Statistical Analysis of Network Traffic - Kaur, Prabhjot;Awasthi, Amit
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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…mehr

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Produktbeschreibung
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.
Autorenporträt
Prabhjot Kaur arbeitet an der Universität von Uttaranchal. Sie hat großes Interesse an der Datenanalyse mit ML.Dr. Amit Awasthi arbeitet an der University of Petroleum & Energy Studies und verfügt über mehr als 15 Jahre Berufserfahrung. Er hat 40 Forschungsarbeiten in SCI-Zeitschriften mit einem Impact-Faktor von 75, einem i10-Index von 18 und Zitierungen von 1000 veröffentlicht.