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With the monstrous grow in the usage of computers and Internet for information sharing, success of applications running on various platforms causes a serious risk to the security policy. Complex behavior of malwares are also increase, the mechanism to catch malwares also needs improvement. The challenges grow towards the network security due to the introduction of new attacks. This paper emphasize on a hybrid data-mining approach based on ensemble classifier. This preferred approach gives a hybrid classifier which improves the overall detection rate. Preferred approach gives more accuracy and decrease the false positive rate. With this preferred approach the classification accuracy is 99.9894% and the false positive rate is about 0.00. The comparison of preferred approach is made with the single best classifier and it is perceived that the preferred approach gives better results for User to Root (U2R) and Remote to Local (R2L) attack that present in NSL KDD intrusion dataset. This approach gives better results for Root-kit attack.

Intrusion detection system (IDS), false positive rate, NSL-KDD Data set

[1] R. Venkatesan, “A Survey on Wireless Intrusion Detection using Data Mining Techniques”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) Volume. 1, Issue. 1, 2014.
[2] M. Gyanchandani, J.L.Rana, R.N.Yadav, “Taxonomy of Anomaly Based Intrusion Detection System: A Review”, International Journal of Scientific and Research Publications, Volume 2, Issue 12, 2012.
[3] N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE: Synthetic Minority Over-sampling Technique”, Journal of Artificial Intelligence Research, Volume 16, pp. 321–357, 2002.
[4] S. L. Pundir, A. Amrit, “Feature Selection Using Random Forest In Intrusion Detection System”, International Journal of Advances in Engineering & Technology, Volume 6, Issue 3, pp. 1319-1324, 2013.
[5] Y. Yang, L. Gao, Y. B. Yuan, K. McLaughlin, S. Sezer, “Multidimensional Intrusion Detection System for IEC 61850 based SCADA Networks”, IEEE ,Volume 32, Issue 2, pp. 1068-1078, 2016.
[6] M. B. Shahbaz, X. Wang, A. Behnad and J. Samarabandu, “On Efficiency Enhancement of the Correlation-based Feature Selection for Intrusion Detection Systems” , In the proceedings of the 2016 IEEE 7th Annual Information Technology, Electronics and Mobile Communication Conference(IEMCON), Canada, pp. 1-7, 2016.
[7] M. A. Iftikhar, M. Hassan, H. Alquhayz, “A colon cancer grade prediction model using texture and statistical features SMOTE and MRMR”, In the proceeding of IEEE 2016 19th International Multi-Topic Conference(INMIC), Pakistan , pp.1-7, 2016.
[8] M. F. Naufal, S. Rochimah, “Software Complexity Metric-based Defect Classification Using FARM with Preprocessing Step CFS and SMOTE”, International Conference on Information Technology Systems and Innovation (ICITSI) ,Bandung – Bali, pp.16–19, 2015
[9] D. H. Deshmukh, T. Ghorpade, P. Padiya, “Improving Classification Using Preprocessing and Machine Learning Algorithms on NSL-KDD Dataset” International Conference on Communication, Information & Computing Technology (ICCICT), India, pp. 245 , 2015
[10] D.P.Gaikwad, R. C. Thool, “Intrusion Detection System Using Bagging Ensemble Method of Machine Learning”, In the proceeding of IEEE International Conference on Computing Communication Control and Automation, India, 2015
[11] S. Chaudhary, A. Bhowal, “Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection system”, In the proceeding of IEEE 2015International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), pp.89-95, 2015
[12] A. Tesfahun, D. L. Bhaskari, “Intrusion Detection using Random Forests Classifier with SMOTE and Feature Reduction”, In the proceeding of IEEE International Conference on Cloud & Ubiquitous Computing & Emerging Technologies, pp. 127-132, 2013
[13] D. K. Dagly, R.V. Gori, R.R. Kamath, D. Sharma, “Hybrid Intrusion Detection System Using K-Means Algorithm”, International Journal of Computer Science and Engineering, Volume 4, Issue 3, pp. 82-85,2016.

R. Richhariya, A.K. Manjhwar, R. R. Singh Makwana, "A Hybrid Approach for User to Root and Remote to Local Attack," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.73-79, 2017.

This Paper Represents The Design And Implementation Of Intelligent Systems In The Industries For Monitoring The Temperature, Gas And Humidity In The Environment Of Any Type Of Industrial Areas. This Project Can Be Implemented By Using The IOT Module And A Peripheral Interface Controller (PIC) Microcontroller. Here the wireless sensor devices will sense the area’s environment and transmits that information to the cloud AMAZON WEB SERVICES (AWS) to store that information for further analysis and immediately sends an alert to the Industry for the purpose of protecting themselves from unexpected disaster. We can see that stored data in cloud from any place of the world.

IOT, PIC, AWS

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D. Janardhan Reddy, S. Phani Kumar, "IOT based Industrial Module For SafteyMeasures," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.74-78, 2017.

Pulse shaping plays a crucial role in spectral shaping in the modern wireless communication to reduce the spectral bandwidth. Pulse shaping is a spectral processing technique by which fractional out of band power is reduced for low cost, reliable, power and spectrally efficient mobile radio communication systems. The Multipath channel has two main considerations spectral efficient transmission and reduced ISI both these requirement of Spectral efficient transmission and reduced ISI are satisfied by pulse shaping the transmitted signal. I2SPhybrid pulse shape introduced in this research work by using weighting techniques has better spectral characteristics and shows improved OBR performance when used with SC-FDMA system.

pulse shape, ISP, OBR, SC-FDMA, companding

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[4] Ishu,” PAPR Reduction in Wavelet based SCFDMA using Pulse Shaping Filters for LTE Uplink Transmission”, International Journal of Applied Engineering Research, Volume 9, Number 20, 2014, pp. 6481-6492.
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[7] T. Frank, A. Klein and E. Costa, IFDMA - a scheme combining theadvantages of OFDMA and CDMA," Special Issue on "Next GenerationCDMA vs. OFDMA for 4G Wireless Applications" IEEE Wireless Communications Magazine, 2007.
[8] Divyangna Gandhi, Shilpi Gupta, Upena Dalal, Implementation of Pulse Shaping Techniques in OFDM System, International Journal of
Computer Applications (0975 – 8887) Volume 68– No.10, April 2013.
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techniques in wireless signal processing approach-In depth review,
International Journal of Computational Engineering & Management,
Vol. 12, April 2011.
[10] A. S Kang , Vishal Sharma, Pulse Shape Filtering in WirelessCommunication-A Critical Analysis, (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 2, No.3, March 2011.
[11]G. Bhandari, S. Vijay, “An Improved pulse shape combined with companding transform to optimize SC-FDMA performance”, International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 11, November 2016, pp 461-470

G. Bhandari, S. Vijay, "I2SPhybrid pulse shape: Modified I2SP pulse by using weighting technique and it’s impact on SC-FDMA system," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.85-89, 2017.

Image retrieval systems are used to search and browse the images from large digital image databases and retrieval of these images. Content-Based Image Retrieval (CBIR) gives an efficient approach to browse and retrieve images from these large databases, but the semantic gap between low-level and high-level features is a big issue. To overcome this issue Particle Swarm Optimization is used with a new combination of low-level features. Moments can be used to characterize the color distribution of an image. A color feature of an image is extracted by calculating color moments which are unique and invariant to rotation and scaling. Rotated Local Binary Pattern is used to extract texture information from the image, it is invariant to rotation and scaling. Edges give the object representation of an image and used as a feature descriptor for image retrieval, Here Edge Histogram Descriptor is used to find out the abruptly changes in the pixel value of the image. Edge Histogram Descriptor (EHD) provides the spatial information about five types of edges of an image. For performance evaluation, we simply used weighted Euclidian distance with optimal weights and calculate Average precision, recall and accuracy. Experiment result shows that the proposed method gives improved precision and recall in comparison to existing method. The efficiency of proposed system is tested for three types of datasets: WANG dataset, LI dataset and Caltech-101 image dataset.

CBIR, feature extraction, color Moments, RLBP, EHD, PSO

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[18] Mohd. Aquib Ansari, and Manish Dixit, “An Image Retrieval Framework: A Review”, International Journal of Advanced Research in Computer Science, ISSN No. 0976-5697, Volume 8, No. 4, May – June 2017.
[19] Diksha Kurchaniya, and Punit Kumar Johari, "Analysis of Different Similarity Measures in Image Retrieval Based on Texture and Shape”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 04, e-ISSN: 2395-0056, p-ISSN: 2395-0072, Apr -2017.

D. Kurchaniya, P. K. Johari, "An Efficient Approach for Image Retrieval using Particle Swarm Optimization," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.90-99, 2017.

Today software has reformed the key element on every environment. Quality of software is connected with the number of faults as well as it determinate by time and cost. Software is a process and maintains continuous change to improve the functionality and effectiveness of the software quality. During the life cycle of software various problems arises like advanced planning, well documentation and proper process control. Software defects are expensive in specification of cost and quality. Software defect prediction improves quality framework predictive techniques and software metrics to provide fault-prone module description. This paper main feature is the concept of change proneness and software prediction model used to control the classes of software which are often to change. We have two aspects to be inscribed Parameters like Accuracy, Precision, Recall and Receiver operating characteristics (ROC). Machine learning algorithms are used for predicting software. This paper is proposing to relate and compare all machine learning techniques interrelated to performance parameters.

Software Quality, Support Vector Machine, Software Defect Prediction, Faults Prone, Change Proneness

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J. Devi, N. Sehgal, "A Technique for Improving Software Quality using Support Vector Machine," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.100-105, 2017.

WWW-world wide web is one of the most required resource for getting information and knowledge. Several organizations rely on websites to get new customers and to hold the existing one. The customer’s surfing pattern can be obtained by web log record. This is a kind of work in the arena of web usage mining. The paper proposed the novel methodology by mining the usage of patters from web log records of real time. Ontology of the web content including user profiles and external data can be developed by Web usage mining. In this paper a method is proposed for discovering and tracking the growing user profiles along with domain specific information facets. To judge the quality of the obtained profiles after mining an objective validation plan is also used. Through this research organizations can take better decision by getting better recommendation

Data-mining, web-mining, web usage mining

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Approach to Web Usage Mining,Technical Report 02-5002, Department of
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Usage Mining, Data Warehousing andKnowledge Discovery, (DaWaK’02),
LNCS 2454, SpringerVerlag Germany, pp73-82, 2002.
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Priyanka Sharma, R.K. Gupta, "A Novel Web Usage Mining Technique Analyzing Users Behaviour Using Dynamic Web log," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.106-111, 2017.

The most popular open source distributed computing framework called Hadoop was designed by Doug Cutting and his team, which involves thousands of nodes to process and analyze huge amounts of data called Big Data. The major core components of Hadoop are HDFS (Hadoop Distributed File System) and MapReduce. This framework is the most popular and powerful for store, manage and process Big Data applications. But drawback with this tool related to stability and performance issues for small file applications in storage, manage and processing the data. Existing approaches deals with small files problem are Hadoop archives and SequenceFile. However, existing approaches doesn’t give an optimized performance to solve small files problems on Hadoop. In order to improve the performance in storing, managing and processing small files on Hadoop, we proposed an approach for Hadoop MapReduce framework to handle the small files applications. Experimental result shows that proposed framework optimizes the performance of Hadoop in handling of massive small files as compared to existing approaches.

Hadoop, Hadoop Distributed File System (HDFS), MapReduce, Hadoop Archives, Sequence File, Small Files

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Guru Prasad M.S., Nagesh H.R., Swathi Prabhu, "An Efficient Approach to Optimize the Performance of Massive Small Files in Hadoop MapReduce Framework," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.112-120, 2017.

Driver tiredness is one of the most important causes of road accidents. This article presents a real-time non disturbance drowsiness monitoring scheme which exploits the driver’s facial appearance to identify and aware tired drivers. This presented work worn the Viola-Jones Algorithm to identify the driver’s facial appearance. Continuously Adaptive Mean Shift algorithm has been used for continuous face tracing of driver. With this uncomplicated and not expensive execution, the whole scheme achieved an accuracy of 99.5%, outperforming other developed schemes adopting expensive hardware to arrive at the similar objective.

Image processing; Face recognition and tracing; Fatigue level; Ada-boost learning classifier; Circular Hough Transform; Continuous Mean shift algorithm; Viola Jones algorithm for Face recognition

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[12] C. Ching, M.H.Yap, I.costen and B. li, “Wrinkle Detection Using Hessian Line Tracking, IEEE Access, Manchester Metropolitan University, Manchester, U.K., pp. 1079-1088, 2015. For Conference
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A.S. Kulkarni, S.B. Shinde, "Monitoring Driver Distraction in Real Time using Computer Vision System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.121-128, 2017.

Image processing used in wide variety of applications such as Image restorations, satellite, and medical etc. With enrichments of the image processing libraries especially of openCV and Matlab, many applications are being developed day by day in computer vision or image processing domain. We have designed the bone fracture detection method using “Image Processing” toolbox in Matlab. Aim of the project is to locate exact fracture area in inputted X-ray image. We will check bone integrity to detect any crack or disjoint of two cartilages. The professed algorithm is divided in few step namely pre-processing, Segmentation and ROI search and detection. Features like area, length and pixel locations of the segments are used to identify fracture in X-ray image. Algorithm has been simulated on various X-ray images which show good results to locate fracture in image. Also, in this approach we found that canny edge detection works far better than any other edge detection for segmenting the fractured part.

Atutomatic, Bone, Canny, Fracture, Image Processing, Preprocessing,Segmentation, X-Ray

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B. Gajjar, S. Patel, A.Vaghela, "Fracture detection in X-ray images of long bone," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.129-133, 2017.

Regression testing is used to retest the modified version of software. Regression testing is expensive but still an important process. In regression testing, test case prioritization is used to improve the efficiency of the regression test suite by executing the most critical test cases first. As retesting of entire program is not possible with adequate time and cost i.e. only subset of all test cases will execute for regression testing. In this paper, we introduce a technique for regression test case prioritization based on supervised machine learning. We use Genetic Algorithm to make test case description processable for machine learning. In our approach we have consider machine learning classification model logistic regression to evaluate and calculate the prioritization quality. Our result indicates that our technique gives more accurate result as compare to other techniques. We use hybrid combination of genetic algorithm and logistic regression to improve the test case prioritization technique.

Regression testing, test cases, prioritization techniques, Genetic Algorithm, Logistic Regression

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Neeraj Kumar Saklani, Parulpreet Singh, "A Genetic Algorithm for Regression Test Case Prioritization," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.134-137, 2017.