Dhinaharan Nagamalai

B. Prabadevi, N. Jeyanthi, Nur Izura Udzir et al.

Sniffing is one of the most prominent causes for most of the attacks in the digitized computing environment. Through various packet analyzers or sniffers available free of cost, the network packets can be captured and analyzed. The sensitive information of the victim like user credentials, passwords, a PIN which is of more considerable interest to the assailants can be stolen through sniffers. This is the primary reason for most of the variations of DDoS attacks in the network from a variety of its catalog of attacks. An effective and trusted framework for detecting and preventing these sniffing has greater significance in today's computing. A counter hack method to avoid data theft is to encrypt sensitive information. This paper provides an analysis of the most prominent sniffing attacks. Moreover, this is one of the most important strides to guarantee system security. Also, a Lattice structure has been derived to prove that sniffing is the prominent activity for DoS or DDoS attacks.

Marija Prokopijević, Aleksandar Stančić, Jelena Vasiljević et al.

Neural networks have been known for a long time as a tool for different types of classification, but only just in the last decade they have showed their entire power. Along with appearing of hardware that is capable to support demanding matrix operations and parallel algorithms, the neural network, as a universal function approximation framework, turns out to be the most successful classification method widely used in all fields of science. On the other side, multifractal (MF) approach is an efficient way for quantitative description of complex structures [1] such as metastatic carcinoma, which recommends this method as an accurate tool for medical diagnostics. The only part that is missing is classification method. The goal of this research is to describe and apply a feed-forward neural network as an auxiliary diagnostic method for classification of multifractal parameters in order to determine primary cancer.

Jelena Vasiljević, Ivica Milosavljević, Vladimir Krstić et al.

This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from a normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability