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i-manager's Journal on Computer Science (JCOM)

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Evaluating Hybrid Model Performance in Predicting the Impact of Lockdown on Controlling Covid-19 Cases Progression in Middle Income Settings
A Method for the Identification of Denial of Service (DoS) Attack in Network Traffic using Machine Learning Techniques
Agriculture Management System
Unveiling Sentiment Analysis: A Comparative Study of LSTM and Logistic Regression Models with XAI Insights
Resume Screener System

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Volume 5 Issue 2 June - August 2017

Article

A Classification of DDoS Attacks and its Approach for Attack Prevention

M. Chaitanya Kishore Reddi* , Sunil Kumawath, T. Gowtham Sai Krishna*, T.M. Sneha****

* Assistant Professor, Department of Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, India.

-** Scholar, Department of Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, India.

Reddy, M.C.K., Kumawath, S., Krishna, T.G.S., and Sneha, T.M. (2017). A Classification of DDoS Attacks and its Approach for Attack Prevention. i-manager’s Journal on Computer Science, 5(2), 1-7. https://doi.org/10.26634/jcom.5.2.13903

Abstract

Currently, distributed denial of service attack (DDoS) is a very serious threat in the internet. Large number of packets are send to a victim to jam the traffic so that the attacker can use the data of the victim. Various attack methods, its mechanisms, flooding attacks etc (Mirkovic and Reiher, 2005) are briefed in this paper. The scopes of DDOS attacks, measures and solutions to the attacks are hereby explored with effectiveness in various attack scenarios. A Distributed Denial-of-Service (DDoS) attack is an attack where the perpetrator uses more than one unique IP address, often thousands of them. Attacks may involve forging sender's Internet Protocol (IP) addresses (IP address spoofing) as an alternative or augmentation of DDoS, making it difficult to identify and defeat the attack. For specific targeted purposes, including disrupting transactions and accessing databases, an application layer DDoS attack is done, which requires less resource and often accompanies network layer attacks.

References

[1]. Alomari, E., Manickam, S., Gupta, B. B., Karuppayah, S., & Alfaris, R. (2012). Botnet-based Distributed Denial of Service (DDoS) Attacks on Web servers: Classification and Art. Cornel University Library.

[2]. Bellovin, S. M. & Gont, F. (2012). Defending against sequence number attacks. RFC 1948.

[3]. Bhuyan, M. H., Kashyap, H. J., Bhattacharyya, D. K., & Kalita, J. K. (2013). Detecting distributed denial of service attacks: methods, tools and future directions. The Computer Journal, 57(4), 537-556.

[4]. Chang, R. K. (2002). Defending against floodingbased distributed denial-of-service attacks: a tutorial. IEEE Communications Magazine, 40(10), 42-51.

[5].Cohen, M. L. & Kuykendall, D. A. (2011). Prevention of distributed denial of service attacks. U.S. Patent Application No. 12/889, 322.

[6]. Dittrich, D. (1999). The DoS Project's 'trinoo' distributed denial of service attack tool

[7]. Ferguson, P. & Senie, D. (2000). Network Ingress Filtering: Defeating Denial of service Attacks which Employ IP Source Address Spoofing. RFC 2827.

[8]. Garg, A. & Reddy, A. L. N. (2002). Mitigation of DoS attacks through QoS Regulation. In Proceedings of IWQOS workshop.

[9]. Gavrilis, D. & Dermatas, E. (2005). Real-time detection of distributed denial-of-service attacks using RBF networks and statistical features. Computer Networks, 48(2), 235- 245.

[10]. Hashmi, M. J., Saxena, M., & Saini, R. (2012). Classification of DDoS attacks and their defense techniques using intrusion prevention system. International Journal of Computer Science & Communication Networks, 2(5), 607-614.

[11]. Iumatti-Lodewyk, V. (2015). A meta-synthesis of micro facial expression literature and the legal system (Doctoral dissertation, Alaska Pacific University).

[12]. Lau, F., Rubin, S. H., Smith, M. H., & Trajkovic, L. (2000). Distributed denial of service attacks. In Systems, Man, and Cybernetics, 2000 IEEE International Conference on (Vol. 3, pp. 2275-2280). IEEE.

[13]. Leiwo, J. & Zheng, Y. (1997, July). A method to implement a denial of service protection base. In Australasian Conference on Information Security and Privacy (pp. 90-101). Springer, Berlin, Heidelberg.

[14]. Mirkovic, J. & Reiher, P. (2004). A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Computer Communication Review, 34(2), 39-53.

[15]. Mirkovic, J. & Reiher, P. (2005). D-WARD: a sourceend defense against flooding denial-of-service attacks. IEEE transactions on Dependable and Secure Computing, 2(3), 216-232.

[16]. Paxson, V. (2001). An analysis of using reflectors for distributed denial-of-service attacks. ACM SIGCOMM Computer Communication Review, 31(3), 38-47.

[17]. Peng, T., Leckie, C., & Ramamohanarao, K. (2007). Sur vey of network-based defense mechanisms countering the DoS and DDoS problems. ACM Computing Surveys (CSUR), 39(1), 3.

[18]. Prier, G. K. (2003). IDward: Implementing DWard on the IXP (Doctoral dissertation, University of California, Los Angeles).

[19]. Solms, R. V. & Niekerk, J. V. (2013). From information security to cyber security. Computers & Security, 38, 97- 102.

[20]. Spatscheck, O. & Peterson, L. L. (1999, February). Defending against denial of service attacks in Scout. In OSDI (Vol. 99, pp. 59-72).

[21]. Zargar, S. T., Joshi, J., & Tipper, D. (2013). A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Communications Surveys & Tutorials, 15(4), 2046-2069.

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Research Paper

Smart Helmet and Fingerprint Based Ignition System for Vehicles

Jyoti Prasad* , Jyoti Samota, Suruchi Rajput*, Tripti Banjare**, Ankit Singh*, Dharmendra Singh****

*-** B.E. Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

*-**** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Prasad, J., Samota, J., Rajput, S., Banjare, T., Singh, A., Singh, D. (2017). Smart Helmet and Fingerprint Based Ignition System for Vehicles. i-manager’s Journal on Computer Science, 5(2), 8-11. https://doi.org/10.26634/jcom.5.2.13904

Abstract

In this era, Human identification and authentication play a very important role in human life, since the fingerprint is unique for all human beings. It helps the person to identify or secure the system. In this paper, a method is proposed by which a vehicle can be ignited using a helmet or fingerprint of the user. Nowadays, as the injuries and the death rate due to accidents are increasing, an automated helmet based system is designed through which the driver will not be able to start the motorcycle engine without a helmet. Also, to overcome the theft activity fingerprint module is used to start the vehicle.

References

[1]. Amusa, K. A., Nuga, O. O., & Adetomi, A. A. (2012). Design of SMS enabled car security system. Transnational Journal of Science and Technology, 2.

[2]. Arora, R. & Kumar, K. (2015). Start-Up The Engine Using Fingerprinting. International Journal of Computer Engineering and Applications, 9(10), 21-24.

[3]. Bhatt, B., Kalani, P., Parmar, N., & Shingala, N. (2015). Smart Vehicle Security System Using GSM & GPS. International Journal of Engineering and Computer Science, 4(06).

[4]. Farina, A., Kovacs-Vajna, Z. M., & Leone, A. (1999). Fingerprint minutiae extraction from skeletonized binary images. Pattern Recognition, 32(5), 877-889.

[5]. Gill, K. & Raheja, S. (2015). Robotic Path Planning – Using Fuzzy Inference System. IEEE Conference on International Computing, Control, and Computation Automation, 15- 16.

[6]. Hong, L. (1998). Automatic personal identification using fingerprints. Michigan State University.

[7]. Jain, A., Hong, L., & Bolle, R. (1997). On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4), 302-314.

[8]. Maio, D. & Maltoni, D. (1997). Direct gray-scale minutiae detection in fingerprints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(1), 27-40.

[9]. Prashantkumar, R., Sagar, V. C., Santosh, S., & Nambiar, S. (2013). Two wheeler vehicle security system. International Journal of Engineering Sciences & Emerging Technologies, 6(3), 324-334.

[10]. Ratha, N. K., Chen, S., & Jain, A. K. (1995). Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recognition, 28(11), 1657-1672.

[11]. Saxena, A., Sharma, S., Gaur, S., Chauhan, S., Varshney, S. (n.d). Ignition Based on Fingerprint Recognition. International Journal of Scientific Research and Management Studies (IJSRMS), 2(1), 66-71.

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Research Paper

Touch Screen Based Food Ordering System for Restaurants

Charu Verma* , Priya Yadav, Pooja Samrit, Supreeti Karashi, Dharmendra Singh

- B.E. Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Verma, C., Yadav, P., Samrit, P., Karashi, S., and Singh, D. (2017). Touch Screen Based Food Ordering System for Restaurants. i-manager’s Journal on Computer Science, 5(2), 12-16. https://doi.org/10.26634/jcom.5.2.13905

Abstract

Automation has become a very important part of human life. Internet of things (IoT) has brought a drastic change in the daily life of human beings. Earlier the food industry was lagging behind all other industries. Nowadays, the adoption of wireless technologies and emergence of mobile devices has led to automation in the food industry. The food industry also increases business opportunity; the automated food ordering system for any restaurant will attract people. In this paper, the manual menu system is replaced by an automated touch screen based menu system. In this system, the input module is the touch screen sensor which is placed on Graphical Liquid Crystal Display (GLCD) which is used to get the input from the user, the second module, is the microcontroller which accepts the order from the input module, and the output module is the ZigBee module which is used to communicate between the user table and the receiver end.

References

[1]. Bhalla, M. R. & Bhalla, A. V. (2010). Comparative study of various touchscreen technologies. International Journal of Computer Applications, 6(8), 12-18.

[2]. Bhanusiramshetti, P. R. & Bhavika, K. (2013). Zigbee Based E-Menu Ordering System. International Journal of Science, Engineering and Technology Research, 2(10), 1922-1925.

[3]. Chauhan, J. S. & Semwal, S. (2013). Microcontroller based speed control of DC geared motor through RS-232 interface with PC. International Journal of Engineering Research and Applications (IJERA), 3(1), 778-783.

[4]. Dambhare, S. S., Parjane, M. A., & Deore, D. M. (2012). Touch Screen based menu ordering and displaying system for restaurants. International Journal of Computer Engineering and Technology, 3(2), 297-307.

[5]. Liu, C. L. & Layland, J. W. (1973). Scheduling algorithms for multiprogramming in a hard-real-time environment. Journal of the ACM (JACM), 20(1), 46-61.

[6]. Rove, J. J. (n.d). A study on Touch Screen. System-level solutions Lab Manual.

[7]. Sahoo, S. K., Sultana, W. R., & Mallika, S. (2010). Touch screen based speed control of single phase induction motor. International Journal of Engineering and Technology, 2(6), 392-396.

[8]. Sridhar, V. (2012). Image based password authentication for illiterates with touchscreen. International Journal of Science, Engineering and Technology Research, 1(3), 18.

[9]. Zhou, H. & Guangwen Wu, (2016). The programming based on the embedded real-time operating system. Beijing: Beihang University Press.

[10]. ZigBee Specification, (2004). ZigBee Alliance Document 053474r06. Version, 1, 378.

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Research Paper

Vehicle Tracking and Anti-Theft System

G. Ravi Kumar* , Ch. Srinivasa Reddy, Prasanthi Kalavala Gowthami*, G. Sunita****

* -* Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India.

** UG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.

Kumar, G.R., Srinivasareddy, Ch., Prasanthi, K.G., and Sunita, G. (2017). Vehicle Tracking and Anti-Theft System. i-manager’s Journal on Computer Science, 5(2), 17-22. https://doi.org/10.26634/jcom.5.2.13906

Abstract

A major problem today for vehicle owners is that they are in constant fear of having their vehicles stolen from a common parking lot or from outside their home. Currently, most of the people are having their own vehicles. Stolen in parking lots and sometimes while driving in insecure places. The safety of vehicles is extremely essential for public. The main theme of this paper is Vehicle tracking which basically concentrates on tracking the vehicle location at the time of vehicle theft. In order to prevent vehicle theft an ignition controlling technique has been used. When the owner removes the key from the ignition lock, the system turns ON. When a person tries to steal the vehicle, an alert will be sent to the owner (authorized person) in the form of message by GSM (Global System for Mobile Communications) and GPS (Global Positioning System) technologies. When vibrations are detected, SMS is send to owner of the vehicle, so that the owner in return can send a ‘stop’ message.

References

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Research Paper

A Comparative Analysis of Fuzzy Based Load Balancing Algorithms

Neeraj Kumar Rathore* , Pramod Kumar Singh**

*-** Assistant Professor, Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, India.

Rathore, N., and Singh, P.K. (2017). A Comparative Analysis of Fuzzy Based Load Balancing Algorithms. i-manager’s Journal on Computer Science, 5(2), 23-33. https://doi.org/10.26634/jcom.5.2.13907

Abstract

A distributed system is a network of physically separated computing resources that are connected by some means of communication medium. It is a communication intensive system, where each system sends and receives some messages to keep itself updated with the current system status. It provides various advantages such as good resource sharing, reliability, extensibility and good performance. In distributed system, there is a chance that some processors of the system are overloaded; at the same time some processors are idle or underloaded, and hence the performance of the system is degraded. The Load Balancing Algorithm aims (LBA) to have all processing elements to equal workloads over the long term. The performance of a distributed system heavily depends on the performance of Load Balancing Algorithm employed to it. Load Balancing Algorithms work on different matrices such as rate of resource utilization, communication overhead, reliability, scalability, stability, and fault tolerance. LBA helps in optimal utilization of resources in distributed system.

References

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[7]. Jain, N., Rathore, N.K., and Mishra, A. (2017). “An Efficient Image Forgery Detection Using Biorthogonal Wavelet Transform and Improved Relevance Vector Machine With Some Attacks ", Interciencia Journal, 42 (11), 95-120.

[8]. Jain, N., Rathore, N.K., and Mishra, A. (2017). “An Efficient Image Forgery Detection Using Biorthogonal Wavelet Transform and Singular Value Decomposition " In th 5 International conference on Advance Research Applied Science, Environment, Agriculture & Entrepreneurship Development (ARASEAED), Bhopal organized & sponsored by janparishad, JMBVSS & International Council of people at Bhopal (M.P.) India, pp 274-281.

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Survey Paper

Comparison of Programming Languages for Human Gender Classification

G.D.K.Kishore* , Babu Reddy Mukkamalla**

* Research Scholar, Department of Computer Science, Krishna University, Andhra Pradesh, India.

** Assistant Professor and Head, Department of Computer Science, Krishna University, Andhra Pradesh, India.

Kishore, G. D. K., and Reddy, M.B. (2017). Comparison of Programming Languages for Human Gender Classification. i-manager’s Journal on Computer Science, 5(2), 34-39. https://doi.org/10.26634/jcom.5.2.13908

Abstract

Nowadays, the investigation of Human gender classification (Yong et al., 2012; Mahmood et al., 2012) is capable and powerful, of predicting human gender by utilizing statistical tools and methods. There are a few numerical computational tools that fill in as instructive tools and are likewise accessible for business use to contrast different tools. In this paper, statistical tools like Python, R and MATLAB along with their advantages, working environment, features and challenges are discussed. Here, a review on the most important software tools for image classification applications is presented. Additionally, which software tool is best for beginning learners to learn image learn classification with accurate results and for analysing output results perfectly are also discussed.

References

[1]. Muenchen. R. (2017). Data Science Job Report 2017: R Passes SAS, But Python Leaves Them Both Behind [Blog post]. Retrieved from http://r4stats.com/2017/02/28/rpasses- sas/

[2]. The R Foundation. (2017). The R Project for Statistical Computing. Retrieved from https://www.r-project.org/

[3]. Python vs. Matlab. (pizo.org). Pyzo. Retrieved from http://www.pyzo.org/python_vs_matlab.html

[4]. Revolution Analytics. (2014, February 20). What is R? Retreived from http://www.inside-r.org/what-is-r, February 20, 2014.

[5]. Yong, C. Y., Chew, K. M., Mahmood, N. H., Ariffin, I. (2012). A Survey of Image Processing Tools Package in Medical Imaging. International Conference on Teaching and Learning & In Higher Education in conjunction with Regional Conference on Engineering Education & Research in Higher Education (RCEERHed 2012).

[6]. Mahmood, N. H., Yong, C. Y., Chew, K. M., & Ariffin, I. (2012). Image Processing Software Package in Medical Imaging: A review. International Journal of Computational Engineering Research (IJCER), 2(1), 199-203.

[7]. Ozgur, C., Hall, U., Colliau, T., Rogers, G., & Hughes, Z. (2017). MatLab vs. Python vs. R. Journal of Data Science, 15(3), 355-372.

[8]. Eglen SJ (2009). A Quick Guide to Teaching R Programming to Computational Biology Students. PLoS Comput, Biol., 5(8): e1000482. https://doi.org/10.1371/ journal.pcbi.1000482.

[9]. Feldman, P. M. (2015). Eight Advantages of Python Over Matlab. Retrieved from http://phillipmfeldman.org/ Python/Advantages_of_Python_Over_Matlab.html

[10]. Guo, P. (2014). Python is Now the Most Popular Introductory Teaching Language at Top U.S. Universities. Retrieved from http://cacm.acm.org/blogs/blogc a cm/ 1 7 6 4 5 0 - p y t h o n - i s - n o w-t h e -mo s t- p o p u l a ri n t r o d u c t o r y t e a c h i n g - l a n g u a g e - a t - t o p - u s - universities/fulltext

[11]. Guo, P. (2007). Why Python is a great language for teaching beginners in introductory programming classes. Retrieved from http://pgbovine.net/pythonteaching. htm

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i-manager's Journal on Computer Science (JCOM)

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Volume 5 Issue 2 June - August 2017

A Classification of DDoS Attacks and its Approach for Attack Prevention

M. Chaitanya Kishore Reddi* , Sunil Kumawath**, T. Gowtham Sai Krishna***, T.M. Sneha****

* Assistant Professor, Department of Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, India. **-**** Scholar, Department of Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, India.

Reddy, M.C.K., Kumawath, S., Krishna, T.G.S., and Sneha, T.M. (2017). A Classification of DDoS Attacks and its Approach for Attack Prevention. i-manager’s Journal on Computer Science, 5(2), 1-7. https://doi.org/10.26634/jcom.5.2.13903

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Smart Helmet and Fingerprint Based Ignition System for Vehicles

Jyoti Prasad* , Jyoti Samota**, Suruchi Rajput***, Tripti Banjare****, Ankit Singh*****, Dharmendra Singh******

*-**** B.E. Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India. *****-****** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Prasad, J., Samota, J., Rajput, S., Banjare, T., Singh, A., Singh, D. (2017). Smart Helmet and Fingerprint Based Ignition System for Vehicles. i-manager’s Journal on Computer Science, 5(2), 8-11. https://doi.org/10.26634/jcom.5.2.13904

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Touch Screen Based Food Ordering System for Restaurants

Charu Verma* , Priya Yadav**, Pooja Samrit***, Supreeti Karashi****, Dharmendra Singh*****

*-**** B.E. Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India. ***** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Verma, C., Yadav, P., Samrit, P., Karashi, S., and Singh, D. (2017). Touch Screen Based Food Ordering System for Restaurants. i-manager’s Journal on Computer Science, 5(2), 12-16. https://doi.org/10.26634/jcom.5.2.13905

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Vehicle Tracking and Anti-Theft System

G. Ravi Kumar* , Ch. Srinivasa Reddy**, Prasanthi Kalavala Gowthami***, G. Sunita****

* -*** Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India. **** UG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.

Kumar, G.R., Srinivasareddy, Ch., Prasanthi, K.G., and Sunita, G. (2017). Vehicle Tracking and Anti-Theft System. i-manager’s Journal on Computer Science, 5(2), 17-22. https://doi.org/10.26634/jcom.5.2.13906

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A Comparative Analysis of Fuzzy Based Load Balancing Algorithms

Neeraj Kumar Rathore* , Pramod Kumar Singh**

*-** Assistant Professor, Department of Computer Science & Engineering, Jaypee University of Engineering & Technology, Guna, India.

Rathore, N., and Singh, P.K. (2017). A Comparative Analysis of Fuzzy Based Load Balancing Algorithms. i-manager’s Journal on Computer Science, 5(2), 23-33. https://doi.org/10.26634/jcom.5.2.13907

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Comparison of Programming Languages for Human Gender Classification

G.D.K.Kishore* , Babu Reddy Mukkamalla**

* Research Scholar, Department of Computer Science, Krishna University, Andhra Pradesh, India. ** Assistant Professor and Head, Department of Computer Science, Krishna University, Andhra Pradesh, India.

Kishore, G. D. K., and Reddy, M.B. (2017). Comparison of Programming Languages for Human Gender Classification. i-manager’s Journal on Computer Science, 5(2), 34-39. https://doi.org/10.26634/jcom.5.2.13908

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M. Chaitanya Kishore Reddi* , Sunil Kumawath, T. Gowtham Sai Krishna*, T.M. Sneha****

* Assistant Professor, Department of Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, India.

-** Scholar, Department of Computer Science and Engineering, St. Peter’s Engineering College, Hyderabad, India.

Jyoti Prasad* , Jyoti Samota, Suruchi Rajput*, Tripti Banjare**, Ankit Singh*, Dharmendra Singh****

*-** B.E. Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

*-**** Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Charu Verma* , Priya Yadav, Pooja Samrit, Supreeti Karashi, Dharmendra Singh

- B.E. Scholar, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

Assistant Professor, Department of Electronics and Telecommunication, SSIPMT, Raipur, India.

G. Ravi Kumar* , Ch. Srinivasa Reddy, Prasanthi Kalavala Gowthami*, G. Sunita****

* -* Assistant Professor, Vignan's Institute of Information Technology, Andhra Pradesh, India.

** UG Scholar, Vignan's Institute of Information Technology, Andhra Pradesh, India.

* Research Scholar, Department of Computer Science, Krishna University, Andhra Pradesh, India.

** Assistant Professor and Head, Department of Computer Science, Krishna University, Andhra Pradesh, India.