AI Driven Intrusion Detection Enhancing Cybersecurity with Machine Learning

AI Driven Intrusion Detection Enhancing Cybersecurity with Machine Learning

                    Suvetha V                                                       Srilakshmi rajakumari P I                                          Vadiyemgati Kalpana

        Department of Electronics                                            Department of Electronics                                           Department of Electronics

        and Communication                                                       and Communication                                                    and Communication

 Panimalar Institute of Technology                            Panimalar Institute of Technology                             Panimalar Institute of Technology

               Chennai,India                                                             Chennai,India                                                             Chennai,India

       vsuvetha2004@gmail.com                               srilakshmirajakumari2921@gmail.com                             kalpanavelu3103@gmail.com

Dr.S.Sathiya Priya,M.E,Ph.D.,

Head & Professor

Department of Electronics and Communication

Panimalar Institute of Technology

Chennai,India

sathiyapriyasecepit@gmail.com

    Abstract— Intrusion detection in cybersecurity has become crucial due to the increasing number of cyber threats and data breaches. Traditional security measures fail to detect sophisticated attacks, making machine learning (ML) an essential tool for improving security frameworks. The integration of artificial intelligence (AI) enables real-time monitoring, anomaly detection, and predictive analytics, ensuring proactive threat management. This paper discusses an AI-driven intrusion detection system (IDS) that includes supervised and unsupervised ML techniques to enhance cybersecurity resilience. The system improves accuracy, efficiency, and automation, thereby mitigating risks and reducing false positive rates. Through rigorous testing, the proposed IDS demonstrates superior performance over conventional methods.

Keywords—Intrusion Detection System (IDS), Artificial Intelligence (AI), Machine Learning (ML), Cybersecurity, Anomaly Detection, False Positives, Real-Time Monitoring.