Authors :
Sahana Susheela; N. Sarat Chandra; S. Sakthi Priyan
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/yvxvpsvd
Scribd :
https://tinyurl.com/2p9dbcar
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR705
Abstract :
Cyber-attacks are becoming increasingly
sophisticated and difficult to detect using traditional
security measures. To address this challenge, we propose
a predictive analytics- enabled cyber-attack detection
system that utilizes machine learning algorithms to
analyze network traffic and identify potential security
threats in real time. Our system uses a combination of
supervised and unsupervised learning techniques to
identify patterns and anomalies in network data, and to
generate anomaly and normal alert. The system is
trained using historical data from known cyber-attacks
and anomalies and we visualize the accuracy of various
algorithms.
Keywords :
Cyber-Attacks, Machine Learning, Predictive Analytics, Anomalies, Network Data.
Cyber-attacks are becoming increasingly
sophisticated and difficult to detect using traditional
security measures. To address this challenge, we propose
a predictive analytics- enabled cyber-attack detection
system that utilizes machine learning algorithms to
analyze network traffic and identify potential security
threats in real time. Our system uses a combination of
supervised and unsupervised learning techniques to
identify patterns and anomalies in network data, and to
generate anomaly and normal alert. The system is
trained using historical data from known cyber-attacks
and anomalies and we visualize the accuracy of various
algorithms.
Keywords :
Cyber-Attacks, Machine Learning, Predictive Analytics, Anomalies, Network Data.
