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Sinopsis
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.
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- EditorialLAP LAMBERT Academic Publishing
- Año de publicación2022
- ISBN 10 6205516519
- ISBN 13 9786205516515
- EncuadernaciónTapa blanda
- IdiomaInglés
- Número de páginas64
- Contacto del fabricanteno disponible
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Statistical Analysis of Network Traffic
Publicado por
LAP LAMBERT Academic Publishing Nov 2022, 2022
ISBN 10: 6205516519
ISBN 13: 9786205516515
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
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 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. 64 pp. Englisch. Nº de ref. del artículo: 9786205516515
Cantidad disponible: 2 disponibles
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Statistical Analysis of Network Traffic
Publicado por
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205516519
ISBN 13: 9786205516515
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
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. Nº de ref. del artículo: 762324448
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Statistical Analysis of Network Traffic : Basic statistical tool
Publicado por
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6205516519
ISBN 13: 9786205516515
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
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. Nº de ref. del artículo: 9786205516515
Cantidad disponible: 1 disponibles
Imagen del vendedor
Statistical Analysis of Network Traffic
Publicado por
LAP LAMBERT Academic Publishing Nov 2022, 2022
ISBN 10: 6205516519
ISBN 13: 9786205516515
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. 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 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.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch. Nº de ref. del artículo: 9786205516515
Cantidad disponible: 2 disponibles