Volume-4 , Issue-4 , Apr - 2016 Go Back

Orthogonal Frequency Division Multiplexing (OFDM) is an efficient scheme for high speed data transmission in communication systems. However, this scheme has inherent problem of a High Peak to Average Power Ratio (PAPR), which causes significant reduction in performance and power efficiency of this scheme. In this paper, we propose a PAPR reduction method based on companding technique for the localized and Distributed Discrete Fourier Transform-Spread OFDM (DFTS-OFDM) systems. The simulation results indicate that the proposed method obtains about 10 dB PAPR reduction compared with simple OFDM systems.

OFDM; PAPR Reduction; Pre-coding; Companding method; LTE

[1] Zhong, X.; Qi. J.; Bao, J., “Using clipping and filtering algorithm to reduce PAPR of OFDM system”, In the Proceedings of the International Conference on Electronics, Communication, and ControlNingbo, China, pp (1763-1766) , September 2011.
[2] Slimane, S.B., "Reducing the Peak-to-Average Power Ratio of OFDM Signals Through Precoding," Vehicular Technology, IEEE Transactions on, Vol-56, No-02, pp (686-695), March 2007.
[3] Y. Jiang, "New companding transform for papr reduction in ofdm," in IEEE Communications Letters, Vol-14, No-04, pp (282-284), April 2010.
[4] Dhungana, H.; Sah, S.K.; Shakya, S., "Performance evaluation of PAPR reduction in multicarrier system by PTS and SLM methods,” 2012 Third Asian Himalayas International Conference on Internet, Kathmandu, pp (1-5), 2012.
[5] Kitaek Bae.; Andrews, J.G.; Powers, E.J., "Adaptive active constellation extension algorithm for peak-to-average ratio reduction in OFDM," Communications Letters, IEEE, vol-14, No-01, pp(39-41), January 2010.
[6] Wattanasuwakull, T.; Benjapolakul, W., "PAPR Reduction for OFDM Transmission by using a method of Tone Reservation and Tone Injection," 2005 5th International Conference on Information Communications & Signal Processing, Bangkok, pp ( 273-277), 2005.
[7] Mountassir, J.; Isar, A.; Mountassir, T., "Precoding techniques in OFDM systems for PAPR reduction," Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean, pp.728, 731, 25-28 March 2012.
[8] Erik Dahlman; Stefan Parkvall; Johan Sköld “4G LTE/LTE-advanced for Mobile Broadband”, UK: Elsevier, 1st edition, ISBN: 9780124199859, 2011.

Hossein Hamedi, Azim Fard , "PAPR Reduction Method for the Localized and Distributed DFTS-OFDM System Using the Companding Technique," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.1-5, 2016.

Recently, discovering outliers among large scale web videos have attracted attention of many web mining researchers. There are number of outlier/abnormal videos exists in each category of web videos such as- ‘Entertainment’, ‘Sports’, ‘News and Politics’, etc. The task of identifying and manipulate (to remove from the web or to share with others in the web, or to watch/download from the web etc) such outlier web videos have gained significant important research aspect in the area of Web Mining Research. In this work, we propose novel methods to detect outliers from the web videos based on their metadata objects. Large scale web video metadata objects such as- length, view counts, numbers of comments, rating information are considered for outliers’ detection process. The outlier detection method –Local Outlier Factor (LOF) with different nearest neighbor values (with K=3, K=5 and K=7) are used to find abnormal/outlier web videos of same age. The resultant outliers are analyzed and compared as a step in the process of knowledge discovery.

Outliers, Lcal Outlier Factors, Inter-Quartile Range, Web Video Outliers, Clustering, YouTube

[1] Chueh-Wei Chang, Ti-Hua Yang and Yu-Yu Tsao, “Abnormal Spatial Event Detection and Video Content Searching in a Multi-Camera Surveillance System”, MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN.
[2] Fan Jiang, Ying Wu, Aggelos K. Katsaggelos, “Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering”, Northwestern University, USA.
[3] Tushar Sandhan et al., “Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns”, IEEE 28th International Conference on Image and Vision Computing, 2013- New Zealand
[4] Thi-Lan Le and Thanh-Hai Tran, “Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization”, Advances in Intelligent Systems and Computing 341, DOI 10.1007/978-3-319-14633-1_2, Springer International Publishing-2015, Switzerland.
[5] Yang Cong et al., “Abnormal Event Detection in Crowded Scenes using Sparse Representation”, Pattern Recognition, January 30, 2013
[6] Cewu Lu et al., “Abnormal Event Detection at 150 FPS in MATLAB”, The Chinese University of Hong Kong.
[7] Yang Cong et al., “Sparse Reconstruction Cost for Abnormal Event Detection”.
[8] Bin Zhao et al., “Online Detection of Unusual Events in Videos via Dynamic Sparse Coding”, 2011.
[9] Mahmoudi Sidi Ahmed et al., “Detection of Abnormal Motions in Video”, Chania ICMI-MIAUCE’08 workshop, Crete, Greece, 2008.
[10] Du Tran et al., “Video Event Detection: From Subvolume Localization to Spatiotemporal Path Search”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, 2014.
[11] Dataset for "Statistics and Social Network of YouTube Videos", http://netsg.cs.sfu.ca/youtubedata/.
[12] Siddu P Algur, Prashant Bhat, "Metadata Based Classification and Analysis of Large Scale Web Videos", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 4, Issue 3, May - June 2015 , pp. 111-120 , ISSN 2278-6856.
[13] Siddu P. Algur, Prashant Bhat, Suraj Jain, “The Role of Metadata in Web Video Mining: Issues and Perspectives”, International Journal of Engineering Sciences & Research Technology, Volume 4, Issue 2, February-2015.
[14] Chirag Shah, Charles File, “Infoextractor – A Tool for Social Media Data Mining”, JITP 2011.

Siddu P. Algur, Prashant Bhat, "Abnormal Web Video Detection Using Density Based LOF Method," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.6-14, 2016.

Proposed is an application that gives visual commands to the computer without touching the keyboard or mouse for performing certain actions. This puts together a simple laser gesture recognition application and used it to control Windows Media Player and Microsoft PowerPoint. This is far more comfortable than using a remote control because you don't have to look for the correct buttons in the dark. All you have to do is to make a few simple gestures anywhere in the camera's field of view with a laser pointer.

Laser spot detection, Gesture Recognition

[1] Aryuanto Soetedjo, Ali Mahmudi, L. L., “Detecting Laser spot in shooting simulation using an embedded camera”, Int. Journal of Smart Sensing and Intelligent Systems, Volume-07, Issue-01, March 2014.
[2] ByungMoo Jeon, “Hardware architecture for detecting laser point using FPGA”, Int. Conference on Control, Automation and Systems, Page No (199-203), October 2012.
[3] Xingyan Li, “Gesture Recognition Based on Fuzzy C-Means Clustering Algorithm”, April 2002.
[4] Jean-Francois Lapointe, Guy Godin, “On screen Laser spot detection for large display interaction”, IEEE International Workshop on Haptic Audio Visual Environments and their applications, Page No (72-76), October 2005.
[5] Darren Phi Bang Dang, “Template Based Gesture Recognition”, June 1996.
[6] Khushboo Arora, Shrutika Suri, Divya Arora and Vaishali Pandey, “Gesture Recognition Using Artificial Neural Network”, Int. Journal of Computer Sciences and Engineering, Volume-02, Issue-04, Page No (185-189), April 2014.

Umang Keniya, Sarthak Kothari, Akash Mehta, Bhakti Palkar, "Laser Gesture Recognition for Human Machine Interaction," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.15-17, 2016.

In the Data Intensive grid environment, researchers always try to avoid failures by improving the data availability with mechanism called replication. In this paper, data availability which is shown works towards efficient way of applying Object replication and its object replicas availability prediction and thus users are able to predict its dynamic replication status that decides replicas management and its performance in data grid. The proposed way of Availability prediction gives status of data availability at nodes and thus it gives data reliability information to the scheduler, in such manner our proposed scheme helps to make better jobs execution decisions with minimum jobs execution time and band width by considering account of some of the objective functions involved while data access. It is an eminent method to deal with object replicas utilization that can be improved jobs execution performance, and proposed method is an auspicious improvement over performance of replicas utilization without failures in data grid environment.

Replication, Fault tolerance, Data grid, Dynamic nature of data grid, tree-based replica location service, restrictions on security issues in grid, Resource utilization in data grid

[1] H. Lamehamedi, Z. Shentu, B. Szymanski, E. Deelman, Simulation of dynamic data replication strategies in data grids, in: Proc. 12th Heterogeneous Computing Workshop, HCW2003, Nice, France, April 2003, IEEE Computer Science Press, Los Alamitos, CA, 2003.
[2] D. Deatrich, S. Liu, C. Payne, R. Tafirout, R. Walker, A. Wong, M. Vetterli, Managing Petabyte-scale storage for the ATLAS Tier-1 centre at TRIUMF, in: 22nd International Symposium on High Performance Computing Systems and Applications, HPCS 2008, 9-11 June 2008, pp. 167-171.
[3] J. Bresnahan, M. Link, G. Khanna, Z. Imani, R. Kettimuthu, I. Foster, Globus GridFTP: What's new in 2007, in: Proceedings of the First International Conference on Networks for Grid Applications, GridNet 2007, Lyon, France, 2007.
[4] R. Slota, D. Nikolow, L. Skital, J. Kitowski, Implementation of replication methods in the Grid environment, in: Advances in Grid Computing - EGC 2005,in: Lecture Notes in Computer Science, vol. 3470/2005, 2005, pp. 474-484.
[5] P. Liu, J. Wu, Optimal replica placement strategy for hierarchical Data Grid systems, in: Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid, CCGRID 06, 2006, pp. 417-420.
[6] C.D. Nam, C. Youn, S. Jeong, E. Shim, E. Lee, E. Park, “An efficient replication scheme for data grids”, in: Proceedings 12th IEEE International Conference on Networks, ICON 2004, 2004, pp. 392-396.
[7] H. Bell, D.G. Cameron, L. Capozza, P. Millar, K. Stockinger, and F. Zini, “Evaluation of an economy-based file replication strategy for a data grid,” Proc. IEEE Int’l Symp. Cluster Computing and the Grid (CCGrid), pp. 661-668, 2003.
[8] R.S. Chang, J.S. Chang, and S.Y. Lin, “Job scheduling and data replication on data grids,” Future Generation Computer Systems, Vol. 23, Issue 7, pp. 846-860, August 2007.
[9] K. Ranganathan and I. Foster, “Computation scheduling and data replication algorithms for data Grids”, Grid resource management: state of the art and future trends, pp. 359-373, 2004.
[10] R.S. Chang and H.P. Chang, “A dynamic data replication strategy using access-weights in data grids,” Journal of Supercomputing, Vol.45, Issue 3, pp. 277 – 295, 2008.
[11] K. Sashi and A. S. Thanamani, “Dynamic replication in a data grid using a modified BHR Region Based Algorithm,” Future Generation Computer Systems, vol. 27, no. 2, pp. 202–210, 2011.
[12] N.Mansouri,” A Threshold-based Dynamic Data Replication and Parallel Job Scheduling strategy to enhance Data Grid” In Proceedings of the Cluster Computing (2014) 17:957–977.

P.Sunil Gavaskar , "An Adaptive Replication Approach for Relocation Services in Data Intensive Grid Environment," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.18-24, 2016.

In this paper we proposed that, the set of minutia factors is taken into consideration to be the maximum different feature for fingerprint illustration and is extensively used in fingerprint matching. It turned into believed that the minutia set does now not contain sufficient data to reconstruct the unique fingerprint photograph from which minutiae had been extracted. The prior know-how approximately fingerprint ridge systems is encoded in terms of orientation patch and non-stop segment patch dictionaries to improve the fingerprint reconstruction. We additionally proposed a brand new or first publicly to be had database for minor (additionally foremost) finger knuckle photographs from 503 exceptional topics. The efforts to expand an automatic minor finger knuckle sample matching scheme reap promising outcomes and illustrate its simultaneous use to seriously enhance the overall performance over the conventional finger knuckle identification.

Fingerprint Reconstruction; Knuckle Matching; Major & minor knuckle

[1] Kai Cao and Anil K. Jain, Fellow, IEEE, “Learning Fingerprint Reconstruction: From Minutiae to Image”, IEEE Transaction on information forensics and security, Vol. 10, No. 1, January 2015.
[2]Ajay Kumar, Senior Member, IEEE, “Importance of Being Unique From Finger Dorsal Patterns: Exploring Minor Finger Knuckle Patterns in Verifying Human Identities”, IEEE Transaction on information forensics and security, Vol. 09, No. 8, August 2014.
[3] D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fingerprint Recognition, 2nd ed. New York, NY, USA: Springer-Verlag, 2009.
[4] Information Technology—Biometric Data Interchange Formats—Part 2: Finger Minutiae Data, ISO/IEC Standard 19794-2:2005, 2005.
[5] FVC2002. (2002). Fingerprint Verification Competition. [Online]. Available: http://bias.csr.unibo.it/fvc2002/.
[6] C. J. Hill, “Risk of masquerade arising from the storage of biometrics,” B.S. thesis, Dept. Comput. Sci., Austral. Nat. Univ., Canberra, ACT, Australia, 2001.
[7] B. G. Sherlock and D. M. Monro, “A model for interpreting fingerprint topology,” Pattern Recognit., vol. 26, no. 7, pp. 1047–1055, 1993.
[8] A. Ross, J. Shah, and A. K. Jain, “From template to image: Reconstructing fingerprints from minutiae points,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 4, pp. 544–560, Apr. 2007.
[9] R. Cappelli, D. Maio, A. Lumini, and D. Maltoni, “Fingerprint image reconstruction from standard templates,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 9, pp. 1489–1503, Sep. 2007.
[10] J. Feng and A. K. Jain, “Fingerprint reconstruction: From minutiae to phase,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 2, pp. 209–223, Feb. 2011.
[11] S. Li and A. C. Kot, “An improved scheme for full fingerprint reconstruction,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 6, pp. 1906–1912, Dec. 2012.
[12] A. Kumar and Y. Zhou, “Personal identification using finger knuckle orientation features,” Electron. Lett., vol. 45, no. 20, pp. 1023–1025,Sep. 2009.

Kumavat Kalpesh, Jadhav Samiksha, Burkul Gauri, Mahajan Gaurav, "Verifying Human Unique Identities Using Fingerprint Reconstruction and Knuckle Recognization," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.25-29, 2016.

According to Bonald and Feuillet (2011), the management of network resources has taken on a new urgency with the highly interactive nature of modern computing and the increasing interdependence of networked applications. As a result, the monitoring of network behavior has become an integral part of management. It is critical that applications are armed with tools that can facilitate the estimation of performance and allow the selection of suitable encoding schemes, buffering sizes, and adaptation features. This paper examines the importance to assess the requirements for monitoring the network performance within a wireless environment for the processing and presentation of the statistical outcome of surveillance information. In comparison to the probing technique, which is based on the distance separating data packets, the two monitoring schemes lead to considerable traffic within the network. The proposed method will use a straightforward statistical analysis to determine variations in the network characteristics used a hybrid passive and active surveillance approach with interchangeable filters and a time window mechanism.

Buffer Size, Surveillance Information, Interchangeable Filters, Time Window Mechanism

[1] Bonald, T., & Feuillet, M. “Network performance analysis”. London, UK: ISTE 2011.
[2] George, D. K., et al “Exact-order asymptotic analysis for closed queuing networks” Journal of Applied Probability, 49(2), 2012, 503-520.
[3] Holt, A. “Network performance analysis: Using the J programming language” London, UK: Springer 2008
[4] Markl, C., & Huhn, O. (Eds.). “Evaluation of prioritization in performance models of DTP Systems” IEEE Conference on Commerce and Enterprise Computing CEC’09: Vienna, Austria: IEEE. 2009
[5] Marshall, P. “Quantitative analysis of cognitive radio and network performance” Norwood, MA: Artech House. 2010
[6] Nelson, P., & Rebelo, P. “Network performance”. New York, NY: Taylor & Francis. 2009.
[7] Perkins, D. D et al Proceedings of IEEE International Conference on Communications COC’12: Factors affecting the performance of ad hoc networks. Lafayette, LA: IEEE, 2012.
[8] Pallavi S and M. Lakshmi, “Estimation Of Burst Length in OBS Networks” Indian Journal of Computer Science and Engineering (IJCSE). Vol.5, Issue-2, 2014, pp.78-84. e-ISSN:0976-5166 p-ISSN:2231-3850.

Prathap M and Antony Selvadoss Thanamani, "Hybrid Passive and Active Surveillance Approach with Interchangeable Filters and a Time Window Mechanism for Performance Monitoring," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.30-33, 2016.

Image compression is the application of data compression on digital images. The discrete cosine transform (DCT) is a technique for converting a signal into elementary frequency components. It is widely used in image compression. Here we develop some simple functions to compute the DCT and to compress images. The discrete cosine transform (DCT) is a mathematical function that transforms digital image data from the spatial domain to the frequency domain .In this paper the lossy compression techniques have been used, where data loss cannot affect the image clarity in this area. It is also used for reducing the redundancy that is nothing but avoiding the duplicate data. It also reduces the storage area to load an image. Compression refers to reducing the quantity of data used to represent a file, image or video content without excessively reducing the quality of the original data. Image compression is the application of data compression on digital images. The main purpose of image compression is to reduce the redundancy and irrelevancy present in the image, so that it can be stored and transferred efficiently. The compressed image is represented by less number of bits compared to original. Hence, the required storage size will be reduced, consequently maximum images can be stored and it can transferred in faster way to save the time, transmission bandwidth. Depending on the compression techniques the image can be reconstructed with and without perceptual loss. In lossless compression, the reconstructed image after compression is numerically identical to the original image. In lossy compression scheme, the reconstructed image contains degradation relative to the original. Lossy technique causes image quality degradation in each compression or decompression step. In general, lossy techniques provide for greater compression ratios than lossless techniques i.e. Lossless compression gives good quality of compressed images, but yields only less compression whereas the lossy compression techniques lead to loss of data with higher compression ratio. The inverse DCT would be performed using the subset of DCT coefficients. The error image (the difference between the original and reconstructed image) would be displayed.

Image compression, DCT, QUANTIZER LPTCM

[1] Chang Sun and En-Hui Yang, “An Efficient DCT-Base Image Compression System Based on Laplacian Transparent Composite Model” IEEE transactions on image processing, vol. 24, no. 3, march 2015
[2] E.-H. Yang, X. Yu, J. Meng, and C. Sun, “Transparent composite model for DCT coefficients: Design and analysis,” IEEE Trans. Image Processing, vol. 23, no. 3, pp.1303–1316, Mar. 2014.
[4] A.M.Raid1 ,W.M.Khedr 2 , M. A. El-dosuky 1 and WesamAhmed,“Jpeg Image Compression Using Discrete Cosine Transform” - A Survey International Journal of Computer Science & Engineering Survey (IJCSES) Vol.5, No.2, April 2014
[5 ]Walaa M. Abd-Elhafiez,“New Approach for Color Image Compression” International Journal of Computer Science and Telecommunications Volume 3, Issue 4, April 2012
[6] Walaa M. Abd-Elhafiez,** WajebGharibi, “Color Image Compression Algorithm Based on the DCT Blocks”
[7] FouziDouak ,RedhaBenzid, Nabil Benoudjit,“Color image compression algorithm based on the DCT transform combined to an adaptive block scanning” Int. J. Electron. Communication 13 July 2015 (AEU ¨ )65 (2011) 16–26
[8] Nikolay N. Ponomarenko, Karen O. Egiazarian, Senior Member, IEEE, Vladimir V.Lukin, and Jaakko T.Astola, Fellow, IEEE, “High-Quality DCT-Based Image Compression Using Partition Schemes” IEEE signal processing letters, vol. 14, no. 2, february 2007.
[9] Ponomarenko, Nikolay, et al. "DCT based high quality image compression." Image Analysis. Springer Berlin Heidelberg, 2005. 1177-1185.

Sarika P. Bagal and Vishal B. Raskar, "JPEG Image Compression by Using DCT," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.34-38, 2016.

Mining supposition targets and assessment words from online surveys are essential assignments for fine-grained feeling mining[1], the key segment of which includes identifying conclusion relations among words. To this end, this paper proposes a novel methodology taking into account the halfway administered arrangement model, which sees distinguishing assessment relations as an arrangement process. At that point, a chart based co-positioning calculation is misused to evaluate the certainty of every hopeful. At last, hopefuls with higher certainty are extricated as assessment targets or conclusion words. Contrasted with past techniques taking into account the closest neighbour leads, our model catches sentiment relations all the more correctly, particularly for long-traverse relations. Contrasted with language structure based techniques, our assertion arrangement display viably eases the negative impacts of parsing mistakes when managing casual online writings. Specifically, contrasted with the customary unsupervised arrangement display, the proposed model gets better exactness in light of the use of halfway supervision. What's more, when evaluating competitor certainty, we punish higher-degree vertices in our diagram based co-positioning calculation[1] to diminish the likelihood of blunder era. Our test results on three corpora with various sizes and dialects demonstrate that our methodology viably outflanks cutting edge techniques.

Opinion Mining, Opinion Targets Extraction, Opinion Words Extraction,Ranking

[1] Kang Liu, Liheng Xu, and Jun Zhao, “Co-Extracting Opinion Targets and Opinion Words from Online Reviews Based on the Word Alignment Model”, IEEE Transactions on knowledge and data engineering, Vol. 27, NO. 3, March 2015.
[2]M. Hu and B. Liu, “Mining and summarizing customer reviews,” in Proc. 10th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Seattle, WA, USA, 2004, pp. 168–177.
[3] F. Li, S. J. Pan, O. Jin, Q. Yang, and X. Zhu, “Cross-domain coextraction of sentiment and topic lexicons,” in Proc. 50th Annu. Meeting Assoc. Computer. Linguistics, Jeju, Korea, 2012, pp. 410–419.
[4] L. Zhang, B. Liu, S. H. Lim, and E. O’Brien-Strain, “Extracting and ranking product features in opinion documents,” in Proc. 23th Int. Conf. Computer. Linguistics, Beijing, China, 2010, pp. 1462–1470.
[5] K. Liu, L. Xu, and J. Zhao, “Opinion target extraction using wordbased translation model,” in Proc. Joint Conf. Empirical Methods Natural Lang. Process. Comput. Natural Lang. Learn., Jeju, Korea, Jul. 2012, pp. 1346–1356.
[6] M. Hu and B. Liu, “Mining opinion features in customer reviews,” in Proc. 19th Nat. Conf. Artif. Intell., San Jose, CA, USA, 2004, pp. 755–760.
[7] A.-M. Popescu and O. Etzioni, “Extracting product features and opinions from reviews,” in Proc. Conf. Human Lang. Technol. Empirical Methods Natural Lang. Process., Vancouver, BC, Canada, 2005, pp. 339–346.

Rajeshwari G. and J. Nagesh Babu , "Partially Supervised Word Alignment Model for Ranking Opinion Reviews," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.39-42, 2016.

Smartphones are becoming the important devices for us in the post-PC era, which aid in our daily tasks with the useful functionalities such as Internet, GPS, cameras, NFC (Near Field Communication) and accelerometers. Millions of users are using android phones and android application is becoming more and more popular. Among all the mobile platforms available, Android has become the more targeted one. The exponential growth of the Android platform in the recent years has made it a main target of cyber-criminals. As a result, the amount of malware for Android is constant and rapidly growing. This exponential growth of malware given, there is a need for new detection models designed to specifically target Android malware in order to better protect the end-users and, eventually, to counter the rise of Android malware itself. This is probably due to the fact that Android is the most widespread platform and to some technical particularities, such as the fact that Android applications are really easy to reverse engineer and to modify/repackage. This paper focus on the survey of security threats in Android platform. We gives the survey on malware and malicious applications in Android.

Smartphone, Android, Malicious applications, Security, Malware

[1] BHAS N. Press Release: More Than 80% of Smartphones Remain Unprotected from Malware and Attacks, Juniper Research Finds [EB/OL].[2014-02-23].http://www.juniperresearch.com/viewpressrelease.php?pr=404.
[2] D. Research. (2013). Global Smartphone Shipments to Reach 1.24 Billion in 2014. [Online]. Available: http://www.digitimes.com/news/a20131125PD218.html
[3] I. D. Corporation. (2013). Android Pushes Past 80% Market Share While Windows Phone Shipments Leap 156.0% Year Over Year in the Third Quarter, According to IDC. [Online]. Available:http://www.idc.com/getdoc.jsp?containerId=prUS24442013
[4] H. Peng et al., “Using probabilistic generative models for ranking risks of Android apps,” in Proc. ACM Conf. CCS, 2012, pp. 241–252.
[5] BARRERA D, KAYACIK H G, OORSCHOT P C V, et al. A Methodology for Empirical Analysis of Permission-Based Security Models and Its Application to Android[C]//Proceedings of the 17th ACM Conference on Computer and Communications Security: Oct 4-8, 2010, Chicago, Illinois, USA: ACM, 2010: 73-84.
[6] FELT A P, GREENWOOD K, WAGNER D. TheEffectiveness of Application Permissions[C]//Proceedings of the 2nd USENIX Conference on Web Application Development: Jun 15-16, 2011, Portland, Oregon, USA: USENIX Association, 2011: 7-7.
[7] FELT A P, HA E, EGELMAN S, et al. Android Permissions: User Attention, Comprehension, and Behavior[C]//Proceedings of the 8th Symposium on Usable Privacy and Security: Jul 11-13, 2012, Washington, D.C., USA: ACM, 2012: 1-14.
[8] CHEN Y, XU H, ZHOU Y, et al. Is This App Safefor Children?: A Comparison Study of Maturity Ratings on Android and Ios Applications[C]//Proceedings of the 22nd International Conference on World Wide Web: May 13-17, 2013, Rio de Janeiro, Brazil: International World Wide Web Conferences Steering Committee, 2013: 201-212.
[9] ZHOU Y, JIANG X. Dissecting Android Malware: Characterization and Evolution[C]//Proceedings of the 2012 IEEE Symposium on Security and Privacy: May 21-23, 2012, San Francisco, California, USA: IEEE Computer Society, 2012: 95-109.
[10] B. Uscilowski. (2013). Symantac White Paper: Mobile Adware and Malware Analysis. [Online]. Available: symantec.com/content/en/us/enterprise/media/security_response/whitepapers/madware_and_malware_analysis.pdf
[11] Wei Wang, Xing Wang, Dawei Feng, Jiqiang Liu, Zhen Han, and Xiangliang Zhang, Exploring Permission-Induced Risk in Android Applications for Malicious Application Detection, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, NO. 11, NOVEMBER 2014, pp-1869-1883
[12] B. P. Sarma, N. Li, C. Gates, R. Potharaju, C. Nita-Rotaru, and I. Molloy, “Android permissions: A perspective combining risks and benefits,” in Proc. 17th ACM SACMAT, 2012, pp. 13–22.
[13] XIONG Ping, WANG Xiaofeng, NIU Wenjia, ZHU Tianqing, LI Gang, Android Malware Detection with Contrasting Permission Patterns, PROTECTING COMMUNICATIONS INFRASTRUCTURE AGAINST CYBER ATTACKS, IEEE 2014, pp-1-14
[14] R. Pandita, X. Xiao, W. Yang, W. Enck, and T. Xie, “WHYPER: Towards automating risk assessment of mobile applications,” in Proc. 22nd USENIX Secur. Symp., 2013, pp. 527–542.
[15] W. Enck, D. Octeau, P. McDaniel, and S. Chaudhuri, “A study of Android application security,” in Proc. 20th USENIX Secur. Symp., 2011,p. 21.

Vishal Kumar Gujare and Praveen Malviya, "Android Malicious apps and Malware Security: A Review," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.43-47, 2016.

In Current Trends many public places like bus stations, restaurant, malls etc. provides Wi-Fi connectivity to the users with free of cost. These public places having a device like wireless access point through which they provide service to the end users. . It is designed to utilize the existing wireless LAN infrastructure. These rogue access points (APs) expose the enterprise network to a barrage of security vulnerabilities in that they are typically connected to a network port behind the firewall. The growing acceptance of wireless local area network causes a risk of wireless security attacks. The attacker creates a malevolent access point to attract the users and perform attacks on user devices through WLAN. Malevolent access point is one of the serious threats in wireless local area network. We Study in this paper survey on recent different fake access point detection methods and identified their advantages and disadvantages.

FAP, WLAN , MITMA, Evil Twin Attatck

[1] W.wei,S.Jaiswal,J.Kurose and D.Towsley,Identifying 802.11 traffic from passive measurments using iterative Bayesian inference in Proc. IEEE INFOCOM 06,2006.
[2] L.Watkins,R.Beyah, and C. Corbett, Apassive apporach to rogue access point detection,in Proc. IEEE INFOCOM 06,2006.
[3] Active User-side Evil Twin Access Point Detection Using Statistical Techniques Chao Yang, Yimin Song, and Guofei Gu, Member, IEEE.
[4] Roth, V., Polak, W., Rieffel, E. Turner, T.,"Simple and effective defense against Evil Twin Access Points", WiSec’08, March 31–April 2, 2008, Virginia,USA, 2008.
[5] S. B. Patil, S. M. Deshmukh, Dr. Preeti Patil and Nitin Chavan, “Intrusion Detection Probability Identification in Homogeneous System of Wireless Sensor Network”, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 12 - 18, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[6] Ajay M. Patel, Dr. A. R. Patel and Ms. Hiral R. Patel, “A Comparative Analysis of Data Mining Tools for Performance Mapping of WLAN Data”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 241 - 251, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375.
[7] Sandip Patil and Vanjale S.B,”Wireless LAN Intrusion Detection and Prevention System for Malicious Access Point ”, Proceedings of the 9th INDIACom; INDIACom-2015; IEEE Conference,11-13th March ,2015
[8] Sandip Patil, Sandeep Vanjale, “Wireless LAN Intrusion Detection System (WLIDS) for Malicious Access Point:” Goa Conference IRAJ, and 13 July 2014.
[9] Sandip Patil and Sandeep Vanjale, “A Survey on Malicious Access Point Detection Methods for Wireless Local Area Network”, IJCSE (E-ISSN: 2347-2693) Vol.2, Issue 3, March 2014.

Punam Rajput and Prasad Kulkarni, "A Survey on Wireless Malevolent Access Point Detection Methods for WLAN," International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.48-50, 2016.