UUT: Unveiling Unknown Threats with Machine Learning for Exploit Detection

In cybersecurity, attack detection indicates finding and countering efforts to take advantage of weak spots in computer systems using cutting edge technologies like AI and machine learning. Exploits are software or procedures that exploit vulnerabilities in computer systems, networks, or applications to compromise security. It's a tool or method used by attackers to exploit system flaws. Software defects, configuration mistakes, and design weaknesses may be exploited. A successful exploit may provide unauthorized access, data alteration, or malicious code execution. AI quickly looks at how networks work, finds strange behavior, and spots possible security holes before they get worse. This proactive strategy enables firms to react to and decrease cyber hazards promptly, reducing their consequences and improving overall network security. Early cybersecurity vulnerability discovery is essential to minimize harm. Early exploit detection permits quick reaction, reducing cyberattack damage. Early detection helps firms fix vulnerabilities, protect sensitive data, and strengthen defenses, minimizing the chance of breaches and improving cybersecurity resilience. By trying out different machine learning methods for attack identification, this research article gives useful information. According to the results, the decision tree and XGBoost algorithms are the best at finding and reducing risks. Through its review and suggestions of strong attack detection methods, this research study helps to improve cybersecurity practices.