Kapil Verma

Curve Fitting Analysis of Internet Traffic Sharing Management in Computer Network under Cyber Crime

crime is growing fast day-by-day through the spreading of Internet around the world. Many under-developed countries are using dial-up-setup network where a call is connected only after a little extra effort. Some identified cyber crimes are writing abusive letters, giving threats to other, sending unwanted mails, hacking secret data, phishing attack on websites etc. The Internet traffic growth has some positive correlation with the cyber crime as justified by the Markov Chain Model based analysis of authors to examine the interrelationship between traffic sharing and blocking probability. This analysis has been extended for two-call basis also and model based relations are derived. These relations are complicated in terms of mathematical structure. This paper presents least square based curve analysis for Markov Chain model based relationship between traffic sharing and network blocking. The earlier suggested complicated relationship has been simplified in the form of straight line showing a good fitting. The Coefficient of determination has been computed showing the high value towards unity. It proves that simplified linear relationships perform well as a thumb rule for expressing the complex relationship between traffic sharing and network blocking probability. .

Estimation of Ready Queue Processing Time using Transformed Factor-Type (T-F-T) Estimator in Multiprocessor Environment

ready queue processing time estimation problem appears when many processes remain in the ready queue just before the occurrence of sudden failure of system. The system administrator has to decide immediately, how much further time is required to process the remaining jobs in the ready queue, before shutting down the entire system as precautionary measure, so that while restart, it may remain in the safe state. In lottery scheduling, this prediction is possible with the help of sampling techniques. Factor- Type estimation method, existing in literature of sampling, was used by many authors to predict the time required provided the highly correlated sources of auxiliary information are available. This paper suggests two new estimation methods to predict the remaining total processing time required to process completely the ready queue provided sources of auxiliary information are negatively correlated. Under this approximation, the bias and m.s.e of the proposed estimators have been obtained using the set up of random sampling applicable to lottery scheduling. Performance of both estimation methods are compared in terms of mean squared error. The confidence intervals are calculated for comparing the efficiency of the estimate. One proposed estimator found better over other. . KeywordsScheduling, Transformed Factor-Type (T-F-T) Estimator, Mean Squared Error (M.S.E), Variance, Confidence Intervals

Explicit Query based Detection and Prevention Techniques for DDOS in MANET

The wireless adhoc networks are highly vulnerable to distributed denial of service (DDoS) attacks because of its unique characteristics such as open network architecture and shared wireless medium. A denial-of-service attack (DoS attack) or distributed denial-of-service attack (DDoS attack) is an attempt to make a computer resource unavailable to its legitimate users. The denial of service (DOS) does not result in information theft or any kind of information loss but can be very dangerous, as it can cost the person a large amount of time and money. Significant efforts have been made towards making adhoc network secure and free from DDoS attacks. In this paper we study how various detection parameters together work as a single and efficient method to detect various DDoS attacks in Manet. Later in this paper a technique to prevent DDoS attacks in Manet is also presented which help in preventing the attacks to communicate in the network and did not allow them in the network.

Least Square based Curve Fitting in Internet Access Traffic Sharing in Two Operator Environment

In Internet service market, at many places the competition occurs among internet service providers. The network suffers from congestion and users have priority to prefer that network having lowest congestion. During the repeated connecting attempt process the user has to maintain call-by-call effort. In this paper Markov chain model is used to establish a relationship between internet access traffic sharing and blocking probability of the network. The model based relationship has been simplified into the linear relationship. The accuracy of the fitting is examined through coefficient of determination. This process has eased up the multiple parameter based complicated relationship into a much simplified form. An average linear relationship is predicted for handy applications.