A Review: Machine Learning Approaches for Zero-Day Attacks Recognition

DOI

https://www.doi.org/10.59256/ijrtmr.20240402018

Published In

Volume 4, Issue 2 (2024)

Published Online

March-April 2024

Pages

105-109

Article Type

Review Article

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Advancements and rapid technological developments in information age promoted computerization of day to day activities in all walks of life. As technology is growing rapidly, the cybercrime rate also increases both in number and complexity. Since a variety of attacks evolves regularly with complex patterns and varied signatures the task of securing cyberspace becomes more and more difficult and challenging. To minimize the impact of cybercrime through early detection of intrusions, network activity in terms of network traffic, is monitored in real-time thus accumulating huge data which is sometimes erroneous. Therefore, synergizing the concepts of cybersecurity and data analytics is essential to develop effective security algorithms for attack detection. Existing security measures like firewalls are no longer sufficient to deal with these emerging attacks. Because the firewall only checks the header of the data packet, it doesn't go through the details of the packet. Intrusion Detection Systems (IDSs) are the systems introduced as a second line of security after firewalls to handle cyber intrusions more efficiently. IDSs play a key role in protecting cyberspace by examining the entire details of the traffic packets to detect intrusions.