ANALISA MALWARE PADA TRAFFIC JARINGAN BERBASIS POLA LALU LINTAS DATA MENGGUNAKAN METODE ANOMALY
Jian Malik Hidayat, Herri Setiawan, Tasmi Tasmi · Prosiding SNAST · 2024
Network security is a major challenge in the era of increasingly rapid digitalization. PDF files, which are widely used for sharing information, are often exploited by cybercriminals to insert malware. This research aims to analyze the impact of malware in PDF files on network traffic using Wireshark software. With a traffic pattern-based approach and anomaly detection, this research identifies malicious activities such as connections to servers, data exfiltration, traffic spikes, and the use of obfuscation techniques. The malware in the PDF file shows suspicious traffic patterns that include increased volume of outgoing data, and repeated data packets to certain destinations. Additionally, these activities cause significant disruption to network performance, open security gaps, and increase the risk of sensitive data leakage. Wireshark is used to capture, analyze and identify traffic anomalies in real-time. The research results show that pattern and anomaly-based analysis using Wireshark effectively improves the accuracy of PDF malware detection at the network level. These findings support the importance of applying traffic analysis methods to detect hidden cyber threats. In addition, this research makes an important contribution to the development of network analysis-based cyber attack mitigation strategies, helping organizations respond to threats more quickly and reduce potential losses. With this approach, network security can be strengthened to deal with evolving threats.