Sahu M.,Centurion University of Technology and Management |
Das S.N.,GIET Gunupur
2014 International Conference on High Performance Computing and Applications, ICHPCA 2014 | Year: 2015
The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). Intrusion Detection is the process of monitoring and identifying attempted unauthorized systems access or manipulation. Successful High Performance Computing (HPC) requires a combination of technical innovation as well as political and operational experience to balance out the many (sometimes contradictory) pressures encountered in this field. This is particularly true with respect to operational field. In this paper we try to summarize the various types of Intrusion detection systems available and explain some key points for each particular type of IDS available in the market today and also insight IDS on High Performance Computing (HPC) environment. © 2014 IEEE.
Padhy S.,National Institute of Technology Rourkela |
Padhi S.S.,GIET Gunupur
Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013 | Year: 2013
In this paper, a spatially adaptive denoising algorithm is proposed which provides satisfactory results when the image is corrupted with the additive white Gaussian noise. Generally, suppression of Gaussian noise poses a trade-off problem between suppressing the noise and preserving the detailed information of the image. Detection of noise and suppression of noise are the two stages used in the proposed algorithm. Noise detection in the image is modeled as a pattern classification problem. Bayes classifier has been utilized to classify the pixels as noisy or non noisy. For effective noise suppression, a Gaussian filter is used in the proposed method which preserves detailed information as compared to PWMAD, SAWM and SADA methods. Bayesian classifier, computational cost, error detection, over-smoothness, and smoothing degree of reconstructed image are the parameters taken into account to effectively suppress the noise components in the proposed method. © 2013 IEEE.