The rapid development of digital technologies and the increasing dependence on connectedsystemshavegreatlyincreasedtheriskof cybersecuritythreats.Organizations and individualsarefacingvariouscyber-attackssuchasmalware,phishing,ransomware,denialof-service attacks, and advanced persistent threats. These attacks are becoming more complex, frequent,anddifficulttodetectusingtraditionalsecuritymethods.Conventionalcybersecurity solutions mainly depend on predefined rules and signature-based detection, which are often ineffective against new and unknown threats. To overcome these challenges, Artificial Intelligence(AI)andMachineLearning(ML)havebecomeimportanttechnologiesinmodern cyber security. Artificial Intelligence enables machines to perform tasks that require human intelligence, including learning, reasoning, and decision-making.

MachineLearningallowscomputersystemstolearnfromdata,identifypatterns,andimprove their performance without requiring direct programming. In cyber security, AI and ML techniques help analyzelargeamounts of network traffic, systemrecords, and user behaviour datainrealtime.Thesetechnologiessupportthreatdetection,intrusionidentification,malware classification, anomaly detection, and prediction of possible cyber-attacks. Different learning methods such as supervised learning, unsupervised learning, and reinforcement learning are used to identify existing threats and discover new attack patterns.AI-based cyber security systems improve detection accuracy, reduce false alarms, and provide faster responses to security incidents.

MachineLearning-basedIntrusionDetectionSystems(IDS)andIntrusionPreventionSystems (IPS) can identify complex attack behaviours that may not be detected by traditional approaches. AI also helps automate incident response by reducing human dependency and improving response speed. Behaviour analysis using ML further strengthens security by identifying unusual user activities and possible insider threats.