Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

A Review on an Efficient Ransomware Detection

Author : Avadhesh Kumar 1 D Saravanan 2

Date of Publication :8th February 2018

Abstract: Cybersecurity shields the framework from unapproved access and obliteration of information. The expectation is to give security to the framework by blocking assailants. Malware or malignant programming is any sort of program which is created with the point of doing mischief to victim’s information. Viruses, worms, Trojan steeds, Ransomware, and spyware are various kinds of malware. At the point when pernicious programming goes into the framework, it will encode the client information, erases or changes the information. This kind of programming likewise used to take the client information. Ransomware is one of the kinds of malware that was created with the goal of getting cash from the victim. When Ransomware begins executing in our framework, it will begin encoding, erasing and changing documents. The client will get an unscrambling key simply subsequent to paying the guaranteed cash. Many have discovered a few solutions for recognizing some particular Ransomware. Ransom attacks can be forestalled by giving nearer consideration to application authorization demand and by utilizing anticipation systems. Avoidance methods can help distinguish and expel Ransomware without acquiring data about Ransomware. The focal point of the paper is onransomware assaults on windows, android and different conditions. In windows ransomware, aggressors can be forestalled by checking unusual document framework and in android it tends to be identified by giving close consideration to the android manifest record

Reference :

    1. J. B. S. Christensen and N. Beuschau, “Ransomware detection and mitigation tool.”
    2. An Efficient Ransomware Detection System.”
    3. S. B. Surati and G. I. Prajapati, “A Review on Ransomware Detection & Prevention,” Int. J. Res. Sci. Innov. |, vol. IV, 2017.
    4. D. Morato, E. Berrueta, E. Magaña, and M. Izal, “Ransomware early detection by the analysis of file sharing traffic,” J. Netw. Comput. Appl., vol. 124, pp. 14–32, Dec. 2018, doi: 10.1016/j.jnca.2018.09.013.
    5. S. Alsoghyer and I. Almomani, “Ransomware Detection System for Android Applications,” Electronics, vol. 8, no. 8, p. 868, Aug. 2019, doi: 10.3390/electronics8080868.
    6. E. Pazik, “Ransomware: Attack Vectors, Mitigation and Recovery,” 2017.
    7. M. Akbanov, V. G. Vassilakis, and M. D. Logothetis, “Ransomware detection and mitigation using softwaredefined networking: The case of WannaCry,” Comput. Electr. Eng., vol. 76, pp. 111–121, 2019, doi: 10.1016/j.compeleceng.2019.03.012.
    8. S. Saxena and H. K. Soni, “Strategies for ransomware removal and prevention,” in Proceedings of the 4th IEEE International Conference on Advances in Electrical and Electronics, Information, Communication and Bio-Informatics, AEEICB 2018, 2018, doi: 10.1109/AEEICB.2018.8480941.
    9. A., M. Poriye, and V. Kumar, “Ransomware Detection And Prevention,” Int. J. Comput. Sci. Eng., vol. 6, no. 5, pp. 900–905, 2018, doi: 10.26438/ijcse/v6i5.900905.
    10. H. J. Chittooparambil, B. Shanmugam, S. Azam, K. Kannoorpatti, M. Jonkman, and G. N. Samy, “A review of ransomware families and detection methods,” in Advances in Intelligent Systems and Computing, 2019, vol. 843, pp. 588–597, doi: 10.1007/978-3-319-99007- 1_55.
    11.  Y. Solanki, “Detection and Prevention for Ransomware using Machine Learning,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 7, no. 6, pp. 632–635, 2019, doi: 10.22214/ijraset.2019.6110.
    12. Ishleen Kaur, Gagandeep Singh Narula, Ritika Wason, Vishal Jain and Anupam Baliyan, “Neuro Fuzzy— COCOMO II Model for Software Cost Estimation”, International Journal of Information Technology (BJIT), Volume 10, Issue 2, June 2018, page no. 181 to 187 having ISSN No. 2511-2104.
    13. Ishleen Kaur, Gagandeep Singh Narula, Vishal Jain, “Differential Analysis of Token Metric and Object Oriented Metrics for Fault Prediction”, International Journal of Information Technology (BJIT), Vol. 9, No. 1, Issue 17, March, 2017, page no. 93-100 having ISSN No. 2511-2104.
    14. Basant Ali Sayed Alia, Abeer Badr El Din Ahmedb, Alaa El Din Muhammad,El Ghazalic and Vishal Jain, "Incremental Learning Approach for Enhancing the Performance of Multi-Layer Perceptron for Determining the Stock Trend", International Journal of Sciences: Basic and Applied Research (IJSBAR), Jordan, page no. 15 to 23, having ISSN 2307-4531.
    15. RS Venkatesh, PK Reejeesh, S Balamurugan, S Charanyaa, “Further More Investigations on Evolution of Approaches for Cloud Security”, International Journal of Innovative Research in Computer and Communication Engineering , Vol. 3, Issue 1, January 2015
    16. K Deepika, N Naveen Prasad, S Balamurugan, S Charanyaa, “Survey on Security on Cloud Computing by Trusted Computer Strategy”, International Journal of Innovative Research in Computer and Communication Engineering, 2015
    17. P Durga, S Jeevitha, A Poomalai, M Sowmiya, S Balamurugan, “Aspect Oriented Strategy to model the Examination Management Systems”, International Journal of Innovative Research in Science, Engineering and Technology , Vol. 4, Issue 2, February 2015

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