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Twitter sentiment analysis offers organizations an ability to monitor public feeling towards the products and events related to them in real time. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous tweets. It offers organizations a fast and more effective way to analyze customer’s perspectives towards the success in the market place. Sentiment analysis is an approach to be used to computationally measure customer’s perceptions to a vast extent. This is a survey on the design of a sentiment analysis. After extraction of a vast amount of tweets, it classifies perspectives of customers via tweets into positive and negative sentiments. Which is obtained after classifying the data by using classification approaches like for example Bayes Naïve, Linear Regression, etc.

Twitter, sentiment analysis, datasets, pre-processing, feature extraction, classification

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Eriq-Ur Rahman, Rituparna Sarma, Rajesh Sinha, Priyankar Sinha, Adarsh Pradhan, "A Survey on Twitter Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.644-648, 2018.

Increase in internet dependency in all walks of life, digital nature of data in huge amounts getting accumulated through online transactions and decentralization of data repositories, has led to the development of effective security mechanisms. While discussing the challenges of combating cybercrime, this paper provides a comprehensive overview of cyber security mechanisms, recent attack prediction techniques to create attack prediction models. This paper also explores recent trends in cyber-security like graph data analytics and security in wireless sensor networks. Emerging trends in security system design leveraging social behavioral biometrics, network security analytics, and contextual information to identify known as well as unknown cyber- attacks are also discussed. A framework for contextual information fusion to detect cyber-attacks is presented.

Contextual information, Attack similarity, Zero-day attack, vulnerability

[1] Oreku, George S., and Fredrick J. Mtenzi. "Cybercrime: Concerns, Challenges and Opportunities." Information Fusion for Cyber-Security Analytics. Springer, Cham, pp. 129-153, 2017.
[2] Namayanja, Josephine M., and Vandana P. Janeja. "Characterization of Evolving Networks for Cybersecurity." Information Fusion for Cyber-Security Analytics. Springer, Cham,. pp.111-127, 2017.
[3] Chakraborty, I, Das, P., “Data Fusion in Wireless Sensor Network-A Survey”, International Journal of Scientific Research in Network Security and Communication, 5(6), pp.9-15, 2017.
[4] Anchugam, C. V., and K. Thangadurai. "Security in Wireless Sensor Networks (WSNs) and Their Applications." Information Fusion for Cyber-Security Analytics. Springer, Cham, pp.195-228, 2017.
[5] Gavrilova, M. L., et al. "Emerging trends in security system design using the concept of social behavioural biometrics." Information Fusion for Cyber-Security Analytics. Springer, Cham, pp.229-251, 2017.
[6] Grahn, Kaj, Magnus Westerlund, and Göran Pulkkis. "Analytics for network security: A survey and taxonomy." Information fusion for cyber-security analytics. Springer, Cham, pp.175-193, 2017.
[7] AlEroud, Ahmed, and George Karabatis. "Using contextual information to identify cyber-attacks." Information Fusion for Cyber-Security Analytics. Springer, Cham, pp.1-16, 2017.
[8] Singh, U.K., Joshi, C, Singh, S.K., ”Zero day Attacks Defense Technique for Protecting System against Unknown Vulnerabilities”, International Journal of Scientific Research in Computer Science and Engineering, 5, pp.13-18.,2017.
[9] AlEroud, Ahmed, and George Karabatis. "A Framework for Contextual Information Fusion to Detect Cyber-Attacks." Information Fusion for Cyber-Security Analytics. Springer, Cham, pp.17-51, 2017.
[10] AlEroud, Ahmed, and George Karabatis. "Detecting Unknown Attacks Using Context Similarity." Information Fusion for Cyber-Security Analytics. Springer, Cham, pp.53-75, 2017.

Nerella Sameera, M. Shashi, "A Survey on Cyber Security Analytics," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.649-652, 2018.

This paper provides the critical reviews on Real time Video Compression and Efficient use of Fuzzy Logic Techniques used in Video Compression and Quality Enhancement. Since the Internet is highly heterogeneous environment video codec needs to be able to generate bit streams that are highly scalable in terms of bandwidth and processing requirements looking all these problems this research paper explores the possibility of better compression ratio in real time and quality enhancement by efficient use of fuzzy logic . The first section of this paper tells the overview of the real time video compression .The second section of this paper describes the related work which has been done in the past regarding real time video compression it consists a Table-1 in reference of the time line of real time video compression and Table -2 about the differences between H.265 and H.264 .The third section of this paper consists a Table-3 which represents about the research time line using fuzzy logic in video compression .The fourth section of this paper consists a Table-4 which represents the research time line of real time video compression. Finally the conclusion of this paper is an overview on past, present and future trends in Video Compression Technologies, review of the improvements and development in video encoding over the last two decades with future possibilities.

Real time, Video compression, Fuzzy logic, Motion vector estimation, Bit rate

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[5] Pei-Jun Lee and Ming-Long Lin, “ Fuzzy Logic Based Temporal Error Concealment for H.264 Video ”, ETRI Journal Vol. 28, Number 5, pp. 574-582, 2006.
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[9] Junhua Chai, Jun Ying and Li Li, “A Fuzzy Video Pre-filtering Method for Impulse Noise Reduction”, In the Proceedings of the IEEE Conferences 2009 International Conference on Test and Measurement , Hong Kong, China, pp. 176 - 183, 2009.
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[13] T. Fryza, “Introduction to Implementation of Real Time Video Compression Method”, In the Proceedings of the IEEE Conferences 2008 15th International Conference on Systems, Signals and Image Processing , Bratislava, Slovakia, pp. 217 - 220, 2008.
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Algorithm for Real-Time Video Processing”, In the Proceedings of the IEEE Conferences 2010 Electronics, Robotics and
Automotive Mechanics Conference ,Morelos, Mexico, pp. 405 - 410, 2010.
[15] Gary J. Sullivan, Jens-Rainer Ohm, Woo-Jin Han and Thomas Wiegand , “Overview of the High Efficiency Video Coding (HEVC) Standard” IEEE Transactions on Circuits and Systems for Video Technology Vol. 22, Issue: 12, pp. 1649 – 1668, 2012
[16] Stamos Katsigiannis, Dimitris Maroulis, Georgios , Papaioannou , “A GPU based real-time video compression method for video conferencing”, In the Proceedings of the IEEE Conferences 2013 18th International Conference on Digital Signal Processing (DSP) , Fira, Greece, pp. 1 - 6, 2013.
[17] Ali Makki Sagheer, Ahmeed Suliman Farhanand Loay E. George, “Fast Intra-frame Compression for Video Conferencing using Adaptive Shift Coding” International Journal of Computer Applications , Vol. 81, No.8, pp. 29 – 33, 2013
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[21] Huaying Xue, Yuan Zhang, Yunong Wei, “ Fast ROI-Based HEVC Coding for Surveillance Videos”, In the Proceedings of the
IEEE Conferences 2016 19th International Symposium on Wireless Personal Multimedia Communications (WPMC), Shenzhen, China, pp. 299 - 304, 2016.
[22] S. Aparna, M. Ekambaram Naidu, “Spatial Compression and Reconstruction of Digital Video Stream Using Morphological
Filters”, In the Proceedings of the IEEE Conferences 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) , Dehradun, India, pp. 777 - 781, 2016.
[23] Vivek Diliprao Indrale, Mrs. Vidya N. More, “Study of x265 and Genetic Motion Search Algorithm”, In the Proceedings of the
IEEE Conferences 2016 International Conference on Computing Communication Control and automation (ICCUBEA) , Pune, India, pp. 1 - 5, 2016.
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[25] Zhao Wang, Shiqi Wang, Jian Zhang, Shanshe Wang, Siwei Ma, “Effective Quadtree Plus Binary Tree Block Partition Decision for Future Video Coding”, In the Proceedings of the IEEE Conferences 2017 Data Compression Conference (DCC) , Snowbird, UT, USA, pp. 23 - 32, 2017.
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[27] Krishna Reddy Konda, Yonas Teodros Tefera, Nicola Conci, and Francesco G.B. De Natale, “Real-time moving object detection and segmentation in H.264 video streams”, In the Proceedings of the IEEE Conferences 2017 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) , Cagliari, Italy, pp. 1 - 6, 2017.
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Upendra Kumar Srivastava, Navin Prakash, "A Systematic Review on Real Time Video Compression and Enhancing Quality Using Fuzzy Logic," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.653-665, 2018.

An attack in computers systems and its networks may attempt to explore, disable, destroy, steal or gain unauthorized access to make illegal use of asset. World Economic Forum has observed that Scalable algorithms and Robustness of cyber-attacks have significant improvement in the technology. Recent technical reports stated that offensive cyber capabilities were developed more rapidly than our ability to deal with hostile incidents. A computer network attack disrupts the integrity or authenticity of data, usually through malicious code that alters program logic that controls data, leading to errors in output. Network security covers a wide variety of both public and private networks that are regularly utilized in jobs like, transactions and communications among businesses, government agencies and individuals. The review paper presents about the types of attacks with issues and challenges in MANET, WSN and VANET.

MANET, WSN, VANET, Security, Attacks, Vulnerabilities

[1] Mohan V. Pawar, Anuradha “Network Security and Types of Attacks in Network”, Procedia Computer Science 48, 503 – 506, 2015.
[2] NehaKhandelwal, Prabhakar.M. Kuldeep Sharma, “An Overview Of security Problems in MANET”, ISEM International Conference, 2011.
[3] Anupam Joshi and Wenjia Li. “Security Issues in Mobile Ad Hoc Networks- A Survey”, research gate.
[4] Ali Ghaffari, “Vulnerability and Security of Mobile Ad hoc Networks”, Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization, Lisbon, Portugal, 124-130, 2006.
[5] Monali S. Gaigole “The Study of Network Security with Its PenetratingAttacks and Possible Security Mechanisms”, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.5, PP: 728-735, 2015.
[6] SarveshTanwar, Prema K.V “Threats & Security Issues in Ad hoc network: A Survey Report”, International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, 2013.
[7] Satyam Shrivastava, “A Brief Introduction of Different type of Security Attacks found in Mobile Ad-hoc “,/ International Journal of Computer Science & Engineering Technology (IJCSET), Vol. 4 No. 218-222, 2013.
[8] DivyaChadha, “Vehicular Ad hoc Network (VANETs): A Review”
[9] Tyagi P, DemblaD,”Investigating the security threats in Vehicular ad hoc Networks (VANETs)”, proc. IEEE International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014.
[10] Dr.H.G.Chandrakanth, Rajkumar, Sunitha.K.R “A Survey on Security Attacks in Wireless Sensor Networks”, International Journal of Engineering Research and Applications, Vol. 2, Issue 4, 1684-1691, 2012.
[11] V.E.Ekong,“A Survey of Security Vulnerabilities in Wireless Sensor Networks”, Nigerian Journal of Technology (NIJOTECH) Vol. 35, No. 2, 392 – 397, 2016.
[12] Sarika SA, PravinAb, Vijayakumar AC, Selvamani KD, “Security Issues In Mobile Ad Hoc Networks”
[13] Yongguang Zhang and Wenke Lee, “Security in Mobile Ad-Hoc Networks”, Book Chapter.
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[15] PriyankaGoyal, VintiParmar, Rahul Rishi, “MANET: Vulnerabilities, Challenges, Attacks, Applications”, IJCEM International Journal of Computational Engineering & Management, Vol. 11, 32-38, 2011.
[16] Ram Shringar Raw, “Security Challenges, Issues and Their solutions For VANET”, International Journal of Network Security & Its Applications (IJNSA), Vol.5, No.5, 2013.

R. Yogapriya, A. Subramani, "A Survey on Vulnerabilities, Attacks and Issues in MANET, WSN and VANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.666-671, 2018.

As technological interconnection and digital communication schemes wide spreading, data accessing required to be kept in sharing environment and hence it will surely lead to compromise the data in many aspects. So to keep data secure and protected there are variety of techniques and tools also developed and Intrusion detection system (IDS) is one of them. IDS system conceptualized with identifying the intrusions in place of stopping the attacks. There are various techniques discussed here in context of signature and behavior based IDS. These IDS tools use different identification techniques to classify and identify the attacks and type of attacks. This paper includes different types IDS which has capability inclusion of identifying attacks like probe, DoS, R2L etc. It is also covering categorized descriptions of host based as well as network based hybrid intrusion detection systems.

Attacks, Classification, Communication, Detection, DoS, Intrusion, R2L, Signature etc

[1] Roshan Kumar, Deepak SharmaHyINT: Signature-Anomaly Intrusion Detection System 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2018.
[2] A survey on Intrusion Detection System (IDS) and Internal Intrusion Detection and protection system (IIDPS) IEEE 2017.
[3] Akash Garg ; Prachi Maheshwari,10th International Conference on Intelligent Systems and Control (ISCO), 2016.
[4] Chaimae Saadi ,Ensak-Morroco and Habiba Chaoui “Intrusion detection system based interaction on mobile agents and clust-density algorithm “IDS-AM-Clust”, International Colloquium on Information Science and Technology (CiSt),IEEE,2016.
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[10] Bin Zeng , Lu Yao and ZhiChen Chen"A network intrusion detection system with the snooping agents",International Conference on Computer Application and System Modeling, 2010.
[11] Vera Marinova-Boncheva, “A Short Survey of Intrusion Detection Systems”, Problems of Engineering Cybernetics and Robotics, 2007.
[12] Khaled Labib, Computer security and intrusion detection, Crossroads, Volume 11, Issue 1, August 2004.
[13] Murthy, S.K. , Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey. Data Mining and Knowledge Discovery, 24, 1998.
[14] Aurobindo Sundaram, An Introduction to Intrusion Detection, Crossroads, Volume 2, Issue 4, Pages: 3 –7, 1996.
[15] Northcutt, S. “Network Intrusion Detection: An Analyst’s Handbook.” New Riders, Indianapolis 1999.

K Shukla, R K Gupta, V. Namdeo, "An Overview Of Intrusion Detection System Using Various Classification Concepts," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.672-675, 2018.

In today’s world each individual wish that his private information is not revealed in some or the other way. Privacy preservation plays a vital role in preventing individual private data preserved from the praying eyes. Anonymization techniques enable publication of information which permit analysis and guarantee privacy of sensitive information in data against variety of attacks. The problem is that information loss and distortion are unavoidable by anonymization job. To reduce the distortion, this paper presents an efficient method that is based on deep anonymization detection. In the method, data publishers analyze the anonymization work, and determine if it is deep or light. If it is thought as deep anonymization, high information distortion is allowed when being distributed to a third party after anonymization. Otherwise, information distortion is kept as low as possible when anonymizing Big-Data to provide the receivers with more meaningful data. The decision for deep anonymization is done by considering a domain data characteristic, data receiver’s purpose, and data criticality. Anonymization approaches are used to develop to reduce information loss or increase privacy protection. It aimed to give comparative evolution of the various algorithms. These algorithms are compared for efficiency (in terms of time) and utility loss. We analysis that paillier encryption is more efficient than other algorithms

Privacy, Anonymization, encryption, Big Data

[1] Latanya Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5):557570, 2002
[2] Turban and J.E. Aronaon. Decision support Systems and Intelligent Systems, Prentice-Hall, New Jersey, USA, 2001
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[9] M. Jensen, “Challenges of Privacy Protection in Big Data Analytics,” IEEE Int’l Congress on Big Data (Big Data Congress), 2013; doi.org/10.1109/BigData.Congress.2013.39.
[10] ISO/IEC 27040, Information Technology – Security Techniques – Storage, standard ISO/IEC 27040, Int’l Organization for Standardization, 2015; www.iso.org/standard/44404.html.
[11] 2016 Data Breach Investigations Report, report, Verizon, 2016; www.verizonenterprise.com/resources/reports/rp_DBIR_2016_Report_en_xg.pdf.
[12] Latanya Sweeney. k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-Based Systems, 10(5):557570, 2002
[13] Turban and J.E. Aronaon. Decision support Systems and Intelligent Systems, Prentice-Hall, New Jersey, USA, 2001
[14] P. P. de Wolf, J.M.Gouweleeuw, P. Kooiman, L. Wil-lenborg, Reflections on PRAM. Statistical data protection, proceedings of the conference, Lisbon, 1998.
[15] R. J. Bayardo and R. Agrawal. Data Privacy through Optimal k- Anonymization. In Proc. of ICDE-2005, 2005 [13] Stephen Lee Hansen and Sumitra Mukherjee. A Polynomial Algorithm for Optimal Microaggregation
[16] Matthias Schmid1 and Hans Schneeweiss, 2005, The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study
[17] X. Zhang, C. Liu, S. Nepal, C. Yang, J. Chen, ”Privacy Preservation over Big Data in Cloud Systems,” Security, Privacy and Trust in Cloud Systems, pp 239-257, Springer.
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Vidhi Desai, "Privacy Preserving Big Data Usings Combine Anonymous And Encryption Approach-Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.676-680, 2018.

Nowadays, the security associated with information over the web has turned into a significant issue. With the aim to take care of the issue, two fundamental procedures are utilized cryptography and Steganography. The two strategies are utilized for information security reason. Cryptography changes the type of the information and Steganography totally hides its core from the clients, aside from the proposed recipient. The steganography implies secured script while cryptography implies mystery composing. In this paper, a strategy is defined which joins these two techniques to give a more productive and successful outcome. In this paper, different steganographic methods have been analyzed. These methods include text hiding, audio hiding, file hiding and image hiding. This paper reviews and analyzes many existing digital image steganographic methods in both the spatial and transform domains.

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H.A. Patil, P. Saxena, "Analysis and Review of the Steganographic Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.681-685, 2018.

Tag mining is important as far as image search engines/databases are concerned viz. Flicker, Picasa, Facebook...etc. Tag Mining is a difficult and highly relevant machine task. In this paper, we present a new approach to hybrid features based object mining and tagging that identifies the objects with higher accuracy from an occluded image. In existing system tag Mining with algorithms based on ‘Nearest neighbor classification’ have achieved considerable attention implementation point of view but at the cost of increasing computational complexity both during training and testing. It is very difficult to identify the object which is occluded in image. The objective of object tagging of image is to search over user contributed photo online which have accumulated rich human knowledge and billions of photos, then associate surrounding tags from those visually similar photos for the unlabeled image. For an unlabeled image, photos in the social media are extracted by the Feature based object tagging of image, the annotations associated with the images are expanded, and then each object group is classify. In this paper different features and classifier are compare with advantages and disadvantages.

Image processing, Object recognition, object mining, object tagging, feature extraction, classification, SVM

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Hemali Patel, Milin M Patel, "A Review On Hybrid Feature Based Object Mining And Tagging," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.686-689, 2018.

Mutation Testing is a fault based software testing technique, was proposed in the 1970’s, it has been considered as an effective technique of software testing process for evaluating the quality of the test data. In other words, Mutation Testing is used to evaluate the fault detection capability of the test data by inserting errors into the original program to generate mutations, and after then check whether tests are good enough to detect them. A lot of solutions have been proposed to solve that problem. A new form of Mutation Testing is Higher Order Mutation Testing, was first proposed by Harman and Jia in 2009 and is one of the most promising solutions. In this paper, we consider the main limitations of Mutation Testing and previous proposed solutions to solve that problems. This paper also refers to the development of Higher Order Mutation Testing and reviews the methods for finding the good Higher Order Mutant.

FOM, HOM, SHOM, GI.

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Shivani Chauhan, Raghav Mehra, "Higher Order Mutation-based Framework for Genetic Improvement (GI)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.690-694, 2018.

Online advertising is a huge, rapidly growing advertising market in today`s world. One common form of online advertising is using image ads. A decision is made (often in real time) every time a user sees an ad, and the advertiser is eager to determine the best ad to display. Consequently, many algorithms have been developed that calculate the optimal ad to show to the current user at the present time. Typically, these algorithms focus on variations of the ad, optimizing among different properties such as background color, image size, or set of images but none of them define the property of objects. Our study looks at new qualities of ads that can be determined before an ad is shown (rather than online optimization) and defines which ad image’s objects are most likely to be successful. We present a set of algorithms that utilize machine learning to investigate online advertising and to construct object detection models which can foresee objects that are likely to be in successive ad image. The focus of results is to get high success rate in ad image with objects appear in it. In this paper we are finding the best classifier among the all.

Object Detection, Machine Learning, And Prediction Model

[1] Michael Fire, Jonathan Schler. “Exploring Online Ad Images Using a Deep Convolutional Neural Network Approach” 2017.
[2] Jinsu Lee, Junseong Bang, and Seong Yang. “Object Detection With Sliding Window in Images Including Multiple Similar Objects”.
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Krushil Bhadani, Bijal Talati, "A Review On Exploring Online Ad Images Using A Clustering Approach," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.695-698, 2018.