Refine your search
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Gokul, S.
- A Procedural Framework to Design and Fabrication Controlled by Pneumatics
Abstract Views :185 |
PDF Views:0
Authors
Affiliations
1 Department of Mechanical Engineering, Velammal Institute of Technology, Chennai-601204, IN
1 Department of Mechanical Engineering, Velammal Institute of Technology, Chennai-601204, IN
Source
International Journal of Engineering Research, Vol 6, No 12 (2017), Pagination: 513-518Abstract
This project deals with design development and fabrication of “MULTI PURPOSE PNEUMATIC MACHINE”. This machine is designed for the purpose of multi-operation i.e. Grinding, punching, and cutting. This machine performs multipurpose operation at same time with required speed and this machine is automatic which is controlled or operated by pneumatic pressure. This model of multi-operational machine may be used in industry and domestic operation which can perform mechanical operation like grinding, punching, and cutting of a thin metallic as well as wooden model or body. A high pressure compressed air is forced on a fan and the fan is made to rotate. This rotation is transmitted to the machining head by a shaft and the required operation is carried out.References
- i. Yang, “Pneumatic Punching Machine” Feb11 2015 CN104339690 A.
- ii. Pan Lig Steel “Pneumatic Cutting Machine” July 13 2013 CN203076688, Zhejiang Feiferring Wafer Pneumatic Tool Co.Ltd.
- iii. Wu Zhe “Pneumatic Grinding Machine”, April 16 2014.
- iv. Markur Augsburg “Chamfering Of Bevel Gear “Mar 21 2013, CN103889628A.
- v. Schunck Richard,”minimizing gearbox noise inside and otside the box”, Motion System Design.
- vi. khurmi r s,(2014) “ A textbook of machine design” Eurasia publishing house(p)ltd., New-Delhi, ISBN 9788121925372.
- vii. Mahadevan k and Reddy k.Balaveera, (2015), “ Design data hand book” cbs publishers and distributors (p) ltd., New-Delhi, ISBN 9788123923154.
- viii. Adithan, M.; Gupta, A.B.(2002), Manufacturing Technology,New Age International Publisher, ISBN 978-81-224-0817-1.
- ix. Matthew , Sam.” The basics of abrasive cutting” Retrieved 17 December 2016.
- x. Todd, Robert H., Dell K. Aleen,And Leo Alting. Manufacturing processes reference guide. New York: Industrial Press inc. 1994.pg 107.
- xi. Kalpakjian, Serope; Schmid, Steven R (2006).Manufacturing Engineering And Technology (5th edition ed) p.428.
- xii. Peter Ulintz,Hole Extrusions-part1 metal forming magazine, oct 2011.2017-01-12.
- xiii. Stephenson, David A; Agapiopu , Jhon S. (1997), metal cutting theory and practice, Markcel Dekker,P.164, ISBN 978-0-8247-9579-5.
- Analysis of Phishing Detection Using Logistic Regression and Random Forest
Abstract Views :227 |
PDF Views:0
Authors
S. Gokul
1,
P. K. Nizar Banu
1
Affiliations
1 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, IN
1 Department of Computer Science, CHRIST (Deemed to be University), Bangalore, Karnataka, IN
Source
Journal of Applied Information Science, Vol 8, No 1&2 (2020), Pagination: 07-13Abstract
In the present era of technology, cybercriminals are getting smarter day by day. From the past few years, the cybercrimes have increased to an extent that most of the big companies are finding it difficult to prevent cybercrimes. One such cyber-attack is phishing where the victims are lured in entering their sensitive information like usernames, passwords, bank details, etc. It’s very easy for an attacker to get sensitive information through phishing. The attacker should know some information about the victim’s profile so that the victims can be easily tricked. A phished URL that the victims receive is very tough to differentiate as looks similar to the original URL. In this paper, we have made use of the information in the URL to determine if the URL is phished or not. So, it is not necessary for the user to enter the website and expose themselves to the malicious code. We have also discussed the metadata that is present in the URL. In this paper, we also make use of metadata to classify a URL. Random forest and logistic regression are the two algorithms used to classify the URL present in the dataset as phished or not phished. After using the classification algorithm on the given datasets, we found that the random forest algorithm has better accuracy in classifying if a URL is legit.Keywords
Classification, Cyber Attack, Logistic Regression, Phishing, Random Forest, URL Phishing.References
- T. Dakpa, and P. Augustine, “Study of phishing attacks and preventions,” International Journal of Computer Applications, vol. 163, no. 2, pp. 5-8, April 2017.
- R. G. Brody, E. V. Mulig, and V. Kimball, “Phishing, pharming and identity theft,” Academy of Accounting and Financial Studies Journal, vol. 11, no. 3, pp. 43-56, 2007.
- A. Mahalakshmi, N. S. Goud, and G. V. Murthy, “A survey on phishing and it’s detection techniques based on support vector method (SVM) and software defined networking (SDN),” International Journal of Engineering and Advanced Technology, vol. 8, no. 2s, pp. 498-503, December 2018.
- H. Thakur, and S. Kaur, “A survey paper on phishing detection,” International Journal of Advanced Research in Computer Science, vol. 7, no. 4, pp. 64-68, January 2017.
- R. B. Basnet, and A. H. Sung, “Mining web to detect phishing URLs,” 2012 11th International Conference on Machine Learning and Applications, Boca Raton, FL, 2012.
- S. Jagadeesan, A. Chaturvedi, and S. Kumar, “URL phishing analysis using random forest,” International Journal of Pure and Applied Mathematics, vol. 118, no. 20, pp. 4159-4163, 2018.
- D. N. Pande, and P. S. Voditel, “Spear phishing: Diagnosing attack paradigm,” International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp. 2720-2724, 2017.
- A. Subasi, E. Molah, F. Almkallawi, and T. J. Chaudhery, “Intelligent phishing website detection using random forest classifier,” 2017 International Conference on Electrical and Computing Technologies and Applications (ICECTA), Ras Al Khaimah, pp. 1-5, 2017.
- J. Wang, T. Herath, R. Chen, A. Vishwanath, and H. R. Rao, “Research article phishing susceptibility: An investigation into the processing of a targeted spear phishing email,” IEEE Transactions on Professional Communication, vol. 55, no. 4, pp. 345-362, December 2012.
- C. Lin, C. Tien, C. Chen, C. Tien, and H. Pao, “Efficient spear-phishing threat detection using hypervisor monitor,” International Carnahan Conference on Security Technology (ICCST), Taipei, pp. 299-303, 2015.
- UCI Machine Learning Repository. [Online]. Available: https://archive.ics.uci.edu/ml
- J. C. S. Fatt, C. K. Leng, and S. S. Nah, “Phishdentity: Leverage website favicon to offset polymorphic phishing website,” 2014 Ninth International Conference on Availability, Reliability and Security, Fribourg, 2014.
- T. Ayodele, “Types of machine learning algorithms,” New Advances in Machine Learning, 2010.
- M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: A literature survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2091-2121, Fourth Quarter 2013.
- N. Stembert, A. Padmos, M. S. Bargh, S. Choenni, and F. Jansen, “A study of preventing email (Spear) phishing by enabling human intelligence,” 2015 European Intelligence and Security Informatics Conference, Manchester, 2015.
- http://dataaspirant.com/2017/05/22/random-forest-algorithm -machine-learing/