A Review on Machine Learning Framework for Detection of Intrusion

Intrusion Detection Systems (IDSs) are critical tools in the realm of cybersecurity, designed to detect and respond to unauthorized access, malicious activities, and potential threats within network or host environments. These systems monitor and analyze network traffic or system behavior, identifying patterns that may indicate security breaches. IDSs are classified into various types, including Network-based (NIDS), Host-based (HIDS), and Hybrid systems, each offering distinct advantages based on the nature of the monitored environment. Signature-based detection focuses on identifying known threats, while anomaly-based detection aims to detect unknown or novel attacks by analyzing deviations from normal system behavior. Despite their effectiveness, IDSs face challenges such as false positives, resource constraints, and the need for constant updates. The integration of IDSs with complementary tools, such as firewalls and Security Information and Event Management (SIEM) systems, enhances their capabilities. This abstract highlights the importance of IDSs in maintaining robust cybersecurity defenses, emphasizing the need for continuous adaptation and improvement to combat ever-evolving threats and safeguard organizational systems and data