On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE | Paper Citation Record

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This paper cites Knerler, I.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Knerler, I

Reference 1

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This paper cites Security operations center: A systematic study and open challenges,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Security operations center: A systematic study and open challenges,

Reference 2

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This paper cites Suricata,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Suricata,

Reference 3

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This paper cites ’give me structure’: Synthesis and evaluation of a (network) threat analysis process supporting tier 1 investigations in a security operation center,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE ’give me structure’: Synthesis and evaluation of a (network) threat analysis process supporting tier 1 investigations in a security operation center,

Reference 4

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This paper cites True attacks, attack attempts, or benign triggers? an empirical measurement of network alerts in a security operations center,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE True attacks, attack attempts, or benign triggers? an empirical measurement of network alerts in a security operations center,

Reference 5

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This paper cites Ruling the unruly: Designing effective, low-noise network intrusion detection rules for security operations centers,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Ruling the unruly: Designing effective, low-noise network intrusion detection rules for security operations centers,

Reference 6

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This paper cites Alert alchemy: Soc workflows and decisions in the management of nids rules,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Alert alchemy: Soc workflows and decisions in the management of nids rules,

Reference 7

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This paper cites DeepCASE: Semi-Supervised Contextual Analysis of Security Events,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE DeepCASE: Semi-Supervised Contextual Analysis of Security Events,

Reference 8

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This paper cites Omega- log: High-fidelity attack investigation via transparent multi-layer log analysis,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Omega- log: High-fidelity attack investigation via transparent multi-layer log analysis,

Reference 9

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This paper cites Combating alert fatigue with alertpro: Context-aware alert prioritization using reinforcement learning for multi-step attack detection,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Combating alert fatigue with alertpro: Context-aware alert prioritization using reinforcement learning for multi-step attack detection,

Reference 10

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This paper cites Insomnia: Towards concept-drift robustness in network intrusion detection,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Insomnia: Towards concept-drift robustness in network intrusion detection,

Reference 11

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This paper cites Transcending transcend: Revisiting malware classification in the presence of concept drift,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Transcending transcend: Revisiting malware classification in the presence of concept drift,

Reference 12

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This paper cites Anomaly detection in the open world: Normality shift detection, explanation, and adap- tation,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Anomaly detection in the open world: Normality shift detection, explanation, and adap- tation,

Reference 13

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This paper cites xNIDS: Explaining deep learning-based network intrusion detection systems for active intrusion responses,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE xNIDS: Explaining deep learning-based network intrusion detection systems for active intrusion responses,

Reference 14

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This paper cites Ruling the rules: Quantifying the evolution of rulesets, alerts and incidents in network intrusion detection,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Ruling the rules: Quantifying the evolution of rulesets, alerts and incidents in network intrusion detection,

Reference 15

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This paper cites Detecting credential spearphishing attacks in enterprise settings,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Detecting credential spearphishing attacks in enterprise settings,

Reference 16

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This paper cites A systematic review on imbalanced data challenges in machine learning: Applications and solutions,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE A systematic review on imbalanced data challenges in machine learning: Applications and solutions,

Reference 17

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This paper cites An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics,

Reference 18

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE The base-rate fallacy and the difficulty of intrusion detection,

Reference 19

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This paper cites TESSERACT: Eliminating experimental bias in malware classification across space and time,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE TESSERACT: Eliminating experimental bias in malware classification across space and time,

Reference 20

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This paper cites Dos and don’ts of machine learning in computer security,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Dos and don’ts of machine learning in computer security,

Reference 21

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE The curse of dimensionality in data mining and time series prediction,

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This paper cites Error prevalence in nids datasets: A case study on cic-ids-2017 and cse-cic-ids-2018,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Error prevalence in nids datasets: A case study on cic-ids-2017 and cse-cic-ids-2018,

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This paper cites Sok: Explainable machine learning for computer security applications,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Sok: Explainable machine learning for computer security applications,

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE A review on the attention mechanism of deep learning,

Reference 25

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE A density-based algorithm for discovering clusters in large spatial databases with noise,

Reference 26

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE How Complex is your classification problem? A survey on measuring classification complexity

Reference 27

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This paper cites The effect of dataset size on training tweet sentiment classifiers,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE The effect of dataset size on training tweet sentiment classifiers,

Reference 28

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This paper cites Nodoze: Combatting threat alert fatigue with automated provenance triage,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Nodoze: Combatting threat alert fatigue with automated provenance triage,

Reference 29

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This paper cites Bad design smells in benchmark nids datasets,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Bad design smells in benchmark nids datasets,

Reference 30

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This paper cites ’give me structure’: Synthesis and evaluation of a (network) threat analysis process supporting tier 1 investigations in a security operation center,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE ’give me structure’: Synthesis and evaluation of a (network) threat analysis process supporting tier 1 investigations in a security operation center,

Reference 31

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This paper cites Troubleshooting an intrusion detection dataset: the cicids2017 case study,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Troubleshooting an intrusion detection dataset: the cicids2017 case study,

Reference 32

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Unresolved cited work

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Unresolved cited work

Reference 34

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This paper cites Regression with graphics: A second course in applied statistics,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Regression with graphics: A second course in applied statistics,

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This paper cites The kolmogorov-smirnov test for goodness of fit,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE The kolmogorov-smirnov test for goodness of fit,

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This paper cites Capturing tacit knowledge in security operation centers,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Capturing tacit knowledge in security operation centers,

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE A tale of four metrics,

Reference 38

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This paper cites A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE A unifying view of class overlap and imbalance: Key concepts, multi-view panorama, and open avenues for research,

Reference 39

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Focal loss for dense object detection,

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This paper cites Deepdive: Evaluating deepcase on dataset imbalance & validity of explanations,.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Deepdive: Evaluating deepcase on dataset imbalance & validity of explanations,

Reference 41

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This paper cites Random search for hyper-parameter optimization.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Random search for hyper-parameter optimization

Reference 42

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On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE O PTIMAL HYPERPARAMETERS FOR THE DIFFERENT DATASETS

Reference 45

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This paper cites Available: https://www.usenix.org/conference/ usenixsecurity19/presentation/pendlebury 9.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Available: https://www.usenix.org/conference/ usenixsecurity19/presentation/pendlebury 9

Reference 746

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This paper cites Available: https://doi.org/10.1145/357830.357849.

On the Effect of Ruleset Tuning and Data Imbalance on Explainable Network Security Alert Classifications: a Case-Study on DeepCASE Available: https://doi.org/10.1145/357830.357849

Reference 2000

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