A Machine Learning-Based Packet Sniffer for Detection and Classification of the Denial-Of-Service Attack Packets at the Network Layer
Kipkorir Peacemark, Ephantus Gichuki Mwangi, Jotham Milimo Wasike · International Journal of Research and Innovation in Applied Science · 2025
Cyber threats attacks have continued to evolve in complexity and sophistication, posing significant risks to an organization’s network infrastructure and sensitive data's availability, confidentiality, and integrity. Therefore, there is a great need to create a defense mechanism to counteract this problem. This study therefore was focused on modeling a packet sniffer utilizing machine learning techniques to identify denial of service (DOS) attack packets at the network layer of the OSI model. The overall purpose of the study was to capture and interpret packets transmitted over a local area network to detect and capture the DOS threats within the Open Systems Interconnection Model (OSI) network layer. This layer is prone to several attacks for instance, denial-of-service, routing protocol attacks, Port scanning and enumeration, and fragmentation-based attacks. This study, delved into detecting and capturing the denial of service threats at the third layer of the OSI model in a local area network. Some examples of DOS attacks are UDP flood which sends a significant quantity UDP (User Datagram Protocol) packets to the targeted systems and thereby exhausting network resources, ICMP flood which transmits a significant quantity of Internet Control Message Protocol (ICMP) packets to overwhelm network devices, SYN flood which takes advantage of the TCP three-way hand-shake procedure by sending a lot of SYN requests without carrying out the necessary handshake, using server resources and blocking valid connections.