9786205516515 - statistical analysis of network traffic: basic statistical tool de kaur, prabhjot; awasthi, amit (5 resultados)
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Librería: preigu, Osnabrück, Alemaniapreigu
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Taschenbuch. Condición: Neu. Statistical Analysis of Network Traffic | Basic statistical tool | Prabhjot Kaur (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786205516515 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | A…nbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Network forensics helps in tracking down cyber fraudsters by assessing and tracing back network data. The use of various network traffic gathering tools is required. Network forensics is analyzing network traffic to detect intrusion…s and studying how the crime occurred, i.e., establishing a crime scene for investigation and replays. This study proposes a general network forensic process model and architecture. A secondary data set, KDD CUP of normal and anomalous traffic is used for analysis to simulate the entire process. The dataset is largely processed for feature selection and redundancy removal. The dataset was cleaned before being analyzed using the Support Vector Machine learning model to classify the traffic. The multiclass classification has been used to categorize various types of network attacks. The accuracy of the model is then evaluated using the obtained results. 64 pp. Englisch.
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Librería: moluna, Greven, Alemaniamoluna
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kaur PrabhjotPrabhjot Kaur is working at Uttaranchal University. She has keen interest in data analysis using ML.Dr. Amit Awasthi is working at University of Petroleum & Energy Studies having more than…15 years of professional experi.
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Envío por EUR 60,00Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Network forensics helps in tracking down cyber fraudsters by assessing and tracing back network data. The use of various network traffic gathering tools is required. Network forensics is analyzing network traffic to detect intrusions an…d studying how the crime occurred, i.e., establishing a crime scene for investigation and replays. This study proposes a general network forensic process model and architecture. A secondary data set, KDD CUP of normal and anomalous traffic is used for analysis to simulate the entire process. The dataset is largely processed for feature selection and redundancy removal. The dataset was cleaned before being analyzed using the Support Vector Machine learning model to classify the traffic. The multiclass classification has been used to categorize various types of network attacks. The accuracy of the model is then evaluated using the obtained results.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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EUR 44,59
Envío por EUR 60,57Se envía de Alemania a Estados Unidos de AmericaCantidad disponible: 1 disponibles
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Network forensics helps in tracking down cyber fraudsters by assessing and tracing back network data. The use of various network traffic gathering tools is required. Network forensics is analyzing network traffic to detect intrusions and… studying how the crime occurred, i.e., establishing a crime scene for investigation and replays. This study proposes a general network forensic process model and architecture. A secondary data set, KDD CUP of normal and anomalous traffic is used for analysis to simulate the entire process. The dataset is largely processed for feature selection and redundancy removal. The dataset was cleaned before being analyzed using the Support Vector Machine learning model to classify the traffic. The multiclass classification has been used to categorize various types of network attacks. The accuracy of the model is then evaluated using the obtained results.
