Collaborative Profile Assessment to Secure MANET by DDOS Attack

In the Mobile Ad-hoc Network, nodes bind together in the centralised authority's absence because reliability is one of the main challenges. The MANETS protective architecture provides some consequential problems due to the specific features of MANETS. The DDoS attack in the network is not quickly detectable. A management infrastructure that guarantees extensive security and the required network performance from attacks must be developed to overcome the barriers. Direct methods cannot be found successfully in mobile ad hoc networks in which network topology differs animatedly. Different DDoS security systems boost the network's output in front of an attacker to deactivate mismanagement, like NTRS. In this study, the Distributed Profile Evaluation Mechanism (DPEAP) DDoS Attack Effect in the Network proposes that compromise packets tossed out of the network beyond the network's capacity. The NTRS was a modern methodology in the study, and the DPEAP suggested is a new technique. The DPEAP identifies the attacker's behaviour by matching an attacker's profile with the ordinary nodes on the network, provided that the Node Profile is regular in the foaming of the proper network data delivery. The DPEAP then declare that the attacker's network has no hazard. In contrast with NTRS in MANET, the DPEAP method is stable and efficient

Intrusion Alert Correlation Based on UFP-Growth & Genetic Algorithm

Abstract Intrusion alert correlation is an important factor for network security assessment. In the current scenario various security assessment algorithm are available for risk calculation. These algorithms were qualitative in nature. It is difficult for security managers to configure security mechanisms. The paper discuss the problem of managing alerts. A novel approach for intrusion alert correlation using UFP-Growth and Genetic Algorithm is presented in this paper. UFP-Growth is used for association rule mining and genetic algorithm is used for finding optimal pattern. The proposed method implement in MATLAB 7.8.0. For implement purpose various function and script file were written for implementation of model. For the test of our hybrid method, we used DARPA KDDCUP99 dataset. Our proposed method compare with existing ACR (assessment of credibility and risk) technique and getting better result such as risk calculation and minimized alert correlation rate

A Neural Network Approach for ECG Classification

Abstract- bioelectrical signal, which records the heart’s electrical activity versus time, is an electrocardiogram (ECG). It is an important diagnostic tool for assessing heart functions. The interpretation of ECG signal is an application of pattern recognition. signal pre-processing, QRS detection, feature extraction and neural network for signal classification are those techniques which used in this pattern recognition comprise. There are Different ECG feature inputs were used in the experiments to compare and find a desirable features input for ECG classification. Among different structures, it was found that a three layer network structure with 25 inputs, 5 neurons in the output layer and 5 neurons in its hidden layers possessed the best performance with highest recognition rate of 91.8 % for five cardiac conditions. Keyword-- ECG, QRS, Neural Network I

Privacy Preserving Using Randomization and Encryption Methods

Abstract: The technology of data mining is used in extraction of useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Before publishing or sharing private information about individuals, businesses and organizations have to be suppressed. Privacy-preserving data mining has thus become an important issue in current years. This paper has review of evolutionary privacypreserving data mining technology to find appropriate method to perform secure transactions into a database