In the realm of network security, effective detection and mitigation of cyber threats are crucial for
maintaining robust defenses. This study compares Deep Packet Inspection (DPI) and Anomaly-Based
Detection techniques, two prominent approaches for identifying and addressing network threats.
DPI, known for its high precision, excels in detecting known threats through detailed packet analysis
but introduces significant performance overhead and higher costs. In contrast, Anomaly-Based
Detection offers superior recall for novel threats with lower latency and bandwidth usage, making
it more adaptable to dynamic network environments. This comparison evaluates detection accuracy,
performance overhead, scalability, real-time capabilities, false positive/negative rates, and cost/
resource utilization for both techniques. The findings reveal that while DPI provides greater precision
and fewer false positives, Anomaly-Based Detection demonstrates better scalability, efficiency in hightraffic
scenarios, and cost-effectiveness. The insights gained from this study are intended to guide the
selection and implementation of network security solutions tailored to specific organizational needs and
evolving threat landscapes.
