Advanced Techniques in Network Traffic Analysis: Utilizing Wireshark For In-Depth Live Data Packet Inspection And Information Capture

The escalating frequency and sophistication of cyber-attacks have placed a spotlight on the importance of Quality of Service (QoS) and robust network security mechanisms. Effective traffic analysis and distribution are critical for maintaining the integrity of network applications and safeguarding data. Our research focuses on the development and assessment of a network intrusion detection system (NIDS) that utilizes the advanced packet analysis capabilities of Wireshark, a renowned network protocol analyzer. The system is designed to enhance network security by enabling more efficient and accurate data collection through network monitoring tools. This serves as the foundation for detecting and thwarting a wide range of malicious activities, including malware and spyware incursions, within network traffic. The innovation of our approach lies in its integration of packet sniffing technology with AI-driven analysis techniques. By applying Machine Learning algorithms to the data captured by Wireshark, our NIDS is capable of identifying patterns indicative of intrusive behavior, which might elude traditional detection methods. We present a comprehensive evaluation of our system, showcasing its efficacy in real-time intrusion detection and its potential for integration into existing network infrastructures. This study contributes to the ongoing discourse in cybersecurity, offering a sophisticated tool in the battle against digital threats and reinforcing the role of intelligent systems in protecting network environments