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The Use Of Machine Learning Algorithms To Detect Man-in-the-middle (mitm) Attack In User Datagram Protocol Packet Header
In today's fast technological world, the need for computer networks has dramatically increased. Security is one of the vital demands about network infrastructure and hence very necessary to protect the information sent and received during data transmission against outside factors trying to intercept systems. This study proposed a systematic approach in using the Forward Greedy Selection and Random Forest algorithms to detect User Datagram Protocol packet headers from the Man-In-The-Middle attacks in network. Accurate results were achieved in the experimentation but were lower than expected due to the inconsistencies and connectionless nature of UDP traffic.
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