AN INTELLIGENT MACHINE LEARNING FRAMEWORK FOR REAL-TIME CYBER THREAT DETECTION IN NETWORK TRAFFIC
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There has been a drastic increase in the cyber threats associated with using digital technologies across many areas of our daily lives. Many of us rely heavily on the Internet for things like communicating (chatting/emailing), storing data, etc., so as a result, everyone (big businesses and individuals alike) is subjected to increased risk. Many cybercriminals have become very sophisticated in their methods of invading networks. They have developed different types of malware that can bypass traditional security measures (i.e., firewalls and antivirus software). Therefore, many intrusion detection systems (IDS) do not provide adequate response capabilities against novel or evolving cyberattacks; rather, due to their limited detection methodology, most IDS only recognize established "attack signatures".
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R, J. S. (2026). An Intelligent Machine Learning Framework for Real-Time Cyber Threat Detection in Network Traffic. International Journal of Science, Strategic Management and Technology, 02(03). https://doi.org/10.55041/ijsmt.v2i3.053
R, Jeevaa. "An Intelligent Machine Learning Framework for Real-Time Cyber Threat Detection in Network Traffic." International Journal of Science, Strategic Management and Technology, vol. 02, no. 03, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i3.053.
R, Jeevaa. "An Intelligent Machine Learning Framework for Real-Time Cyber Threat Detection in Network Traffic." International Journal of Science, Strategic Management and Technology 02, no. 03 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i3.053.
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