Innovative Genetic Approach for Intrusion Detection by Using Decision Tree
Vikas Sharma, Aditi Nema
Abstract
Vikas Sharma, Aditi Nema
Abstract
Intrusions in an information system are the activities that violate the security policy of the system, and intrusion detection is the process used to identify intrusions. Intrusion detection has been studied for approximately 20 years. Intrusion detection provides a way to identify intrusion and allow responses to attacks against the systems. We present a machine learning approach known as Genetic Algorithm, to identify such attack type of connections. Intrusion detection system used information in the form of audit trails or packet of the network. In this paper we try to solve the fidelity problem. The intrusion detection systems have more result or information in misinterpretations or missed events is known as fidelity.
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Intrusions in an information system are the activities that violate the security policy of the system, and intrusion detection is the process used to identify intrusions. Intrusion detection has been studied for approximately 20 years. Intrusion detection provides a way to identify intrusion and allow responses to attacks against the systems. We present a machine learning approach known as Genetic Algorithm, to identify such attack type of connections. Intrusion detection system used information in the form of audit trails or packet of the network. In this paper we try to solve the fidelity problem. The intrusion detection systems have more result or information in misinterpretations or missed events is known as fidelity.
Key concepts: Intrusion detection system, Anomaly-based intrusion detection system, Computer science, Decision tree, Host-based intrusion detection system, Fidelity, Network security, Misuse detection