In today’s fast-changing digital world, network security has become a critical issue due to the growing frequency and sophistication of cyberattacks [1], [2]. This study provides a detailed analysis of modern network threats and evaluates how defense mechanisms—especially Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS)—can help mitigate these risks. The paper explores current attack vectors, including Distributed Denial-of-Service (DDoS), Man-in-the-Middle (MitM), phishing, and threats that specifically target Internet of Things (IoT) environments [3]. A comparative overview of signature-based and anomaly-based IDS/IPS techniques is presented, with special emphasis on the role of artificial intelligence and machine learning in improving detection accuracy and accelerating response times [4]. Real-world case studies involving widely adopted tools such as Snort and Suricata are examined to illustrate their effectiveness. The findings suggest that hybrid detection systems, when aligned with Zero Trust Architecture (ZTA), offer a proactive and resilient framework for defending modern networks.