International Journal of Information Technology

, Volume 11, Issue 4, pp 707–712 | Cite as

A modified framework to detect keyloggers using machine learning algorithm

  • Divyadev Pillai
  • Irfan SiddavatamEmail author
Original Research


Keyloggers are very dangerous programs that does the monitoring of all the activities carried in our PC. Some of the activities include capturing screenshots of all the activities performed in PC screen, recording the activity performed by browser and generating the different keystrokes of the various activities performed in our PC. These activities are difficult to be traced by any detectable softwares. Hence there is an urgent need to detect the presence of keyloggers in our system and nullify all the existing keyloggers present in PC. They do all the spying and steal all the sensitive, confidential and important information. This information could be used for harmful purposes and endanger the life of the person associated with it. This is really a grave threat to the society. We have tried to find a solution to this grave problem. We proposed a new detection technique that will help in detecting all the keyloggers present in our PC using Machine learning algorithm. The different keyloggers which are available or being installed are detected using Support Vector Machine learning algorithm. After various analysis the result has been generated and it is counter verified with some of the already available anti-keylogger tools.


Anti-keylogger Detection Keylogger Keystroke Support vector machine Windows 



Support vector machine


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  1. 1.
    Hassan NA, Hijazi R (2017) Windows security. In: Digital privacy and security using Windows, chapter 3, pp 103–122CrossRefGoogle Scholar
  2. 2.
    Bos H, Monrose F, Blanc G (2015) Physical-layer detection of hardware keyloggers: research in attacks, intrusions, and defenses. In: 18th International symposium, RAID 2015, Kyoto, Japan, pp 26–47Google Scholar
  3. 3.
    Ul Haq I, Ali S, Khan H, Khayam SA, Jha S, Sommer R, Kreibich C (2010) Bait your hook: a novel detection technique for keyloggers: recent advances in intrusion detection. In: 13th international symposium, RAID 2010, pp 198–217Google Scholar
  4. 4.
    Rédei GP (2008) Support vector machine. Encycl Genet Genom Proteom InformGoogle Scholar
  5. 5.
    Senf A, Chen X, Zhang A (2006) Comparison of one-class SVM and two-class SVM for fold recognition. In: 13th international conference on neural information processing, pp 140–149Google Scholar
  6. 6.
    Ortolani S, Giuffrida C, Crispo B (2013) Unprivileged black-box detection of user-space keyloggers. IEEE Trans Depend Secur Comput 10(1):40–52CrossRefGoogle Scholar
  7. 7.
    Alsmadi I, Burdwell R, Aleroud A, Wahbeh A, Al-Qudah M, Al-Omari A (2018) The ontology of malwares. Practical information security. Springer, Cham, pp 17–52CrossRefGoogle Scholar
  8. 8.
    Vishnani K, Pais AR, Mohandas R (2011) An in-depth analysis of the epitome of online stealth: keyloggers; and their countermeasures. In: ACC 2011, pp 10–19CrossRefGoogle Scholar
  9. 9.
    Ahmed YA, Maarof MA, Hassan FM, Abshir MM (2014) Survey of keylogger technologies. Int J Comput Sci Telecommun 5(2):31Google Scholar
  10. 10.
    Kolte M, Wadekar R, Late R, Lodha P, Bhutada S (2016) Unprivileged detection of user space keyloggers. Int J Innov Res Sci Eng Technol.
  11. 11.
    Rani PJ, Bhavani SD, Abraham A, Mauri JL, Buford JF, Suzuki J, Thampi SM (2011) Advances in computing and communications. In: First international conference, ACC 2011, pp 10–19Google Scholar
  12. 12.
    Holz T, Engelberth M, Freiling F (2009) Learning more about the underground economy: a case-study of keyloggers and dropzones. In: Proceedings of the 14th European symposium on research in computer security, pp 1–18Google Scholar
  13. 13.
    Zhuang L, Zhou F, Tygar JD (2009) Keyboard acoustic emanations revisited. ACM Trans Inf Syst Secur 13(1):1–26CrossRefGoogle Scholar
  14. 14.
    Canteaut A, van Tilborg HCA, Jajodia S (2011) Keylogging. Encycl Cryptogr Secur:22–59Google Scholar
  15. 15.
    Vidhate DA, Kulkarni P (2018) Improved decision making in multiagent system for diagnostic application using cooperative learning algorithms. Int J Inf Technol 10(2):201–209Google Scholar
  16. 16.
    Chandra MA, Bedi SS (2017) Survey on SVM and their application in image classification. Int J Inf Technol:1–11Google Scholar
  17. 17.
    Barman D, Chowdhury N (2016) A novel semi supervised approach for text classification. Int J Inf Technol:1–11Google Scholar
  18. 18.
    Zafar S (2017) Cyber secure corroboration through CIB approach. Int J Inf Technol 9(2):167–175MathSciNetGoogle Scholar
  19. 19.
    Tulsani H, Chawla P, Gupta R (2017) A novel steganographic model for securing binary images. Int J Inf Technol 9(3):273–280Google Scholar
  20. 20.
    Ali TO, Awadelseed OS, Eldewahi AE (2016) Random multiple layouts. In: 2016 conference of basic sciences and engineering studies (SGCAC), IEEEGoogle Scholar

Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2018

Authors and Affiliations

  1. 1.Department of Information SecurityK.J. Somaiya College of EngineeringMumbaiIndia

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