Machine Learning models for early
detection of cyber attacks.
Harshit Jain
Student
Sharda University Agra l
Abstract
The rapid growth of digital infrastructure and internet-connected
systems has significantly increased the risk of cyber attacks.
Traditional security mechanisms such as signature-based intrusion
detection systems are often ineffective against new and evolving
threats. Machine Learning (ML) has emerged as a powerful
approach for detecting cyber attacks at an early stage by
analyzing patterns in network traffic and system behavior. This
research explores the application of various machine learning
models for the early detection of cyber threats, including
malware, phishing, and network intrusions. The study examines
both supervised and unsupervised learning techniques such as
Decision Trees, Random Forest, Support Vector Machines, and
Neural Networks to identify anomalous activities within network
data. Using publicly available cybersecurity datasets, the models
are trained and evaluated based on performance metrics such as
accuracy, precision, recall, and F1-score. The results demonstrate
that machine learning-based detection systems can significantly
improve the speed and accuracy of identifying potential cyber
attacks compared to traditional methods. The proposed approach
contributes to strengthening proactive cybersecurity defense
mechanisms by enabling faster detection and response to
emerging threats.
