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AI-Powered Deep Packet Analyzer
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DOI：10.1007/978-981-96-7499-2_25.png)
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Abstract
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Preserving the security and performance of a network in today's networked environment depends upon monitoring and analyzing network traffic. The paper discusses our system overview that analyzes live network traffic from a mirrored port by means of DPI. It includes the provision for gaining insight into the usage of networked applications by sorting packets into particular categories, such as HTTP, video streaming, or other protocols. The technology employs state-of-the-art machine learning algorithms and categorization in order to identify and alert potentially damaging packets in a timely manner. Upon detecting any threats, the system alerts the user so that he or she can take appropriate preventive measures. The hybrid strategy ensures improved visibility and security of network environments through concurrent, real-time threat detection and traffic classification.
Keywords:
Intrusion detection system
Deep packet inspection
Machine learning
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NIDS: A Network Based Approach to Intrusion Detection and Prevention.png)
2009 International Association of Computer Science and Information Technology - Spring Conference
2009 International Association of Computer Science and Information Technology - Spring ConferenceIF0
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