<jupyter_start><jupyter_text>Experiment 1: DNS vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output>Loading Dataset: ../data/prepared/baseline/DNS_vs_all.csv
	To Dataset Cache: ../data/cache/baseline/DNS_vs_all.csv.pickle

Loading Dataset: ../data/prepared/timebased/DNS_vs_all.csv
	To Dataset Cache: ../data/cache/timebased/DNS_vs_all.csv.pickle

Running experiment #1:	DNS

Baseline results

Running K-Nearest Neighbors on 10000 samples
	Accuracy: 0.8965

              precision    recall  f1-score   support

           0       0.87      0.94      0.90      1024
           1       0.94      0.85      0.89       976

    accuracy                           0.90      2000
   macro avg       0.90      0.90      0.90      2000
weighted avg       0.90      0.90      0.90      2000


Time-based results

Running K-Nearest Neighbors on 10000 samples
	Accuracy: 0.8995

              precision    recall  f1-score   support

           0       0.86      0.96      0.91      1024
           1       0.95      0.84      0.89       976

    accuracy                           0.90      2000
   macro avg[...]<jupyter_text>Experiment 2: LDAP vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 3: MSSQL vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 4: NetBIOS vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 5: NTP vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 6: Portmap vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 7: SNMP vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 8: SSDP vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 9: Syn vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 10: TFTP vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 11: UDP vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output><jupyter_text>Experiment 12: UDP-lag vs All<jupyter_code>results = next(experiment)

visualize_learning_curve_train(results[Baseline])
visualize_learning_curve_train(results[Timebased])
visualize_report(results[Baseline])
visualize_report(results[Timebased])
visualize_confusion_matrix(results[Baseline])
visualize_confusion_matrix(results[Timebased])
visualize_roc(results[Baseline])
visualize_roc(results[Timebased])
visualize_pr_curve(results[Baseline])
visualize_pr_curve(results[Timebased])<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09393161238005136, 'fraction_numerical': 0.01784024868225436, 'mean_word_length': 8.390862944162437, 'pattern_counts': {'":': 0, '<': 48, '<?xml version=': 0, '>': 48, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 6, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '12', 'dir': 'jupyter-structured-clean-dedup', 'id': '444796', 'license': 'MIT', 'n_tokens_mistral': 2756, 'n_tokens_neox': 2422, 'n_words': 266, 'path': 'experiments_not_in_study/testing_faiss_knn.ipynb', 'repo_name': 'jehalladay/DDoS_Research', 'size': '7399'}