Employing Machine Learning for Anomaly, Malware and Intrusion Detection in Real World Network Environments

Research Scholar Yashkiran; Professor Gurpreet Singh; Assistant Professor Simrandeep Kaur

Employing Machine Learning for Anomaly, Malware and Intrusion Detection in Real World Network Environments

Author : Research Scholar Yashkiran, Professor Gurpreet Singh, Assistant Professor Simrandeep Kaur Journa Name: INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH AND ENGINEERING TRENDS Country : India Volume: 10 issue: 3 Year: 2024 Views : 614

Abstract:

Employing machine learning for anomaly, malware, and intrusion detection in computer networks offers significant potential for enhancing security posture and mitigating cyber threats. By leveraging data-driven algorithms and adaptive detection mechanisms, machine learning enables proactive identification of anomalies, timely detection of malware, and accurate recognition of intrusions within network environments. However, challenges such as dataset labeling, model generalization, and real-time processing remain areas of active research and development. Moving forward, continued advancements in machine learning techniques and their integration with network security frameworks will play a crucial role in safeguarding critical infrastructure and protecting against evolving cyber threats.