## Chart Type: Receiver Operating Characteristic (ROC) Curves ### Overview The image is a Receiver Operating Characteristic (ROC) curve plot, comparing the performance of different attack scenarios (Dipper-5, Dipper-10, Dipper-10-5) against a "No Attack" baseline and a "Random Guess" baseline. The plot shows the True Positive Rate (TPR) against the False Positive Rate (FPR) for each scenario. The Area Under the Curve (AUC) is provided for each scenario in the legend. ### Components/Axes * **Title:** Receiver Operating Characteristic (ROC) Curves * **X-axis:** False Positive Rate (FPR), ranging from 0.0 to 1.0 in increments of 0.2. * **Y-axis:** True Positive Rate (TPR), ranging from 0.0 to 1.0 in increments of 0.2. * **Grid:** The plot has a grid for easier reading of values. * **Legend:** Located in the bottom-right corner, the legend identifies each curve by attack scenario and its corresponding AUC value. * Blue: No Attack (AUC=1.0000) * Green: Dipper-5 (AUC=0.9468) * Orange: Dipper-10 (AUC=0.9589) * Red: Dipper-10-5 (AUC=0.9101) * Dashed Gray: Random Guess ### Detailed Analysis * **No Attack (Blue):** The blue line representing "No Attack" rises vertically to a TPR of 1.0 at an FPR of approximately 0.0, and then remains at 1.0 for all FPR values. * **Dipper-5 (Green):** The green line representing "Dipper-5" rises sharply initially, then gradually increases to a TPR of approximately 0.98 at an FPR of approximately 0.6. * **Dipper-10 (Orange):** The orange line representing "Dipper-10" rises sharply initially, then gradually increases to a TPR of approximately 0.99 at an FPR of approximately 0.6. * **Dipper-10-5 (Red):** The red line representing "Dipper-10-5" rises less sharply than the other attack scenarios, reaching a TPR of approximately 0.95 at an FPR of approximately 0.6. * **Random Guess (Dashed Gray):** The dashed gray line representing "Random Guess" is a diagonal line from (0.0, 0.0) to (1.0, 1.0). ### Key Observations * The "No Attack" scenario has a perfect AUC of 1.0000, indicating perfect classification. * The "Dipper-10" scenario has the highest AUC among the attack scenarios (0.9589), followed by "Dipper-5" (0.9468) and "Dipper-10-5" (0.9101). * All attack scenarios perform significantly better than the "Random Guess" baseline. * The ROC curves for the attack scenarios are clustered relatively close together, suggesting similar performance characteristics. ### Interpretation The ROC curves illustrate the performance of different attack scenarios in terms of their ability to correctly classify events (True Positives) while minimizing false alarms (False Positives). The "No Attack" scenario represents an ideal case where there are no false positives and all true positives are correctly identified. The attack scenarios show varying degrees of performance, with "Dipper-10" performing the best among them. The AUC values provide a quantitative measure of the overall performance of each scenario, with higher AUC values indicating better performance. The "Random Guess" baseline represents the performance of a classifier that makes random predictions, providing a lower bound for comparison. The closer the ROC curve is to the top-left corner, the better the performance of the classifier.

## Chart: Receiver Operating Characteristic (ROC) Curves ### Overview The image presents a Receiver Operating Characteristic (ROC) curve chart, comparing the performance of a detection system under different attack scenarios ("No Attack", "Dipper-5", "Dipper-10", "Dipper-10-5") against a random guess baseline. The chart plots the True Positive Rate (TPR) against the False Positive Rate (FPR) for each scenario. The Area Under the Curve (AUC) is provided for each ROC curve. ### Components/Axes * **Title:** Receiver Operating Characteristic(ROC) Curves * **X-axis:** False Positive Rate (FPR) - Scale ranges from 0.0 to 1.0, with tick marks at 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. * **Y-axis:** True Positive Rate (TPR) - Scale ranges from 0.0 to 1.0, with tick marks at 0.0, 0.2, 0.4, 0.6, 0.8, and 1.0. * **Legend:** Located in the bottom-right corner of the chart. * "No Attack" - Blue line, AUC = 1.0000 * "Dipper-5" - Green line, AUC = 0.9468 * "Dipper-10" - Orange line, AUC = 0.9589 * "Dipper-10-5" - Red line, AUC = 0.9101 * "Random Guess" - Gray dashed line. ### Detailed Analysis * **No Attack (Blue Line):** The blue line starts at (0.0, 0.0) and quickly rises vertically to (approximately 0.0, 1.0) and then remains at TPR = 1.0 for all FPR values. This indicates perfect separation between positive and negative classes when no attack is present. AUC = 1.0000. * **Dipper-5 (Green Line):** The green line starts at (0.0, 0.0) and rises gradually, reaching approximately (0.2, 0.8) and then continues to rise more slowly, approaching (1.0, 1.0). This indicates good, but not perfect, separation. AUC = 0.9468. * **Dipper-10 (Orange Line):** The orange line starts at (0.0, 0.0) and rises more steeply than the green line, reaching approximately (0.1, 0.9) and then leveling off, approaching (1.0, 1.0). This suggests better separation than "Dipper-5". AUC = 0.9589. * **Dipper-10-5 (Red Line):** The red line starts at (0.0, 0.0) and rises less steeply than the other attack scenarios, reaching approximately (0.3, 0.7) and then rising more gradually. This indicates the poorest separation among the attack scenarios. AUC = 0.9101. * **Random Guess (Gray Dashed Line):** The gray dashed line is a diagonal line from (0.0, 0.0) to (1.0, 1.0). This represents the performance of a random classifier. ### Key Observations * The "No Attack" scenario exhibits perfect classification performance (AUC = 1.0). * The "Dipper-10" scenario has the highest AUC among the attack scenarios (0.9589), indicating the best performance in distinguishing between positive and negative cases. * The "Dipper-10-5" scenario has the lowest AUC among the attack scenarios (0.9101), indicating the worst performance. * All attack scenarios perform better than random guessing. * The ROC curves for the attack scenarios all lie above the "Random Guess" line, indicating that the detection system is capable of distinguishing between attack and non-attack scenarios to some degree. ### Interpretation The ROC curves demonstrate the effectiveness of the detection system in identifying attacks. The AUC values quantify this effectiveness, with higher values indicating better performance. The "No Attack" scenario serves as a baseline for ideal performance. The differences in AUC values between the attack scenarios suggest that the detection system is more sensitive to certain types of attacks ("Dipper-10") than others ("Dipper-10-5"). The fact that all attack scenarios have AUC values greater than 0.5 indicates that the detection system is performing better than random chance. The curves show a trade-off between TPR and FPR; increasing the threshold for positive classification will reduce the FPR but also reduce the TPR, and vice versa. The choice of an appropriate threshold depends on the specific application and the relative costs of false positives and false negatives.

## ROC Curves: Receiver Operating Characteristic Curves ### Overview The image displays a Receiver Operating Characteristic (ROC) curve, which visualizes the trade-off between the **True Positive Rate (TPR)** and **False Positive Rate (FPR)** for different classification models. The curves are plotted against a grid with axes ranging from 0.0 to 1.0. A legend identifies five data series, each represented by a distinct color and line style. ### Components/Axes - **X-axis**: False Positive Rate (FPR) (0.0 to 1.0) - **Y-axis**: True Positive Rate (TPR) (0.0 to 1.0) - **Legend**: - **No Attack** (blue solid line, AUC=1.0000) - **Dipper-5** (green solid line, AUC=0.9468) - **Dipper-10** (orange solid line, AUC=0.9589) - **Dipper-10-5** (red solid line, AUC=0.9101) - **Random Guess** (gray dashed line) - **Axis Markers**: Grid lines at 0.0, 0.2, 0.4, 0.6, 0.8, 1.0 for both axes. ### Detailed Analysis 1. **No Attack (Blue Line)**: - A horizontal line at TPR=1.0 across all FPR values. - Indicates perfect classification with no false positives. - AUC=1.0000 (ideal performance). 2. **Random Guess (Gray Dashed Line)**: - A diagonal line from (0,0) to (1,1). - Represents a baseline for random classification. 3. **Dipper-5 (Green Line)**: - Starts at (0,0) and curves upward to the left, reaching TPR≈0.95 at FPR≈0.1. - AUC=0.9468. 4. **Dipper-10 (Orange Line)**: - Starts at (0,0) and curves upward to the left, reaching TPR≈0.98 at FPR≈0.05. - AUC=0.9589 (best among the Dipper models). 5. **Dipper-10-5 (Red Line)**: - Starts at (0,0) and curves upward to the left, reaching TPR≈0.92 at FPR≈0.15. - AUC=0.9101 (lowest among the Dipper models). ### Key Observations - The **No Attack** model achieves perfect performance (AUC=1.0000), suggesting it is optimized for scenarios without attacks. - **Dipper-10** outperforms other models (AUC=0.9589), indicating superior discrimination between attack and non-attack cases. - **Dipper-10-5** has the lowest AUC (0.9101), suggesting it is less effective than the other models. - All models (except Random Guess) outperform the baseline (Random Guess line). ### Interpretation The ROC curves demonstrate the effectiveness of different models in distinguishing between attack and non-attack scenarios. The **No Attack** model’s perfect performance implies it is tailored for a specific context (e.g., no attacks in the dataset). Among the Dipper models, **Dipper-10** achieves the highest AUC, indicating it balances TPR and FPR most effectively. The **Dipper-10-5** model’s lower AUC suggests it may have suboptimal threshold settings or design choices. The **Random Guess** line serves as a critical benchmark, confirming that all models perform better than random chance. The curves highlight the importance of model selection and threshold tuning in classification tasks, particularly in security or anomaly detection applications where minimizing false positives is critical.