## 5 Simulation Results and Discussion
### Results Discussion  
Figure 3 displays latency distribution in CIC-DDoS2019, UNSW-NB15, and IoT23 datasets under normal and DDoS attack circumstances. DDoS attacks increase latency, as seen by the box plots' normal and attack traffic ranges. The CIC-DDoS2019 dataset shows a median delay increase from 20 ms during regular traffic to 40 ms during attacks. UNSW-NB15 and IoT23 datasets also reveal DDoS-induced latency spikes. This graphic helps explain how delay indicates anomalous network behaviour, especially in distinguishing benign from attack traffic. These situations' traffic patterns were realistically simulated using simulated data. The dot and lines in the figure shows the range and the colors are given for visibility difference. Figure 4 displays traffic volume distribution for the three datasets. Traffic behaviour is shown by the histograms' frequency of distinct traffic volumes under different situations. While DDoS traffic skews the distribution towards more significant volumes, CIC-DDoS2019 traffic concentrates in the lower volume range during regular operations. UNSW-NB15 and IoT23 distributions also change according to attack situations. Validat  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Client (CL)** & **CPU Power** & **Memory (RAM)** & **Network Bandwidth** & **Latency (ms)** & **Packet Loss (\%)** \\ \hline \multirow{2}{*}{CL1} & 1.0 GHz & \multirow{2}{*}{512 MB} & \multirow{2}{*}{512 kbps} & \multirow{2}{*}{90–110} & \multirow{2}{*}{2.5} \\  & (Single-Core) & & & & \\ \hline \multirow{2}{*}{CL2} & 1.8 GHz & \multirow{2}{*}{1 GB} & \multirow{2}{*}{2 Mbps} & \multirow{2}{*}{60–80} & \multirow{2}{*}{1.5} \\  & (Dual-Core) & & & & \\ \hline \multirow{2}{*}{CL3} & 3.2 GHz & \multirow{2}{*}{8 GB} & \multirow{2}{*}{20 Mbps} & \multirow{2}{*}{20–40} & \multirow{2}{*}{0.5} \\  & (Quad-Core) & & & &  
ing the traffic volume-network anomaly relationship requires this figure. Realistic attack patterns are preserved using simulated distributions. Figure 5 shows the reaction time-latency relationship for the three datasets under normal and attack situations. Error bars show how DDoS attacks vary in response time and latency--CIC-DDoS2019 response times and latency increase from typical to attack situations. Similar trends are seen in UNSW-NB15 and IoT23. This chart emphasises how attacks affect network performance and establishes latency and response time as attack detection metrics. Simulations give accurate network diagnostic trends. DDoS detection counts before and after DPCA balancing are shown in Figure 6. The bar charts show the change from unbalanced datasets to 50/50 attacks and regular traffic distributions. The CIC-DDoS2019 dataset initially indicates 30% DDoS traffic, which rises  
Figure 4: Traffic volume distribution for the three datasets. Figure 5: Response time vs. latency with error bars for normal and DDoS traffic across datasets. Figure 3: Latency distribution under normal and DDoS attack conditions. to 50% after balancing. The changes for UNSW-NB15 (35-50%) and IoT23 (38-50%) are similar. This balance assures fair model assessment and improves detection performance by reducing data distribution biases. Simulations show that DPCA preprocesses network traffic datasets well. Figure 7 shows the protocol-wise distribution of DDoS attacks in the CIC-DDoS2019, UNSW-NB15, and IoT23 datasets. The bar charts demonstrate how TCP, UDP, ICMP, and other protocols affect attack volume for each dataset. CIC-DDoS2019 reveals TCP and UDP as the top protocols, contributing 40% and 30%, respectively. UDP-based attacks are significant in the UNSW-NB15 and IoT23 datasets, demonstrating protocol diversity. This graphic helps explain protocol-specific DDoS attacks and optimises detection algorithms to target protocol-level weaknesses. The different colors are shown for visibility between the bars. Figure 8 shows how network congestion (measured by packet loss and delay) affects DDoS detection rates in the CIC-DDoS2019, UNSW-NB15, and IoT23 datasets.