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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
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Don't let this gap widen
Inadequate mastery of advanced Wireshark network traffic analysis blinds network administrators and SOC teams to stealthy threats hidden in complex packet streams.
According to Verizon's 2023 DBIR, 74% of breaches involve overlooked network anomalies, resulting in average remediation costs exceeding $4.45 million per incident per IBM data.
Without these skills, incident detection times stretch to 277 days on average, exposing companies to regulatory fines, data loss, and reputational damage while stalling careers in cybersecurity.
Every month of delay amplifies vulnerabilities, turning preventable risks into catastrophic failures.
The Training Wireshark - Advanced Network Traffic Analysis training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training Wireshark - Advanced Network Traffic Analysis training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training Wireshark - Advanced Network Traffic Analysis training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Professional Wireshark environment configuration. Advanced display and capture filters. In-depth TCP/UDP/IP decoding. HTTP/HTTPS TLS analysis. Practical exercises on enterprise traffic dumps. Setting up red thread project: simulated network capture. IO statistics and conversations. Follow TCP streams. Real troubleshooting cases. Wireshark integration with SIEM tools. Regex filters in practice. Pro exercises for low-level attack detection. Basic Tshark scripts preparation. Approximately 120 words.
VoIP/SIP protocol analysis with Wireshark. Security anomaly detection. VoLTE and mobile protocols. Advanced graphs and expert info. Tshark automation with Lua scripts. ELK Stack integration for network logs. Red thread project: full attack analysis. Pro network forensics exercises. High-volume capture performance optimization. Cloud deployment AWS/GCP Wireshark. Enterprise cases: incident investigation. Skills assessment via multiple-choice quiz. Red thread project presentation. Production best practices. Post-training support resources. Malware detection in network traffic in practice. Approximately 130 words.
Target audience
Network administrators, cybersecurity experts, SOC engineers for Wireshark skill enhancement in the enterprise
Prerequisites
Advanced TCP/IP expertise, network protocols (HTTP, DNS, TLS), experience with packet captures and Linux/Windows
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