Journal of Applied Information Science

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Artificial Neural Network Based Approach for Identification of Operating System Processes


Affiliations
1 Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
     

   Subscribe/Renew Journal


A computer system can be secured by using various methods like firewalls, anti-virus tools, network security tools, malware removal tools, monitoring tools etc. These tools and applications are being by most of the computer users. These computer security tools need to be updated and monitored regularly by the user. If any computer users fail to update the security tools, then the computer system may be infected by virus or may be attacked. Through this paper a learning system is being proposed to provide security by identify the operating system process as Self and Non-Self. Concepts of Artificial Neural Network (ANN) Learning have been used for the identification of processes. Initially, an Artificial Neural Network is created by using processes parameters with random weights. These weights are updated by using Gradient Descent Algorithm for various training examples, and then this Artificial Neural Network is tested with test data examples. It has been observed that the Artificial Neural Network Learning provides a better approach for identifying Self and Non-Self process and provides a better security.

Keywords

Self and Non Self Process, Machine Learning, Artificial Neural Network, Gradient Descent, Perceptron.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Percus, J. K., Percus, O. E., & Perelson, A. S. (1992). Probability of self-non self discrimination. Theoretical and Experimental Insights into Immunology, 66, 63-70.

  • Forrest, S., Hofmeyr, S. A., Somayaji, A. B., & Longstaff, T. A. (1996). A Sense of Self for UNIX Processes. Proceedings of IEEE Symposium on Computer Security and Privacy. Retrieved from http://www.cs.unm.edu/∼immsec/publications/ieeesp96-unix.pdf

  • Forrest, S., & Perelson, A. S. (1994). Self Non-Self Discrimination in a Computer. In Proceedings of the IEEE Symposium on Research in Security and Privacy. Retrieved from http://www.cs.unm.edu/∼immsec/publications/virus.pdf

  • Solms, R. V., & Niekerk, J. V. (2013). From information security to cyber security. Elsevier's Computer & Security, October, 38, 97-102.

  • Yang, C. Q. (2003). Operating System Security and Secure Operating Systems. Global Information Assurance Certification Paper. Retrieved from http://www.giac.org/paper/gsec/2776/operating-systemsecuritysecure-operating-systems/104723

  • http://www.cyberwarzone.com/massive-cyber-security-tools-list-2013

  • Mitchell, T. M. (1997). Machine Learning. McGrawHill International Editions, Computer Science Series.

  • http://www.nirsoft.net/utils/cprocess.html

  • Haoyong, L., & Tang, H. (2011). Machine Learning Methods and their Application Research. IEEE International Symposium on Intelligence Information Processing and Trusted Computing, (pp. 108-110).

  • Hua, W., Cuiqin, M. A., & Lijuan, Z. (2009). A Brief Review of Machine Learning and its Application. IEEE Information Engineering and Computer Science (pp. 1-4).

  • Nguyen, D. H., & Widrow, B. (1990). Neural Networks for Self-Learning Control System. IEEE Control System Magazine (pp. 18-23).

  • Zhang, G. P. (2000). Neural Networks for Classification: A Survey. IEEE Transaction on System, Man and Cybernetics-Part C: Applications and Reviews, 30(4), 451-462.

  • Baesens, B., & Bouboulis, P. (2012). Neural Networks and Learning Systems Come Together. IEEE Transactions on Neural Networks, 23(1), 1-6.

  • Mandic, D. P. (2004). A Generalized Normalized Gradient Descent Algorithm. IEEE Signal Processing Letters, 11(2), 155-118.

  • Ahmad, F., & Isa, N. A. M. (2010). Performance Comparison of Gradient Descent and Genetic Algorithm Based Artificial Neural Networks Training. 10th International Conference on Intelligent Systems Design and Applications (pp. 604-609).

  • Xu, D., Li, Z., Wu, W., Ding, X., & Qu, D. (2007). Convergence of Gradient Descent Algorithm for Diagonal Recurrent Neural Networks, Bio-Inspired Computing: Theories and Applications, (pp. 29-31).

  • Watterson, J. W. (1990). An Optimum Multilayer Perceptron Neural Receiver for Signal Detection. IEEE Transactions on Neural Network, 1(4), 280-300.

  • Pal, S. K., & Mitra, S. (1992). Multilayer Perceptron, Fuzzy Sets and Classification. IEEE Transaction on Neural Networks, 3(5), 683-697.


Abstract Views: 366

PDF Views: 0




  • Artificial Neural Network Based Approach for Identification of Operating System Processes

Abstract Views: 366  |  PDF Views: 0

Authors

Amit Kumar
Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India
Shishir Kumar
Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

Abstract


A computer system can be secured by using various methods like firewalls, anti-virus tools, network security tools, malware removal tools, monitoring tools etc. These tools and applications are being by most of the computer users. These computer security tools need to be updated and monitored regularly by the user. If any computer users fail to update the security tools, then the computer system may be infected by virus or may be attacked. Through this paper a learning system is being proposed to provide security by identify the operating system process as Self and Non-Self. Concepts of Artificial Neural Network (ANN) Learning have been used for the identification of processes. Initially, an Artificial Neural Network is created by using processes parameters with random weights. These weights are updated by using Gradient Descent Algorithm for various training examples, and then this Artificial Neural Network is tested with test data examples. It has been observed that the Artificial Neural Network Learning provides a better approach for identifying Self and Non-Self process and provides a better security.

Keywords


Self and Non Self Process, Machine Learning, Artificial Neural Network, Gradient Descent, Perceptron.

References