detector,” In Proc. of the 4th Annual Conference on Privacy, Security and Trust (PST’06), Ontario, Canada, pp. 265-274.
[17] Jameel Ahmad Qurashi, Sanjay Kumar "Secure Software Architecture: A Hybrid Approach Based On Non-Functional Security Requirements." International Journal of Computer Sciences and Engineering 7.1 (2019): 790-794.
[18] Anooja A, Jameel Ahmad Qurashi, Sanjay Kumar "A Survey Study of Various Software Cost Effort Estimation in Perspective of India." International Journal of Computer Sciences and Engineering 7.1 (2019): 928-933.

Jameel Ahmad Qurashi, Harvir Singh, Vijay Nunia, "Secure Software Architecture and Design: Security Evaluation for Hybrid Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.1-6, 2019.

OpenNebula, a cloud platform handles a variety of leases employing scheduler, Haizea and majority of them are deadline-sensitive in real time. As existing Backfilling AHP model for deadline-sensitive lease scheduling suffers from lease rejection and do not scrutinize the estimations for waiting leases. In our proposed work, to overcome this pitfall we have devised Hungarian-Genetic Algorithm (HGA). Time Estimations for leases are performed using optimized Hungarian Algorithm to optimally render resources to available leases but it executes boundlessly. Thus, it’s blended with Genetic Algorithm to set bounds to it by utilizing fitness function. Output of HGA is a scheduling structure with optimal lease combination which consumes minimum time. Finally HGA is compared with Backfilling AHP model and HGA schedules greater quota of leases and minimizes lease ostracism comparatively. Also proposed model works fine on increasing number of leases as computational time is not directly proportional to number of leases scheduled.

Deadline sensitive, Resource allocation, Leases, Lease scheduling, Cloud computing

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[3] Arunarani, A. R., D. Manjula, and VijayanSugumaran. "Task scheduling techniques in cloud computing: A literature survey." Future Generation Computer Systems 91 (2019): 407-415.
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[8] Sotomayor, Borja, et al. "Virtual infrastructure management in private and hybrid clouds." IEEE Internet computing 13.5 (2009): 14-22.
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[12] Toosi, Adel Nadjaran, Richard O. Sinnott, and RajkumarBuyya. "Resource provisioning for data-intensive applications with deadline constraints on hybrid clouds using Aneka." Future Generation Computer Systems 79 (2018): 765-775.
[13] Vecchiola, Christian, et al. "Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka." Future Generation Computer Systems 28.1 (2012): 58-65.
[14] Xu, Xiangqiang, and Xinghui Zhao. "A framework for privacy-aware computing on hybrid clouds with mixed-sensitivity data." 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems. IEEE, 2015.
[15] Nayak, SuvenduChandan, and ChitaranjanTripathy. "Deadline sensitive lease scheduling in cloud computing environment using AHP." Journal of King Saud University-Computer and Information Sciences 30.2 (2018): 152-163.
[16] Zhao, Zhuo, Ying Jiang, and Xin Zhao. "SLA_oriented service selection in cloud environment: a PROMETHEE_based Approach." Computer Science and Network Technology (ICCSNT), 2015 4th International Conference on. Vol. 1. IEEE, 2015.
[17] Kaur, Kulbir, and Harshpreet Singh. "PROMETHEE based component evaluation and selection for Component Based Software Engineering." 2014 IEEE Int. Conf. on Advanced Communications, Control and Computing Technologies. IEEE, 2014.
[18] Brans, Jean-Pierre, PhVincke, and Bertrand Mareschal. "How to select and how to rank projects: The PROMETHEE method." European journal of operational research 24.2 (1986): 228-238.
[19] Ali, Hend Gamal El Din Hassan, Imane Aly Saroit, and Amira Mohamed Kotb. "Grouped tasks scheduling algorithm based on QoS in cloud computing network." Egyptian informatics journal 18.1 (2017): 11-19.
[20] Panda, Sanjaya Kumar, ShradhaSurachita Nanda, and Sourav Kumar Bhoi. "A pair-based task scheduling algorithm for cloud computing environment." Journal of King Saud University-Computer and Information Sciences (2018).
[21] Haidri, Raza Abbas, ChittaranjanPadmanabhKatti, and Prem Chandra Saxena. "Cost effective deadline aware scheduling strategy for workflow applications on virtual machines in cloud computing." Journal of King Saud University-Computer and Information Sciences (2017).
[22] Rodriguez, Maria Alejandra, and RajkumarBuyya. "Deadline based resource provisioningand scheduling algorithm for scientific workflows on clouds." IEEE transactions on cloud computing 2.2 (2014): 222-235.
[23] H.W. Kuhn , The Hungarian method for the assignment problem, Nav. Res. Logist. Q. 2 (1-2) (1955) 83–97 .
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Duraksha Ali, Manoj Kumar Gupta, "Deadline Sensitive Lease Scheduling Using Hungarian Genetic Algorithm in Cloud Computing Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.7-15, 2019.

Security is one of the major issues in the current scenario. Because of the wireless nature of the nodes present in Wireless Sensor Networks, there is a chance of the nodes getting easily affected to severe security attacks. One such attack is a Selective Forwarding Attack in which the malicious nodes gain access to the wireless network and interrupts the data communication, overwrites the data packets, drops the packets and degrades the wireless network performance. In this paper, an effective cryptographic protocol using Authentication technique is proposed. To separate the attacking nodes in participating in the future networking activities, a Certificate Revocation Method is also proposed. This paper guarantees security to the nodes and do not allow access to any of the affected nodes by using a more efficient Authentication method. It also improves the performance of a network. Through simulation, the correctness and efficiency of the scheme is verified.

Authentication, Certificates, Cluster, malicious node, message digest, Revocation, Wireless Sensor Network

[1] Aggarwal, S., Goyal Astt Professor, N., & Aggarwal Astt Professor MRCE, K. (2014). “A review of Comparative Study of MD5 and SHA Security Algorithm”. International Journal of Computer Applications.
[2] Wei Liu, Student Member, IEEE, Hiroki Nishiyama, Member, IEEE, Nirwan Ansari, Fellow, IEEE, Jie Yang, and Nei Kato, Senior Member, IEEE “Cluster-Based Certificate Revocation with Vindication Capability for Mobile Ad Hoc Networks”.
[3] Youtao Zhang, Jun Yang, Weijia Li, Linzhang Wang, Lingling Jin: “An authentication scheme for locating compromised sensor nodes in WSNs”, Journal of Network and Computer Applications, vol.33, pp.50-62, 2010.
[4] Sungwook Kim, “Effective Certificate Revocation Scheme based on Weighted Voting Game Approach”, IET Information Security, Vol. 10, No. 4, pp. 180-187, 2016.
[5] K. Park, H. Nishiyama, N. Ansari, And N. Kato, “Certificate Revocation To Cope With False Accusations In Mobile Ad Hoc Networks”, In Proc. 2010 Ieee 71st Vehicular Technology Conference: Vtc2010-Spring, Taipei, Taiwan, May 16-19, 2010.
[6] Madhumita Panda, “Security in Wireless Sensor Networks using Cryptographic Techniques”, American Journal of Engineering Research (AJER) 2014 Volume-03, Issue-01, pp-50-56.
[7] Priti Rathi, Parikshit Mahalle, “Certificate Revocation in Mobile Ad Hoc Networks,” International Journal of Application or Innovation in Engineering & Management, Volume 2, Issue 1, January 2013.
[8] Khawla Naji Shnaikat and Ayman Ahmed AlQudah, “Key Management Techniques in Wireless Sensor Networks”, International Journal of Network Security & Its Applications (IJNSA) Vol.6, No.6, November 2014.
[9] Claude Crêpeau and Carlton R. Davis,” A Certificate Revocation Scheme for Wireless Ad Hoc Networks“ School of Computer Science, McGill University, Montreal, QC, Canada H3A 2A7.
[10] E.K. Neena and C. Balakrishnan,”Cluster Based Certificate Revocation of Attacker’s Nodes in MANET”, International Journal of Computer Science and Engineering(IJCSE), Vol 2, Issue 1, January 2014

T. C. Swetha Priya, A. Kanaka Durga, "Malicious Node Detection in Wireless Sensor Networks using Cryptographic Authentication and Certificate Revocation Mechanism," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.16-20, 2019.

Steganography as being a very useful technique for content hiding is the first choice of criminals, terrorists, and hackers. The steganalysis itself is very complex, and lots of research work is going on all around the world on steganography and steganalysis. However, when the steganography hides exploit instead of simple messages, it becomes more severe and damaging. Stegosploit is a similar toolkit that allows hackers to inject exploits for known vulnerabilities into images. These images, when accessed or downloaded can infect a machine very effectively compared to other ways of doing it. This paper emphasis on a technique that detects such stego images having an exploit inside it. We developed a script that detects this type of image, which is in-general not identified by known anti-viruses including virus total. The study also focuses on the effectiveness of the script for the Windows operating system and Linux Subsystem on Windows. The script derived from this research will help end-users, security professionals, forensic investigators, and researchers in detecting and thus preventing possible cybercrimes.

Steganography, Steganalysis, Stegosploit, Exploit Detection, Image Steganography, Image Exploits, Polyglots

[1] Cox, I., Miller, M., Bloom, J., Fridrich, J., & Kalker, T. (2007). Digital watermarking and steganography. Morgan Kaufmann.
[2] Dumitrescu, D., Stan, I.-M., & Simion, E. (2017). Steganography Techniques.
[3] Cheddad, A., Condell, J., Curran, K., & Mc Kevitt, P. (2010). Digital image steganography: Survey and analysis of current methods. Signal processing, 90(3), 727-752.
[4] Johnson, N. F., & Jajodia, S. (1998). Exploring steganography: Seeing the unseen. Computer, 31(2).
[5] Wu, H. C., Wu, N. I., Tsai, C. S., & Hwang, M. S. (2005). Image steganographic scheme based on pixel-value differencing and LSB replacement methods. IEE Proceedings-Vision, Image and Signal Processing, 152(5), 611-615.
[6] Ingemar, J. C., Miller, M. L., Jeffrey, A. B., Fridrich, J., & Kalker, T. (2008). Digital Watermarking and Steganography. Digital Watermarking and Steganography. Elsevier Inc.
[7] Yang, C.-N., Lin, C.-C., & Chang, C.-C. (2013). Steganography and watermarking. Steganography and Watermarking.
[8] Gupta, S., Goyal, A., & Bhushan, B. (2012). Information hiding using least significant bit steganography and cryptography. International Journal of Modern Education and Computer Science, 4(6), 27.
[9] Song, S., Zhang, J., Liao, X., Du, J., & Wen, Q. (2011). A novel secure communication protocol combining steganography and cryptography. Procedia Engineering, 15, 2767-2772.
[10] Abikoye, O. C., Adewole, K. S., & Oladipupo, A. J. (2012). Efficient data hiding system using cryptography and steganography.
[11] Shah S. (2015), Pastor Manul Laphroaig’s, Export–Controlled, Church Newsletter
[12] Vaniea, K., & Rashidi, Y. (2016, May). Tales of software updates: The process of updating software. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 3215-3226). ACM.
[13] Park, B., Kim, D., & Shin, D. (2015). A Study on a Method Protecting a Secure Network against a Hidden Malicious Code in the Image. Indian Journal of Science and Technology, 8(26).
[14] Jeyasekar, A., Bisht, D., & Dua, A. (2016). Analysis of Exploit Delivery Technique using Steganography. Indian Journal of Science and Technology, 9(39).
[15] Dudheria, R. Attacking Smartphones by Sharing Innocuous Images via QR Codes.
[16] Harblson, C. (2015). Hacking with pictures; new stegosploit tool hides malware inside internet images for instant drive-by pwning.
[17] Pevný, T., Kopp, M., Křoustek, J., & Ker, A. D. (2016). Malicons: Detecting Payload in Favicons. Electronic Imaging, 2016(8), 1-9.
[18] Fridrich, J. (2006). Steganalysis. In Multimedia Security Technologies for Digital Rights Management (pp. 349–381). Elsevier Inc.
[19] Schaathun, H. G. (2012). Histogram Analysis. In Machine Learning in Image Steganalysis (p. 82230).
[20] Provos, N. H. G. K. (2003). Statistical Steganalysis. ProQuest Information and Learning Company, 78–80.
[21] Huang, F., Li, B., Shi, Y. Q., Huang, J., & Xuan, G. (2010). Image steganalysis. Studies in Computational Intelligence, 282, 275–303.
[22] Al-Jarrah, M. M., Al-Taie, Z. H., & Abuarqoub, A. (2017). Steganalysis Using LSB-Focused Statistical Features. In Proceedings of the International Conference on Future Networks and Distributed Systems - ICFNDS ’17 (pp. 1–5). New York, New York, USA: ACM Press
[23] Harshal V. Patil1, B. H. Barhate2, "A Review Paper on Data Hiding Techniques: Stegnography", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.64-67, 2018
[24] Manisha Verma, Hardeep Singh Saini, "Analysis of Various Techniques for Audio Steganography in Data Security", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.1-5, 2019

N. Vaidya, P. Rughani, "An Efficient Technique to Detect Stegosploit Generated Images on Windows and Linux Subsystem on Windows," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.21-26, 2019.

The role of wireless sensor networks is considered to be evolving ubiquitous in the present day life due to its suitability and applicability in surveillance, weather forecasting and implantable sensors used for the purpose of health monitoring and other diversified number of applications. The use of tiny sensor nodes in WSN results in the crucial issues of restricted energy, limited energy and computation time. In this context, the network lifetime expectancy purely depends on the efficient and effective utilization of available resources in the network. However, the organization of sensor nodes into clusters is essential for the potential management of each and every cluster as well as the complete network. In this paper, Analytic Network Process-based Cluster Head Selection Mechanism (ANP-CHSM) is proposed for the objective of the cluster head selection with the view to enhance the network expectancy. This proposed ANP-CHSM considered the parameters that are associated with Residual Energy of Sensor Nodes (RESN), Distance between Nodes (DBN), merged node, Frequency Count in Cluster Head Role (FC-CHR) and Centroid Distance of Sensor Nodes (DSN) for modelling the process of cluster head selection. This proposed ANP-CHSM scheme aided in the optimal cluster head selection process by tackling the aforementioned parameters that attribute towards multi criteria decision making processes. The simulation results of the proposed ANP-CHSM was also considered to be significant over the compared cluster head selection frameworks contributed for effective clustering-based lifetime improvement processes

Analytic Network Process (ANP); Cluster head selection;network lifetime expectancy; Consistency Measure; Eigen Value

[1]J. Leu, T. Chiang, M. Yu and K. Su, "Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor Network With Isolated Nodes", in IEEE Communications Letters, vol.19, no.2, pp. 259-262, Feb. 2015.
[2]J. Lee and T. Kao, "An Improved Three-Layer Low-Energy Adaptive Clustering Hierarchy for Wireless Sensor Networks", in IEEE Internet of Things Journal, vol. 3, no. 6, pp. 951-958, Dec. 2016.
[3]C. Wang, Y. Zhang, X. Wang and Z. Zhang, "Hybrid Multihop Partition-Based Clustering Routing Protocol for WSNs", in IEEE Sensors Letters, vol. 2, no.1, pp. 1-4, March. 2018.
[4]J. Lee and W. Cheng, "Fuzzy-Logic-Based Clustering Approach for Wireless Sensor Networks Using Energy Predication", in IEEE Sensors Journal, vol. 12, no. 9, pp. 2891-2897, Sept. 2012.
[5]S. Murugaanandam and V. Ganapathy, "Reliability-Based Cluster Head Selection Methodology Using Fuzzy Logic for Performance Improvement in WSNs", in IEEE Access, vol. 7, pp. 87357-87368, 2019.
[6]D. Jia, H. Zhu, S. Zou and P. Hu, "Dynamic Cluster Head Selection Method for Wireless Sensor Network", in IEEE Sensors Journal, vol. 16, no. 8, pp. 2746-2754, April 15, 2016.
[7] S. H. ang and T. Nguyen, "Distance Based Thresholds for Cluster Head Selection in Wireless Sensor Networks", in IEEE Communications Letters, vol.16, no.9, pp.1396-1399, September. 2012.
[8]Q. Ni, Q. Pan, H. Du, C. Cao and Y. Zhai,"A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization", in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 14, no. 1, pp. 76-84, Jan-Feb. 2017.
[9]B. Cheng, H. Yeh and P. Hsu, "Schedulability Analysis for Hard Network Lifetime Wireless Sensor Networks With High Energy First Clustering", in IEEE Transactions on Reliability, vol. 60, no. 3, pp. 675-688, Sept. 2011.
[10]W. Osamy, A. M. Khedr, A. Aziz and A. A. El-Sawy, "Cluster-Tree Routing Based Entropy Scheme for Data Gathering in Wireless Sensor Networks", in IEEE Access, vol. 6, pp. 77372-77387, 2018.
[11] Bhuyan, B., Sarma, H. K., Sarma, N., Kar, A., & Mall. R, “Quality of Service (QoS) Provisions in Wireless Sensor Networks and Related Challenges in Wireless Sensor Network”, vol. 02, no. 11, pp. 861-868, 2010.
[12]Deva Sarma, H. K., Mall, R., & Kar, A, “E2R2: Energy-Efficient and Reliable Routing for Mobile Wireless Sensor Networks”, IEEE Systems Journal, vol.10, no.2, pp. 604-616, 2016.
[13]Sarma, H. K., Kar, A., & Mall, R, “A Hierarchical and Role Based Secure Routing Protocol for Mobile Wireless Sensor Networks”, Wireless Personal Communications, vol.90, no.3, pp. 1067-1103, 2016.
[14]Thippeswamy, B. M., Reshma, S., Tejaswi, V., Shaila, K., Venugopal, K. R., & Patnaik, L. M, “STEAR: Secure Trust-Aware Energy-Efficient Adaptive Routing in Wireless Sensor Networks”, Journal of Advances in Computer Networks, vol. 3, no.2, pp. 146-149, 2015.
[15]Rehman, E., Sher, M., Naqvi, S. H., Badar Khan, K., & Ullah, K, “Energy Efficient Secure Trust Based Clustering Algorithm for Mobile Wireless Sensor Network”, Journal of Computer Networks and Communications, vol. 1, page 1-8, 2017.
[16]Kumar, N., Singh, Y., & Singh, P. K, “An Energy Efficient Trust Aware Opportunistic Routing Protocol for Wireless Sensor Network”, International Journal of Information System Modeling and Design, vol. 8, no. 2, page. 30-44, 2017.
[17]Miglani, A., Bhatia, T., Sharma, G., & Shrivastava, G, “An Energy Efficient and Trust Aware Framework for Secure Routing in LEACH for Wireless Sensor Networks”, Scalable Computing: Practice and Experience, vol. 18, no. 3, page. 67-76, 2017.
[18]Bozorgi, S. M., & Bidgoli, A. M, HEEC: a hybrid unequal energy efficient clustering for wireless sensor networks”, Wireless Networks, vol. 1, no. 2, pp. 56-69, 2018.
[19]Udhayavani, M., & Chandrasekaran, M, “Design of TAREEN (trust aware routing with energy efficient network) and enactment of TARF: a trust-aware routing framework for wireless sensor networks”, Cluster Computing, vol. 1(1), pp. 45-59, 2018.
[20] A. Garg, N. Batra, I. Taneja, A. Bhatnagar, A. Yadav, S. Kumar, "Cluster Formation based Comparison of Genetic Algorithm and Particle swarm Optimization Algorithm in Wireless Sensor Network", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.14-20, 2017
[21] Poonam M. Mahajan, "WSN: Infrastructure and Applications", International Journal of Scientific Research in Network Security and Communication, Vol.06, Issue.01, pp.6-10, 2018
[22]Selvi, M., Thangaramya, K., Ganapathy, S., Kulothungan, K., Khannah Nehemiah, H., & Kannan, A, “An Energy Aware Trust Based Secure Routing Algorithm for Effective Communication in Wireless Sensor Networks”, Wireless Personal Communications, vol.103, no. 4,pp. 1475-1490, 2019.

A. Amuthan, A. Arulmurugan, "Analytic Network Process-Based Cluster Head Selection Mechanism for Extending the Network Lifetime," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.27-34, 2019.

As it is an important and challenging problem in computer vision, face age estimation is typically cast as a classification or regression problem over a set of face samples with several ordinal age labels which have intrinsically cross-age correlations across adjacent age dimensions. As an outcome, these such correlations normally lead to age label ambiguities of face samples. Each face sample is associated with a latent label distribution that encodes the cross-age correlation information on label ambiguities. As we propose a totally data-driven distribution learning, approach to adaptively learn the latent label distributions. The proposed approach is capable of effectively discovering the intrinsic age distribution patterns for cross-age correlation analysis on the any prior assumptions on the forms of label distribution learning, this approach is able to flexible model of sample-specific context aware label distribution properties by solving a multi-task problem which jointly optimizes the tasks of age-label distribution learning and age prediction for individuals. Experimental outcomes demonstrate effectiveness of our approach.

Age estimation, subspace learning, label distribution learning

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[9] C.G.Li and R.Vidal, “Structured sparse subspace clustering a unified optimization framework” in Proc.IEEE Conf. computer vis. Pattern June 2015.

Rohini G. Bhaisare, S.S. Ponde, "Age Estimation Using Fixed Rank Representation (FRR)," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.35-40, 2019.

Digital image forensic is a part of multimedia security with the objective to expose the image forgery in digital images. Among different types of image forgeries available, copy–move forgery is the most popular and common forgery. In Copy-move forgery one part of the original digital image is copied and pasted at any other position in the same image. Several methods have been developed to detect the image forgery in digital images. This paper is focusing on pixel-based copy–move image forgery detection methods to detect forgery which later on includes the trending algorithms of Key point based techniques and Block based techniques. Various techniques have been mentioned in the paper from the literature which was used by different authors for feature extraction and forgery detection. Comparative study of key point and block based image forgery detection algorithms is also stated.

Image Forgery, Block based, Key point based

[1] A. Kashyap, R. S. Parmar, M. Agarwal, and H. Gupta, “An evaluation of digital image forgery detection approaches,” Int. J. Appl. Eng. Res., vol. 12, no. 15, pp. 4747–4758, 2017.
[2] M. Ismail and N. Kanwal, “a Review Block Based Copy Move Forgery Detection Techniques,” Int. J. Comput. Sci. Mob. Comput., vol. 7, no. 4, pp. 205–212, 2018.
[3] X. Cun and C. M. Pun, “Image splicing localization via semi-global network and fully connected conditional random fields,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11130 LNCS, pp. 252–266, 2019.
[4] V. Christlein, C. Riess, J. Jordan, C. Riess, and E. Angelopoulou, “An evaluation of popular copy-move forgery detection approaches,” IEEE Trans. Inf. Forensics Secur., vol. 7, no. 6, pp. 1841–1854, 2012.
[5] M. hak and T. Gulati, “Detection of Digital Forgery Image using Different Techniques,” Int. J. Eng. Trends Technol., vol. 46, no. 8, pp. 457–461, 2017.
[6] S. Mushtaq and A. H. Mir, “Image Copy Move Forgery Detection: A Review,” Int. J. Futur. Gener. Commun. Netw., vol. 11, no. 2, pp. 11–22, 2018.
[7] S. Walia and K. Kumar, “Digital image forgery detection: a systematic scrutiny,” Aust. J. Forensic Sci., vol. 51, no. 5, pp. 488–526, 2019.
[8] S. Sadeghi, S. Dadkhah, H. A. Jalab, G. Mazzola, and D. Uliyan, “State of the art in passive digital image forgery detection: copy-move image forgery,” Pattern Anal. Appl., vol. 21, no. 2, pp. 291–306, 2018.
[9] E. Isha and E. V. Goyal, “A literature review of Image Forgery Detection,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 4, no. IX, pp. 75–80, 2016.
[10] T. Dalal, “Survey of Image Forgery Detection Technique Based on Color Illumination Using Machine Learning Approach,” Int. J. Adv. Res. Ideas Innov. Technol., vol. 2, no. 3, pp. 1–7, 2016.
[11] Z. Zhang, C. Wang, and X. Zhou, “A survey on passive image copy-move forgery detection,” J. Inf. Process. Syst., vol. 14, no. 1, pp. 6–31, 2018.
[12] S. Panda and M. Mishra, “Passive techniques of digital image forgery detection: Developments and challenges,” Lect. Notes Electr. Eng., vol. 443, pp. 281–290, 2018.
[13] A. Sahay and A. Gautam, “Comparison between SIFT and SURF image forgery Algorithms,” Int. J. Comput. Appl., vol. 164, no. 2, pp. 9–11, 2017.

Kavita Rathi, "Latest Trends in Image Forgery Detection," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.41-45, 2019.

Advancement in technology plays a vital role in adherence of democratic processes. While making technology encroachment, democratic nation smoothens to the process of e-voting for civilians. Compromising security is an adverse effect in progression of online easy going processes. Trust and privacy are at risk especially in online vote storage. One way to protect stored data is to apply encryption with the condition that only recipient can decode those data. This technique can be carried out with online electronic voting system to prevent vote tampering from insider or outsider adversaries. This research has been carried out to achieve privacy preservation and increasing trust factor among voters. To achieve given objective various additive Homomorphic encryption algorithms are implemented and as a result proved that paillier’s Homomorphic encryption is the effective algorithm to be implemented to accomplish privacy on casted votes.

Homomorphic encryption, e-voting, paillier

[1] A. Saranyadevi, S. Anguraj, S. Senbhaga, “A Detailed Study on Homomorphic Encryption”, International Journal of Morden Trends in Engineering and Research, ISSN: 2349-9745
[2] B. Patel, D. Bhatti, “A Proposed Secured Framework for Cloud Based E-Voting” in the proceeding of International Conference on New Frontiers of Engineering, Science, Management and Humanities (ICNFESMH-2018), pp. 364-369, 2018.
[3] Goldwasser, S. & Micali, S. “Probabilistic Encryption and How to Play Mental Poker Keeping Secret All Partial Information.” 14th Annual ACM Symposium on Theory of Computing (STOC’82), pp. 365-377, 1982.
[4] Benaloh, J. , “Verifiable Secret-Ballot Elections. Doctoral Dissertation”, Department of Computer Science, Yale University, New Haven, Connecticut, USA., 1988
[5] Naccache, D. & Stern, J, “A New Public Key Cryptosystem Based on Higher Residues.” 5th ACM Conference on Computer and Communications Security (CCS’98), pp. 59-66, ACM Press, New York, NY, USA., 1998
[6] Maha TEBAA, Saïd EL HAJJI, Abdellatif EL GHAZI, “Homomorphic Encryption Applied to the Cloud Computing Security”, Proceedings of the World Congress on Engineering 2012 Vol I WCE 2012, July 4 - 6, 2012, London, U.K.
[7] Paillier, P, “Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In: Advances in Cryptology” – Proceedings of EUROCRYPT’99, Lecture Notes in Computer Science (LNCS), Vol 1592, Springer-Verlag, pp. 223-238. 1999
[8] Jaydeep Sen, “Homomorphic Encryption:Theory and Application,” NIT Odisha
[9] Michael O’Keeffe, “The Paillier Cryptosystem – A look Into The Cryptosystem And Its Potential Application”, http://www.tcnj.edu/~hagedorn/papers/CapstonePapers/OKeeffe/CapstoneOKeeffeCryptography.pdf.
[10] S. Sinde, S. Shukla, D.K.Chitre,”Secure E-voting Using Homomorphic Technology”, International Journal of Emerging Technology And Advanced Engineering, Volume 3, Issue 8, ISSN : 2250 – 2459

Bhumika Patel, Dharmendra Bhatti, "Homomorphic Encryption: Privacy Preserving Amicable E-voting System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.46-50, 2019.

The artificial intelligence technique such as Genetic algorithm plays a significant role for handling in many fields such as artificial intelligence, engineering, robotic, etc. This is the technique to evaluate the new populations from natural population and provide the best result generation to generation. This is applied in students’ quantitative data analysis to identify the most impact factor in their performance in their curriculum. The results will help the educational institutions to improve the quality of teaching after evaluating the marks achieved by the students’ in academic career. This student analysis model considers the quantitative factors such as compiler, automata, data structure and other departmental marks to find the most impacting factor using genetic algorithm.

students’ performance, quantitative factors, genetic algorithm, influencing parameter, student’s evaluation results

[1] P ramya, M, Mahesh kumar,”Student Performance Analysis using Educational Data Mining”, International Journal of Computer science and Information Security (IJCSIS), ISSN NO 1947-5500, vol-14, pp:69-76, 2016.
[2] Chew li sa, Emmy Dehlana Hossain, “Student Performance Analysis System (SPAS)”, JANUARY 2015.
[3] Saddam Khan, Sunny Gupta,”A Study of Data Mining Techniques and Genetic Algorithm in education sector”, International Journal of Computer Science and Mobile Computing,ISSN 2320-088X, vol 4, march, 2015, pg 681-683.
[4] Somnath mazi,”genetic algorithm with different crossover for new analysis model of students performance”international journal of advanced research and development, ISSN: 2455-4030, march-2018, volume:3,pp:67-72.

[5] A. Martin, V. Prasanna Venkatesan et al, “To find the most impact financial features for bankruptcy model using genetic algorithm”, International Conference on Advances in Engineering and Technology, (ICAET-2011), May 27-28, 2011.
[6] Ramjeet Singh Yadav, “Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach”, International Journal on Computer Science and Engineering (IJCSE), Vol. 3 No. 2 Feb 2011.
[7] O.K Chaudhari, P.G khot, “Soft computing model for academic performance of teachers using fuzzy lo logic”, British journal of applied science and technology, 2(2):213-226, 2012.
[8] V.O. Oladokun, “Predicting Students’ Academic Performance using Artificial Neural Network: A Case Study of an Engineering Course”, The Pacific Journal of Science and Technology, Volume9. Number 1. May-June 2008 (Springer).
[9] Osman Taylan, Bahattin Karagozog, “An adaptive neuro-fuzzy model for prediction of student’s academic performance computers & Industrial Engineering”, 57 (2009) 732-741.

[10] Emmanuel N. Ogor “Student Academic Performance Monitoring and Evaluation Using Data Mining Techniques” published on Fourth Congress of Electronics, Robotics and Automotive Mechanics, 2009

Abhishek katiyar, Anil pandey, "Implement of Students Result by Using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.51-56, 2019.

There are some things which humans cannot do but a machine can one of them is some locations where humans cannot go and live in that locations by using the machines we can search that particular area for some purpose. So our project stands to solve this kind of problems by using the machine called unmanned aerial vehicle (UAV) which is Drone in our project. By using Drone in our project we can do the object detection and tracking using Deep Learning technology which helps the humans in solving this kind of problems as well it can also be used by the traffic policemen to determine the vehicle number who breaks the traffic rules, crime etc, It can also be used by an army of the country in borders to find out the terrorists who have entered their country borders, It can also be used in the cities to supply medicines and other items during emergencies, It can also be used to detect mining areas, It can also be used in the situations where earthquakes, Tsunami and other natural calamities in this time it can be used to detect humans, cows and other living things to be saved. Our project solves these problems at a greater accuracy with optimized cost as possible

UAV, Drone, terrorists, army, police, earthquakes, tsunami

[1] Ali Rohan, Mohammed Rabah and Sung-ho–kim, "Convolutional Neural Network-Based Real-Time Object Detection and Tracking for Parrot AR Drone 2”.2019
[2] Mohammed Rabah, Ali Rohan, Muhammad Talha, Kang Hyun Nam and Sung Ho Kim, “Autonomous Vision-based Target Detection and Safe Landing for UAV”.2018
[3] Widodo Budiharto, Alexander Agung Santoso Gunawan, Jarot S. Suroso and Andry Chowanda, Aurello Patrik and Gaudi Utama, "Fast Object Detection for Quadcopter Drone using Deep Learning".2018
[4] SiyiLi, Dit-YanYeung, “Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models”.2017
[5] It Nun Thiang, Dr.LuMaw, Hla Myo Tun, “Vision-Based Object Tracking Algorithm With AR. Drone”.2016
[6] Jangwon Lee, Jingya Wang, David Crandall, SelmaˇSabanovi´c and Geoffrey Fox, “Real-Time Object Detection for Unmanned Aerial Vehicles based on Cloud-based Convolutional Neural Networks”.2016
[7] Eleftherios Lygouras, Nicholas Santavas and others, “Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations”.2019
[8] Wang Chao, “Vision-based Autonomous Control and Navigation of a UAV”.2014
[9] Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection".2015
[10] Jiangjian Xiao, Changjiang Yang, Feng Han, and Hui Cheng, “Vehicle and Person Tracking in UAV Videos”.2006
[11] Tu Le, Ehsan Aryafar, “Real-time object detection and tracking on drones”.2018
[12] Min-Hyuck Lee and Seokwon Yeom, “Detection and Tracking of Multiple Moving Vehicles with a UAV”.2018

Ashwin Kumar K, Likith J, Nagendra Prasad, C Manasa, "Deep Learning Approach to Detect Objects Using Drone Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.12, pp.57-61, 2019.