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Published Paper Details:
Network Traffic Analysis for Intrusion Detection: Techniques for Monitoring and Analyzing Network Traffic to Identify Malicious Activities.
Authors
Harshita Cherukuri,Shreyas Mahimkar,Om Goel,Dr Punit Goel,Dr Shailesh Singh
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Keywords
Key Words Network traffic analysis, Intrusion detection, Cybersecurity, Malicious activities, Network monitoring, Data packet analysis, Anomalies, Statistical analysis, Machine learning, Behavioral analysis, Traffic classification, Classification models, Cyberattacks, Databreaches, Threat landscape
Abstract
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.
IJCRT's Publication Details
Unique Identification Number - IJCRT2303991
Paper ID - 266487
Author type - Indian Author
Page Number(s) - i339-i350
Pubished in - Volume 11 | Issue 3 | March 2023
DOI (Digital Object Identifier) -
No Of Downloads - 473
Author Country - India, 201017, ghaziabad, ghaziabad, 201017, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
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Harshita Cherukuri,Shreyas Mahimkar,Om Goel,Dr Punit Goel,Dr Shailesh Singh, "Network Traffic Analysis for Intrusion Detection: Techniques for Monitoring and Analyzing Network Traffic to Identify Malicious Activities.", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.11, Issue 3, pp.i339-i350, March 2023, Available at :http://www.ijcrt.org/papers/IJCRT2303991.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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