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• Open Access   Article

  Low Power Distributed MAC Protocol Against Various Kinds Of Attacks By Using Traffic Analysis Methodology

  T. Dheepak, S. Neduncheliyan

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.1-7, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.17

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  The mobile Ad-hoc network (MANET) lacks fixed infrastructure every node communicates with other nodes. The nodes are therefore prevented from crime attack and at the same time the approach called marking on demand is used for the sake of security purpose. The approach called marking on demand is helpful into the MANET for the functions like message sending and data transmission. The MOD (Marking on Demand) Approach is more effective and entirely different from OPM(Opportunistic Piggyback Marking) and MBT (Marking Based Trace back) which have been already existing. The performance of trace back message and message delivery has not been effective. The aim of this paper is to propose the mechanism known as deterministic packet marking for the sake of tracking out the attack which involves in the MANET. The attack , therefore has to be prevented by using MOD and DBT mechanism. The main purpose of the DPM is to investigate and find the solution for the problem of mitigate and counter message. The technical execution is demonstrated through our design with the help of mathematical model, by which the packet delay is reduced. By this approach the packet delivery ratio and the throughput with the energy level are higher in the states of consumption and efficiency.

  Key-Words / Index Term
  Crime attack, Mitigate Counter Message, Traffic analysis, MANET

  References
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  Citation
  T. Dheepak, S. Neduncheliyan, "Low Power Distributed MAC Protocol Against Various Kinds Of Attacks By Using Traffic Analysis Methodology," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.1-7, 2018

• Open Access   Article

  Sentiment Analysis of movie reviews: A new feature-based sentiment classification

  Ketan Sarvakar, Urvashi K Kuchara

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.8-12, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.812

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  Abstract
  Sentiment Analysis also known as opinion mining is the task of detecting, extracting and classifying opinions or sentiments related to different topics. One such area of interest is sentiment classification or polarity determination of movie reviews in user specific choice which are dependent on either mood or emotion of user perspectives. This plays an important role in today’s world where the promotion of nay product or movies. Polarity determination is an important task for both the user and producer. They can take appropriate decision based on these results of classification. Thus, considering the needs and developing interests in social data mining and increasing dependency of users on customer reviews here we proposed a method to classify the data more accurately by altering the pre-processing tasks mainly filtering. Proposed methodology will be used for the available classification techniques by using the available dataset more consistently by working on the dictionary built by the filters.

  Key-Words / Index Term
  Sentiment Analysis; Dictionary; tokenizer; Accuracy; StringToWordVector, CustomStringToWordVector filter

  References
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  [3] Dr. S. Vijayarani, Ms. R. Janani, " TEXT MINING: OPEN SOURCE TOKENIZATION TOOLS – AN ANALYSIS," Advanced Computational Intelligence: An International Journal (ACII), Vol. 3, No. 1, 2016.
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  [5] Nan Hu, Noi Sian Koh, Srinivas K. Reddy, "Ratings lead you to the product, reviews help you clinch it? The mediating, " Decision Support Systems, Vol. 57, pp. 42–53, 2014.
  [6] Diana Maynard, Ian Roberts, Mark A. Greenwood, Dominic Rout, Kalina Bontcheva, "A framework for real-time semantic social media analysis," Web Semantics: Science, Services and Agents on the World Wide Web, 2017.
  [7] Manajit Chakraborty, Sukomal Pal, Rahul Pramanik, C. Ravindranath, "Recent developments in social spam detection and combating techniques: A survey," Information Processing and Management, Vol. 52, pp. 1053–1073, 2016.
  [8] Donato Hernández Fusilier, Manuel Montes-y-Gómez b, Paolo Rosso c, "Detecting positive and negative deceptive opinions using," Information Processing and Management, Vol. 51, pp. 433–443, 2015.
  [9] Peter F. Brown, Peter V. deSouza*, Robert L. Mercer,V. Pietra, J. C. Lai, "Class-Based n-gram Models of Natural," IBM T. J. Watson Research Center .
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  [13] J. H. Zaragoza, L. E. Sucar, E. F. Morales, C. Bielza, and P. Larranga, "Bayesian chain classifiers for multidimensional classification," In the proceedings of 2011 IJCAI Int. Jt. Conf. Artif. Intell., pp. 2192–2197, 2011.
  [14] S. ChandraKala and C. Sindhu, “Opinion Mining and Sentiment Classification: A Survey,” ICTACT JOURNAL ON SOFT COMPUTING, Vol. 03, Issue. 01, 2012.
  

  Citation
  Ketan Sarvakar, Urvashi K Kuchara, "Sentiment Analysis of movie reviews: A new feature-based sentiment classification," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.8-12, 2018

• Open Access   Article

  An Integrated approach for effective Intrusion Detection with Elasticsearch

  Piyush Pareta, Manish Rai, Mohit Gangwar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.13-17, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.1317

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  Abstract
  Cloud computing environments are easy targets for intruders and pose new risks and threats to an organization because of their service and operational models, the underlying technologies, and their distributed nature that relies on the network for its working. However, IDSs are among the efficient security mechanisms that can handle most of the threats of cloud computing. In spite this, several deficiencies of current IDSs technologies and solutions hinder their adoption in a cloud. The proposed work focuses on developing improved IDS that provides an integrated approach of both techniques i.e. anomaly based as well as knowledge based whether implement on network or host based IDS for cloud computing to detect masquerade, host, and network attacks and provides efficient deployments to detect DDoS attacks. The work comprises of integration of two powerful open source tool Suricata and Snort together with the proposed DDoS detection rule make the working of IDS more effective and high alarm rate generating Hybrid IDS.

  Key-Words / Index Term
  Cloud Security, Distributed Denial of Service Attack (DDoS), Intrusion Detection System (IDS), Suricata, Snort, Hybrid IDS.

  References
  [1]. http://www.nist.gov/itl/cloud/upload/cloud-defv15.pdf
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  [4]. Jabej J, Dr.B. Muthu Kumar, “Intrusion Detection System (IDS): Anomaly Detection using Outlier Detection Approach”, Science Direct, 2015.
  [5]. Nandita Sengupta, Ramya Chinnasamy “Contriving Hybrid DESCAST Algorithm for Cloud Security”, Elsevier, 2015.
  [6]. Anitha H M, P.Jayarekha “Security Challenges of Virtualization in Cloud Environment”, IJCSE, 2018.
  [7]. Praveen Kumar Rajendran, B. Muthukumar, G.Nagarajan, “Hybrid Intrusion Detection System for Private Cloud: A Systematic Approach”, Elsevier, 2015
  [8]. Hassen Mohammed Alsafi , Wafaa Mustafa Abduallah, “IDPS: An Integrated Intrusion Handling Model for Cloud Computing Environment”, Elsevier, 2014
  [9]. Amirreza Zarrabi, Alireza, “Internet Intrusion Detection System Service in a Cloud”, 2012
  [10]. Ahmed Patel, Mona Taghavi, et al, “An intrusion detection and prevention system in cloud computing: A systematic review”, Elsevier , 2012
  [11]. Seyed Mojtaba Hosseini Bamakan, et al., “New Intrusion Detection Approach using PSO based Multiple Criteria Linear Programming”, Elsevier, 2015
  [12]. Cong Wang, Qian Wang, and Kui Ren, “Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing”, IEEE INFOCOM 2010.
  [13]. Dimitrios Zissis, Dimitrios Lekkas “Addressing cloud computing security issues” Future Generation Computer Systems, Elsevier, (2012).
  [14]. Kshetri,N. Privacy and security issues in cloud computing: The role of institutions and institutional evolution. Telecommunications Policy (2012)
  [15]. http://www.nist.gov/itl/cloud/upload/cloud-defv15.pdf
  [16]. K. Salah, J. M. Alcaraz-Calero,S. Zeadally, S. Almulla and M. Alzaabi “Using Cloud Computing to Implement a Security Overlay Network”, IEEE, Security and Privacy, (2011).
  [17]. N. Cao, Z. Yang, C. Wang, K. Ren, W. Lou, "Privacy-preserving query over encrypted graph-structured data in cloud computing", in: IEEE International Conference on Distributed ComputingSystems, ICDCS’11, (2011), pp. 393–402
  [18]. S. Marston et al. / Decision Support Systems 51 (2011) 176–189
  [19]. Cong Wang, Qian Wang, and Kui Ren, "Privacy-Preserving Public Auditing for Data Storage Security in Cloud Computing", IEEE INFOCOM (2010).
  [20]. Shuai Zhang and Shufen Zhang “Cloud Computing Research and Development Trend”, IEEE, Second International Conference on Future Networks, (2010)
  [21]. J. Yao, S. Chen, S. Nepal, D. Levy, J. Zic, "Truststore: making Amazon S3 trustworthy with services composition", in: Proceedings of the ” 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, CCGRID’08, Melbourne, Australia, (2010), pp. 600–605
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  [23]. Groce, J. Katz, ”A new framework for efficient password-based authenticated key exchange”, in: Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS’10, Chicago, USA, (2010), pp. 516–525
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  Citation
  Piyush Pareta, Manish Rai, Mohit Gangwar, "An Integrated approach for effective Intrusion Detection with Elasticsearch," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.13-17, 2018

• Open Access   Article

  Handwriting Recognition System Using Optical Character Recognition

  Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.18-21, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.1821

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  Abstract
  This is an overview of the most recent published approaches to solving the handwriting recognition problem. This paper is aimed at clarifying the role of handwriting recognition in accordance with today`s maturing technologies. It tries to list and clarify the components that build handwriting recognition and related technologies such as OCR (Optical Character Recognition) and Signature Verification. This paper could also be regarded as a survey of handwriting recognition and related topics with a rich list of references for the interested reader. A level of practicality of use of this technology for different languages and cultures is also discussed.

  Key-Words / Index Term
  OCR, Language Model

  References
  [1]. Bhatia Neetu, “Optical Character Recognition Techniques”, International Conference of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 5, May 2014.
  [2]. Goutam Sarker, Monica Besra, Silpi Dhua. (2015) A Malsburg learning back propagation combination for handwritten alpha numeral recognition. 2015 International Conference on Advances in Computer Engineering and Applications, 493-498
  [3]. Fahim Irfan Alam, Bithi Banik. (2013) Offline isolated bangla handwritten character recognition using spatial relationships. 2013 International Conference on Informatics, Electronics and Vision (ICIEV), 1-6.
  [4]. Amjad Rehman, Tanzila Saba. (2012) Off-line cursive script recognition: current advances, comparisons and remaining problems. Artificial Intelligence Review.
  [5]. A.K.Gupta, S.Gupta Research Paper | Isroset-Journal (IJSRCSE) Vol.6 , Issue.2 , pp.38-40, Apr-2018, CrossRef-DOI:https://doi.org/10.26438/ijsrcse/v6i2.3840, Neural Network through face recognition.
  [6]. Haralick R. M.; Shanmugam K.; Dinstein I. (1973): Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, 3(6), pp. 610–621.
  [7]. Clausi D. A. (2002): An analysis of co-occurrence texture statistics as a function of grey-level quantization, Canadian Journal of Remote Sensing, 28(1), pp. 45-62.
  

  Citation
  Priti Gangania, Sowmya Mishra, Shreshtha Garg, Sonam Agarwal, "Handwriting Recognition System Using Optical Character Recognition," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.18-21, 2018

• Open Access   Article

  Image Classification Using Convolutional Neural Network

  N.S. Lele

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.22-26, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.2226

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  Abstract
  Image recognition, in the context of machine vision, is the ability of the software to identify objects, places, people, writing and actions in images. Computers can use machine vision technologies in combination with a camera and artificial intelligence software to achieve the task of image recognition. Image recognition is used to perform a large number of machine-based visual tasks, such as labeling the contents of images, performing image content search for guiding autonomous robots, self-driving cars and accidental avoidance system. While human brains recognize objects easily, computers have difficulty with the task. Software for image recognition requires deep machine learning. Performance is based on the complexity of convolutional neural network as the specific task requires massive amount of computational power for its computer-intensive nature. This work will review ‘CIFAR-10’ dataset which has classified images in various groups. This problem is a supervised learning task which will be able to classify any new images put forward from these various groups. This work also attempts to provide an insight into ‘You Only Look Once (YOLO)’ which is an example of unsupervised image classification. It can immediately classify the images into various objects by drawing rounded boxes around them and naming those objects.

  Key-Words / Index Term
  Deep Learning, Convolutional Neural Network, Image Classification, Computer Vision

  References
  [1] Chan T H, Jia K, Gao S, et al. “PCANet: A simple deep learning baseline for image classification,” arXiv preprint arXiv:1404.3606, 2014.
  [2] TKrizhevsky A, Sutskever I, Hinton G E, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, pp. 1097-1105, 2012.
  [3] Bouvrie J, “Notes on convolutional neural networks,” Neural Nets, 2006.
  [4] Chan T H, Jia K, Gao S, et al. “PCANet: A simple deep learning baseline for image classification,” arXiv preprint arXiv:1404.3606, 2014.
  [5] Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, “YouOnlyLookOnce: Unified,Real-TimeObjectDetection,” arXiv:1506.02640[cs.CV]
  

  Citation
  N.S. Lele , "Image Classification Using Convolutional Neural Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.22-26, 2018

• Open Access   Article

  Mood based Music System

  A.S. Mali, A.A. Kenjale, P.M. Ghatage, A.G. Deshpande

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.27-30, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.2730

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  Abstract
  Music is a significant element of life. People take its help to evoke their emotions and prefer listening to songs according to their mood. It takes a lot of efforts to find appropriate music from the list for the particular emotional state. Music players in today’s world are not giving priority to the emotional state of a person. The aim of this paper is to develop music system which considers human emotions into account. The emotional state can be interpreted from facial expressions through the webcam. We have utilized the CNN classifier to build a neural network model. This model is trained and subjected to detect mood from facial expressions using OpenCV. Songs belonging to particular sentiments are classified on the basis of tempo feature in beats per minute. A system generates music playlist based on that detected mood. Our music player will play that generated music playlist to improves user’s mood.

  Key-Words / Index Term
  Machine learning, Image processing, PyQt5, OpenCV, Convolutional Neural Network (CNN), Haar Cascade, Pygame

  References
  [1] V. Patchava, P. Jain,R. Lomte, P. Shakthi, H. B. Kandala, “Sentiment Based Music Play System ”
  [2] P. Pathak, Prof. S. Aranjo, “Mood based Music Player Karan Mistry”.
  [3] Viola, P., & Jones, M. J., “Robust Real-Time Face Detection”, International Journal of Computer Vision, 57(2), 137-154 (2004).
  [4] O. Arriaga, G. Paul, M. Valdenegro, “Real-time Convolutional Neural Networks for Emotion and Gender Classification”
  [5] Face detection in OpenCV using Haar cascade
  [6] The Echo Nest Developer Center.
  [7] Kaggle FER2013 open source dataset for face
  [8] A. R. Patel, A. Vollal, P. B. Kadam, S. Yadav, R. M. Samant,“MoodyPlayer: A Mood based Music Player”.
  [9] C. Jenefer, S. Leena, M. Nirmala, Dr. J. SelvaKumar, “A Survey: Expression Based Music Player”.
  

  Citation
  A.S. Mali, A.A. Kenjale, P.M. Ghatage, A.G. Deshpande, "Mood based Music System," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.27-30, 2018

• Open Access   Article

  Cloud reliability enhancement mechanisms: A Survey

  Vikas Mangotra, Richa Dogra

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.31-34, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.3134

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  Abstract
  In today’s era cloud computing becomes the hottest topic due to its ability to reduce the cost associated with computing. Cloud computing provides the on demand services like storage, servers, resources etc. to the users without physically acquiring them and the payment is according to pay per use. Since cloud provides the storage, reduces the managing cost and time for organization to the user but security and confidentiality becomes the one of the biggest obstacle in front of us. The major problem with cloud environment is, the number of user is uploading their data on cloud storage so sometimes due to lack of security there may be chances of loss of confidentiality. To overcome these obstacles a third party is required to prevent data, data encryption, and integrity and control unauthorized access for data storage to the cloud. To optimize better results we will review some paper and find the better results to remove the security barriers.

  Key-Words / Index Term
  Cloud Computing, security, and confidentiality

  References
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  [4] R. Jhawar, V. Piuri, and M. Santambrogio, “A Comprehensive Conceptual System-Level Approach to Fault Tolerance in Cloud Computing,” pp. 0–4, 2012.
  [5] C. A. Chen, M. Won, R. Stoleru, and G. G. Xie, “Energy-efficient fault-tolerant data storage and processing in mobile cloud,” IEEE Trans. Cloud Comput., vol. 3, no. 1, pp. 28–41, 2015.
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  Citation
  Vikas Mangotra, Richa Dogra, "Cloud reliability enhancement mechanisms: A Survey," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.31-34, 2018

• Open Access   Article

  An Implementation of an Vulnerability Management in Complex Networks and Defining Severity

  Rikam Palkar, Swati Chopade

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.35-38, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.3538

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  Abstract
  Complex systems are found in an extensive variety of areas from technological and social to biological environments. In spite of this scope of uses and settings in which complex systems are utilized as models, examines propose that numerous genuine systems are represented by a comparable elements. An important characteristic is that in general such networks are robust against failures but vulnerable against targeted attacks. Impossible to miss trademark: resilience. In this technological era business endeavor to stay aware of evolving technology, it implies that they have a high capacity to absorb changes. However, resilience mechanisms are not present per se in technological networks. Thus, this work presents a framework for vulnerability assessment, vulnerability analysis and vulnerability management in versatile technological networks

  Key-Words / Index Term
  Network, VMP

  References
  [1] “CWE- Common Weakness Enumeration”,Available: http://nvd.nist.gov/
  [2] “NVD Data Feed and Product Integration”, National Vulnerability Database, Available: http://nvd.nist.gov/download.cfm
  [3] N. W. Adger,“Vulnerability,” Global Environmental Change, no. 3, pp. 268–281, August.
  [4] A. Reka, J. Hawoong, and B. Albert-Laszlo,“Error and attack tolerance of complex networks,” Nature, no. 6794, pp. 378–382, July.
  [5] “CVE, Common Vulnerabilities and Exposures.” http://cve.mitre.org/
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  [9] “The OVAL Language.” http://oval.mitre.org/ - Open vulnerability and assessment language.
  

  Citation
  Rikam Palkar, Swati Chopade, "An Implementation of an Vulnerability Management in Complex Networks and Defining Severity," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.35-38, 2018

• Open Access   Article

  An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images

  Ratnesh Kumar Shukla, Ajay Agarwal, Anil Kumar Malviya

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.39-43, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.3943

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  Abstract
  In this paper the proposed model is using for the identity to the noisy and blurred images. In our surrounding they are a big problem of randomly change the environment, or climate change. Image processing, are working in different platform. Such as Pattern Recognition, Computer vision Pattern, Machine Learning and Artificial Intelligence is using for the authentication of unauthorised person. Images are using for the authentication and verification. Authentication and Registration is the initial step of the identification and verification of the object. In this paper we are introducing blur and noisy images. And compare these images in our database. If images are verified from proposed model then they registered in the database for future use. The problem is obtaining in unclear images. Blur and Noise is the main disturbance of the images found in captured process. This problem obtain when we are capturing the images. They are found in the presence of dust and lighting. So in this technique we are remove the noise and blur of images. In this proposed model for deblur and denoise is work on corrupted images. In current scenario there are different algorithms working on the quality of images. Images are in pixel from and there are found in million colours in images. Then it is found very difficult to original images. When we are using high quality of cameras then it is possible to capture good quality images. But when we are using these novel method, we are if recognize the image. So we are using proposed algorithm to identify the images. We are comparing the trainee images to store our database. Then we found a real image and registered in the database.

  Key-Words / Index Term
  Blurred and Noise Image, Wiener Filter, RANSAC algorithm, Face Recognition System, Templates, Data storage

  References
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  Citation
  Ratnesh Kumar Shukla, Ajay Agarwal, Anil Kumar Malviya, "An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43, 2018

• Open Access   Article

  A Review on Energy Efficient techniques in Green cloud: Open Research Challenges and Issues

  Anjum Mohd Aslam, Mantripatjit kaur

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.6 , Issue.3 , pp.44-50, Jun-2018

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v6i3.4450

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  Abstract
  Cloud computing is today’s most emerging field as it offers utility-oriented services based on pay-as-you-go model over the network. Due to the availability of on-demand scalable resources and provisioning of services all over the world, large organizations are shifting their workload on the cloud. This growing demand for the services of cloud with high usage of data centers has drastically increased the energy consumption by data centers hosting these cloud computing applications. The high consumption of data centers is responsible for high operational costs and emission of carbon footprints which is unfriendly to our environment. Thus, we need to emphasize and study various green cloud computing solutions that can be utilized to minimize the high operational cost and also to reduce the CO2 dissipation. This paper reviews various energy efficient techniques that can be used to reduce the energy consumption. Comparative analysis is also conducted to suggest better future endeavors.

  Key-Words / Index Term
  Cloud computing, Green data center, Virtualization, Service Level agreement, Energy Efficiency

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  Citation
  Anjum Mohd Aslam, Mantripatjit kaur, "A Review on Energy Efficient techniques in Green cloud: Open Research Challenges and Issues," International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.44-50, 2018