Published Paper Details:

Abstract Network traffic analysis is super important in today's cybersecurity world. It's like a careful at what's happening on a network find any bad stuff going on. This kind of work keeps our digital places safe from the sneaky threats that hide in cyberspace. By examining the many ways data moves around, security experts can spot odd patterns that usually mean trouble is nearby. One key part of this task is keeping a close eye on network traffic. This means capturing and breaking down data packets to find useful information. When analysts look at these digital pieces, they can see when things don't match up with what's expected--like strange amounts of traffic, surprise data transfers, or weird communication methods. These unusual signs are like warning flags that encourage them to dig deeper and take action if needed. To uncover the hidden secrets in network traffic, analysts use various tricks. For example, statistical analysis helps them spot anything that stands out or doesn't fit with "normal" behavior. Machine learning algorithms can also play a big role! These smart tools learn from huge sets of data and can identify complex patterns that might signal harmful activities. Plus, behavioral analysis looks at how users and systems act, helping to catch subtle oddities that regular detection tools might miss. The success of network traffic analysis really depends on telling the difference between good traffic and bad traffic. That's why researchers have come up with advanced models for classifying this data. These models use a mix of details like protocol info, what the content is, and how traffic behaves. They train these models using lots of normal traffic and attack samples so they can make smarter decisions about what they see happening on the network.