Pradeep Yadav

The Enhanced Intrusion Detection System utilizing a Hybrid Machine Learning Approach is a security system that leverages a combination of machine learning algorithms to detect and prevent unauthorized access to computer networks. The system analyzes network traffic patterns and monitors user behavior to identify potential threats. By using a hybrid machine learning approach, the system is designed to improve its ability to detect and prevent cyber attacks, ultimately enhancing network security.

A wireless sensor network consists of an enormous number of sensor nodes. Every sensor node senses environmental conditions such as heat, light, force and transmits the sensed data to a base station which is an extensive way rancid in universal. While the sensor nodes are powered by inadequate power batteries, in regulate to lengthen the lifetime of the network, minimal energy consumption is important for sensor nodes. Spread Energy Efficient Clustering scheme for heterogeneous wireless sensor networks, which the Base Station ensure that the high energy nodes becoming a gateways and cluster heads to improve network lifetime and average energy savings. The use of clustering techniques is a popular approach to improve energy efficiency and extend the lifetime of Wireless Sensor Networks (WSNs). Two-level clustering is a technique that divides the network into two levels of clusters to improve data collection and transmission. This protocol involves small clusters in the first level and larger clusters in the second level, with cluster heads responsible for managing communication within each cluster.

A detec o de intrus es na rede um factor importante para a an lise de risco da seguran a da rede. Na ltima d cada, est o dispon veis diferentes m todos e quadros para a detec o de intrus es e o alerta de seguran a. V rios m todos baseiam-se no processo de descoberta de conhecimentos e alguns quadros baseiam-se em redes neuronais. Estes modelos completos tomam decis es baseadas em regras para a gera o de alertas de seguran a. Nesta disserta o, propusemos um novo m todo para a detec o de intrus es utilizando a fus o de dados e a classifica o SVM. A fus o de dados funciona com base nos enviesamentos da recolha de caracter sticas de ocorr ncia. A m quina de vectores de suporte um super classificador de dados. Agora usamos SVM para a detec o de itens fechados da t cnica baseada em regras.

Network intrusion awareness is important factor for risk analysis of network security. In the recent decade different method and framework are available for intrusion detection and security alertness. A number of method based on knowledge discovery process and some framework based on neural network. These complete model take rule based decision for the generation of security alerts. In this dissertation we proposed a novel method for intrusion awareness using data fusion and SVM classification. The data fusion work on the biases of features gathering of occurrence. Support vector machine is super classifier of data. Now we used SVM for the detection of closed item of ruled based technique.

Die Erkennung von Netzwerkeinbr chen ist ein wichtiger Faktor f r die Risikoanalyse der Netzwerksicherheit. In den letzten zehn Jahren wurden verschiedene Methoden und Rahmenwerke f r die Erkennung von Eindringlingen und die Sicherheitswarnung entwickelt. Eine Reihe von Methoden basiert auf einem Wissensfindungsprozess und einige Rahmenwerke basieren auf einem neuronalen Netzwerk. Diese vollst ndigen Modelle treffen regelbasierte Entscheidungen f r die Generierung von Sicherheitswarnungen. In dieser Dissertation haben wir eine neuartige Methode zur Erkennung von Eindringlingen mittels Datenfusion und SVM-Klassifizierung vorgeschlagen. Die Datenfusion arbeitet mit den Verzerrungen der Merkmale, die beim Auftreten gesammelt werden. Support Vector Machine ist ein Superklassifikator f r Daten. Jetzt haben wir SVM f r die Erkennung von geschlossenen Elementen der regelbasierten Technik verwendet.

La connaissance des intrusions dans les r seaux est un facteur important pour l'analyse des risques li s la s curit des r seaux. Au cours de la derni re d cennie, diff rentes m thodes et structures ont t mises au point pour la d tection des intrusions et la vigilance en mati re de s curit . Un certain nombre de m thodes sont bas es sur le processus de d couverte des connaissances et certains cadres sont bas s sur les r seaux neuronaux. Ces mod les complets prennent des d cisions bas es sur des r gles pour la g n ration d'alertes de s curit . Dans cette th se, nous avons propos une nouvelle m thode de d tection des intrusions utilisant la fusion de donn es et la classification SVM. La fusion de donn es travaille sur les biais de la collecte des caract ristiques de l'occurrence. La machine vecteur de support est un super classificateur de donn es. Nous avons utilis le SVM pour la d tection des l ments ferm s de la technique bas e sur les r gles.

La consapevolezza delle intrusioni di rete un fattore importante per l'analisi dei rischi della sicurezza di rete. Nell'ultimo decennio sono disponibili diversi metodi e strutture per il rilevamento delle intrusioni e l'allerta della sicurezza. Alcuni metodi si basano sul processo di scoperta della conoscenza e altri sulla rete neurale. Questi modelli completi prendono decisioni basate su regole per la generazione di avvisi di sicurezza. In questa tesi abbiamo proposto un metodo innovativo per la consapevolezza delle intrusioni utilizzando la fusione dei dati e la classificazione SVM. La fusione dei dati lavora sulle distorsioni della raccolta delle caratteristiche dell'evento. La macchina vettoriale di supporto un superclassificatore di dati. Ora abbiamo utilizzato SVM per il rilevamento di elementi chiusi di una tecnica basata sulle regole.