Volume-6 , Issue-10 , Oct - 2018 Go Back

Now a day’s security is very global issue in Network based systems. The rate of cyber terrorism has increased day by day and it put national security under risk. In addition, network attacks have caused several damages to different sectors (i.e., individuals, economy, enterprises, organizations, and governments). Network Intrusion Detection Systems are giving the solutions against these attacks. NIDS always need to improve their performance in terms of increasing the accuracy and decreasing false alarm rates. Feature selection gives the ranking to the data set attributes and it can help for selecting the most important features from the entire set of data. In the previous researches feature selection selects the irrelevant and redundant features. These are causes of increasing the processing speed and time. An efficient feature selection method eliminates dimension of data and decrease redundancy and ambiguity. In the present network intrusion detection systems working with the long payload features are not easy tasks because many machine learning algorithms can’t handle these long payload features. Some of the Network Intrusion Detection Systems are not process these long payload features. To solve this problem, a new methodology called feature extraction through Fourgram technique has been proposed. The long payload features are processed these proposed technique and prepared to be implemented in machine learning algorithms and the results were carried out on ISCX 2012 data set. The designed feature selection system has shown a very good improvement on the performance using different metrics like Accuracy, F- Measure etc.

NIDS,Feature Extraction,Fourgram Scheme, Long Payload Features, Dataset, Dictionary Building

[1] TarfaHamed, Rozita Dara, Stefan C. Kremer, Network intrusion detection system based on recursive feature addition and bigram technique, International journal of computers & security, Vol. 73, pp.137–155, 2018.
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[17] Zhang M, Wang L, Jajodia S, Singhal A, Albanese M, “Networkdiversity: a security metric for evaluating the resilience ofnetworks against zero-day attacks”, International journal of transformation of information science Vol.5, No.11, pp.1071–1086, 2016.
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Abdul Rustum Ali, K N Brahmaji Rao, "An Effective Feature Extraction through Fourgram Scheme for Long Payloads in Network Intrusion Detection Systems," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.296-303, 2018.

Security has become one of the key features of data transmission on large databases. Sensible data that are available in the form of documents or unstructured format must be shared among the users in a confidential manner. NoSQL databases are nowadays popular in handling the unstructured data that are available as open source databases such as MongoDB, Cassandra, Redis etc. This paper make a detailed study on the encryption techniques of NoSQL databases especially MongoDB which becomes popular in data management. Since encryption features are not applied on handling the data in MongoDB, the various encryption techniques proposed on securing the sensitive data at rest and at transit are compared based on different encryption algorithms.

Data Security, NoSQL, MongoDB Encryption, encryption at rest, encryption at transit

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[11]. At-test encryption in MongoDB 3.2: features and performance. https://www.mongodb.com /blog/post/at-rest-encryption-in-mongodb-3-2-features-and-performance.Accessed: 2017-01-23.
[12]. Vidushi Jain, Aviral Upadhyay, , “MongoDB and NoSQL Databases”,International Journal of Computer Applications (0975 – 8887) Volume 167 – No.10, June 2017
[13]. Zhu Wei-ping, Li Ming-xin- Using MongoDB to Implement Textbook Management System instead of MySQL IEEE2011
[14]. Lior Okman, Nurit Gal-Oz, Yaron Gonen, Ehud Gudes, Jenny Abramov - Security Issues in NoSQL Databases IEEE2011
[15]. Tianyu Jia, Xiaomeng Zhao, Zheng Wang, Dahan Gong and Guiguang Ding - Model Transformation and Data Migration from Relational Database to MongoDB IEEE 2016.
[16]. Charmi Pariawala, and Ravi Sheth,” Encrypting Data of MongoDB at Application Level”, Advances in Computational Sciences and Technology, Volume 10, Number 5 (2017) pp. 1199-1205
[17]. Karan Patel, Kirti Sharma, Mosin Hasan. “Encrypting MongoDB Data using Application Level Interface”. Discovery, 2015, 46(214), 164-169
[18]. Anand Shende1, Omkar Gurav2, Swapnil Shirode3, Piyush Govekar 4, S.N.Zaware 5, “Secure Unique Identification using Encrypted Storage in NoSQL Database”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 5, Issue 4, April 2016
[19]. Hanan A. Al-Souly, Abeer S. Al-Sheddi, Heba A. Kurdi;Fast, Lightweight Symmetric Encryption Algorithm for Secure Database. (IJACSA) International Journal of Advanced Computer Science and Applications, Special Issue on Extended Papers from Science and Information Conference 2013.
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P. Rajesh Kannan, R. Mala, "COMPARISON OF ENCRYPTION ALGORITHMS ON NoSQL DATABASES," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.304-311, 2018.

Brain tumor is a serious type of disorder which is caused by the abnormal cells formation within the brain. The identification of brain tumor and further analysis from the Magnetic Resonance Imaging (MRI) is a vigorous process and in this paper fast and effective method is used for the tumor detection by using Chebyshev Harmonic Fourier Moments (CHFMs) on segmented magnetic resonance brain images. The proposed method is free from any overflow situations as it does not involve any factorial term and also free from underflow situations as no power terms are involved. Before the segmentation process, the feature set is extracted by using 2D Continuous Wavelet Transform (2D-CWT). Asymmetry in the MR brain image is analyzed by using CHFMs on each of the tissues segmented in the head. Once the presence of asymmetry is confirmed, it leads us to the diagnosis of the tumor. After the presence of tumor, the region of tumor is extracted by using Polar Harmonic Transforms (PHTs) as these transforms are found to be good descriptors in the field of image analysis and impose less computational complexity due to the absence of any factorial term in the calculation of radial kernels. The effectiveness of the proposed method is analyzed by doing experiments on 35 MR brain images with tumor and 65 normal MR brain images. It is observed that that the proposed method and technique is successful in 97% cases.

Tumor detection, Chebyshev Harmonic Fourier Moments, Polar Harmonic Transforms, Segmentation

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A. Prashar, R. Upneja, "Fast and Effective Method for the Detection of Brain Tumor using Chebyshev Harmonic Fourier Moments," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.312-316, 2018.

Vehicular networks facilitate safer and more comfortable travel, but pose several challenges due to high rates of mobility and poor link quality. Information Centric Networking (ICN) is a recently proposed network paradigm that improves performance of networks with content as the focus. In this paper, we propose a differentiated cache replacement scheme called D-cache for content centric vehicular networks. In D-cache, vehicular network traffic is divided into time-sensitive sensor data (S-data) and infotainment related data (I-data). Simulation results show that the proposed scheme improves both the cache hit ratio and the average content retrieval time even at high mobility rates for content centric vehicular networks.

Vehicular networks, content-centric networks, qualtiy of service, caching

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Korla Swaroopa, Sireesha Rodda, Shanti Chilukuri, "Differentiated Caching for Improved QoS in Vehicular Content-centric Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.317-322, 2018.

Human action recognition (HAR) in visual data has become one of the attractive research area in the field of computer vision including object detection, recognition, retrieval, domain adaptation, transfer learning, segmentation etc. Over the last decade, HAR evolved from heuristic hand crafted feature to systematic feature learning namely deep feature learning. Deep feature learning can automatically learn feature from the raw inputs. Deep learning algorithms, especially Convolutional Neural Network (CNN), have rapidly become a methodology of choice for analysing recognition of videos. In this paper, details of recent trends and approaches of deep learning including CNN, Recursive Neural Network (RNN), Long Short term Memory (LSTM) and Autoencoders which are used in HAR are discussed. The challenges are identified to motivate the researchers for future works.

HAR, CNN, LSTM, Deep Learning model

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K.Kiruba, D. Shiloah Elizabeth, C Sunil Retmin Raj, "Deep Learning for Human Action Recognition – Survey," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.323-328, 2018.

Due to reliability, there are million and millions of the uses and users of the cloud storages. So clouds are heavily developing field for various purposes. Many cloud hosts are providing services to different clients for their data storage. Due to disaster management cloud can be used as secured storage mechanism. For such cloud storages encryption is done many a ways for securing cloud data. The hybrid attribute based encryption i.e hybrid H-HABE is the method to encrypt the contents. This paper discusses the hybrid H-HABE encryption method for the cloud storages. The technique is also used to hide identity of the user that means the anonymous authentication can be implemented only by use of attributes. Our research work also analyses the importance of the data security in the cloud. Reason for choosing symmetric encryption algorithms are efficient to handle encryption and decryption for large amount of data, and effective speed of storing data and accessing the data in the cloud system. For implementation purpose here are considered the type of file as document file(doc), text file(txt) which can be enhance to sound file(.avi), video file, image file with different formats(BMP,JPG,GIFF,PNG).

Cryptography,Security,Cloud Computing System; hybrid H-HABE

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G. Narmadhai, S. Vijay Bhanu, "Comparative Study of Hybrid Attribute Based Encryption for Cloud Computing System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.329-335, 2018.

In a mobile ad-hoc network, congestion is one among the foremost necessary restrictions that deteriorate the performance of the total network. It’s essential to regulate the info rate utilized by every sender so as to not overload the network, wherever multiple senders vie for link information measure. The Packets at the network is also dropped after they reach the router and can`t be forwarded. Several packets area unit dropped whereas excessive quantities of packets make a network bottleneck. The proposed ESNM technique of the research uses the Watchdog method with IRTBDM to cut back the congestion rate at the MANET. The proposed technique provides a higher packet delivery ratio, scale back packet drop ratio and communication overhead over existing methods. These factors are necessary for MANET performance and reliability.

ESNM, Congestion control, Watchdog method, Replica allocation, Selfish node

[1]. Suchita. A. Kulkarni, P. U. Dere, “Truthful detection of selfish nodes in context to replica allocation over a mobile ad hoc network”, International Journal of Industrial Electronics and Electrical Engineering, ISSN: 2347-6982 Volume- 2, Issue- 2, Feb.-2014, pp 10-16.
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[5]. Jae-Ho Choi, Kyu-Sun Shim, Sang Keun Lee, and Kun-Lung Wu, “Handling Selfishness in Replica Allocation over a Mobile Ad-Hoc Network”, IEEE Transactions on Mobile Computing, Vol. 11, No. 2, February 2012, pp 909-920.
[6]. Esha Kumar, Shivanka Chugh, S.P Ghrera, “Performance Analysis of RED, With Different TCP Congestion Control Mechanism”, 2011 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC 2011), Published by IEEE 2011, Vol-6, PP 414-420, ISSN 978-1 4577-0240-2/11.
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[8]. Zhao Shuang and Shang Yu, “A Study of Congestion Control Strategy Based on the Router on the Internet”, Published by Third International Conference on Multimedia Information Networking and Security, IEEE 2011 Vol. 13, pp 114-118, print ISBN 978-1-4577-1795-6.
[9]. Gianluigi Pibiri, CiaránMcGoldrick, and Meriel Huggard, “Using Active Queue Management to Enhance Performance in IEEE802.11”.Published by ACM, Vol 17, ISBN 978-1-60558-621, pp70-77.
[10]. N. Gomathi, P. Seethalakshmi, and A. Govardhan, ”Active Queue Management for Multimedia Streaming in Mobile Ad-Hoc Networks”, Published by European Journal of Scientific Research, ISSN 1450-216X Vol.72 No.1 (2012), pp. 107-123.
[11]. Chien-Erh Weng, Rui-Yi Li and Chao Hsing Hsu, ”Joint Priority Random Early Detection and IEEE 802.11 EDCF to Support QoS in Wireless LAN”, Published by International Journal of Innovative Computing, Information and Control Volume 8, Number 8, August 2012 ISSN 1349-4198, pp. 5425-5438.
[12]. D. C. Joy Winnie Wise and N. Kesavan Nair, “Congestion Control Algorithm for Wireless Networks”. Published by European Journal of Scientific Research, ISSN 1450 -216 X Vol.80 No.4 (2012), pp.487-493.
[13]. Wu-changing, Dilip D. Kandlurz, and DebanjanSahaz, Kang G. Shin, “A Self Configuring RED Gateway”, Published by INFOCOM ` Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings IEEE Vol-3, ISSN 0743-166X, PP 1320-1328.

Sandeep dubey, Ravi kumar Singh pippal, "EFFICIENT SELFISH NODE MANAGEMENT METHOD FOR MANET," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.336-340, 2018.

Stream data processing is the next ‘big thing’ in big data which is one of the most propagating fields in computer science. The stream data analytics is an important aspect while dealing with data stream mining. While dealing with the classification of stream data, concept drift and its effect are required to be considered. Massive online analysis (MOA) is one of the most popular tools to perform analytics on stream data. We primarily deal with three features which are provided by moa namely classification, clustering & concept drift. The key emphasis is on experimental analysis on the combination of different procedures and learner algorithm which are suited for training the model so it can be used for the prediction purpose. Besides that, we have also tried to identify drift (change) in data and its effect on performance. So conceptually after taking proper measures about the noise and drifts, we can construct a model which is persistent to all the changes it faces. Stream analytics also required exploring the different clustering techniques which have a wide number of applications. We have presented all the empirical analysis carried out on classification and clustering techniques in a tool called MOA.

Stream Processing, Concept Drift, classification, Clustering, MOA

[1]. Bifet, A., Holmes, G., Pfahringer, B., Read, J., Kranen, P., Kremer, H., ... & Seidl, T. (2011, September). MOA: a real-time analytics open source framework. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 617-620). Springer, Berlin, Heidelberg.
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Hari A. Patel, Harsh N. Patel, Nirav Bhatt, "Empirical Analysis on Stream Classification & Clustering with Concept Drift in MOA," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.341-345, 2018.

Breast cancer (BC) is a deathly cancer disease which occurs mainly in women and the greater number of breast cancer patients leads to death according to global statistics. The early examination of Breast Cancer can augment the durability of patients, and it helps to improve the prompt medication to the patients. Machine learning plays an important role in health care and they are more powerful in classification and prediction process. There are various classification algorithms used based upon then data set. This work is the implementation of few classification algorithms such as Random Forest, K Nearest Neighbor, Navie Bayes, Support Vector Machine, and Artificial Neural Network for breast cancer data set. This paper is the comparative study of these algorithms using R tool. The goal of this paper is to analyze the accuracy of these algorithms. The implementation procedure reveal that the performance of any algorithm varies based on the data set attributes and characteristics.

Machine Learning, Classification, Random Forest, K Nearest Neighbor, Navie Bayes, Support Vector Machine, Artificial Neural Network, R Tool

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[10]. Adnan Alam khan and Shariq Ahmed “Comparative analysis of data mining tools for lungs cancer patients” 2015 Journal of Information & Communication Technology Vol. 9, No. 1, (Spring2015) 33-40.
[11]. E. SathiyaPriya “A Study on Classification Algorithms and Performance Analysis of Data Mining using Cancer Data to Predict Lung Cancer Disease” 2017-International Journal of New Technology and Research (IJNTR) ISSN:2454-4116, Volume-3, Issue-11, November 2017 Pages 88-93.
[12]. ShamreenFathimaSaddique, Sharmithra P, Justin Xavier D “Prediction of Lung Cancer Using Classifier Models” 2016 - International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Index Copernicus Value (2016): 79.57 | Impact Factor (2015): 6.391.
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[14]. T. Marikani “Prediction of Heart Disease using Supervised Learning Algorithms” 2017 - International Journal of Computer Applications (0975 – 8887) Volume 165 – No.5, May 2017.
[15]. R. Jothikumar and R.V. Sivabalan “Analysis of Classification Algorithms for Heart Disease Prediction and its Accuracies” 2016 - Middle-East Journal of Scientific Research 24 (Recent Innovations in Engineering, Technology, and Management & Applications): 200-206, 2016 ISSN 1990-9233.
[16]. Akil Bansal, Manish kumar Ahirwar, Piyush kumar sukla, “A Survey on Classification Algorithms used in Healthcare Environment of the Internet of Things”. International journal of Computer Sciences and Engineering, Vol 6, Issue 7, Pp 883-887, July 2018.
[17]. J.Seetha and T.Chakravarthy, “Diabetes classification using machine learning techniques with the help of cloud computing”. International journal of Computer Science and Engineering, Pp. 278-283, ISSN. 2347-2693, Vol. 6, Issue. 8, Aug-2018.

S. Kumaravel, S. Ophilia Domanica Vithya, "Emperical Evaluation of Machine Learning algorithms for Breast Cancer Data Classification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.346-351, 2018.

Developing a precise and consistent model for classifying imbalanced medical data is one of the major challenges in machine learning and data mining. As the advanced growth in medical technology, a classy medical classification system is essential that make use of data mining algorithms to support medical diagnosis practice. Though the standard medical data seldom obeys the requirements of different knowledge engineering tools, most of the medical datasets are considered to be highly imbalanced with respect to their class label. So the imbalancing problem has been found to thwart the efficiency of the learning model. The only way to avoid this problem is to reduce the gap between both majority and minority class instances. In our approach a fuzzy gravitational classifier with weighting scheme is employed, in which weight is optimized using Particle swarm optimization algorithm. The technique is implemented and tested with three well known bench mark imbalanced dataset from UCI and KEEL repository. A comparative study is made with two existing classification methods viz. Weighted nearest neighbour and class based weighted nearest neighbour. Evaluation results shows our hybrid approach gives better performance on imbalanced data in terms of AUC, F-measure and G-mean.

Imbalanced data, PSO optimization, Data gravitation classifier, Fuzzy soft set, JFIM

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Sinciya P.O., J. Jeya A Celin, "FUZZY GRAVITATIONAL CLASSIFIER FOR CLASSIFYING IMBALANCED DATASETS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.10, pp.352-358, 2018.