This introductory lesson is for data scientists / machine learning engineers to get a basic understanding of common malware types as well as cybersecurity researchers and experts to get a basic understanding of relevant machine learning model evaluation metrics for classification tasks
CONTENT OF THIS VIDEO
00:00 Intro
01:42 Introduction to Malware types
06:13 Malware Analysis tools and types
10:13 Precision, Recall, Accuracy and F1-Score
13:59 Sensitivity, Specitivity and Balanced Accuracy Score
16:43 Receiver operating characteristic curve (ROC)
20:00 Example cancer detection classifier threshold with true positive rate and false positive rate
22:08 Precision Recall Curve
24:47 Average Precision Score
Handshake between Cybersecurity and Machine Learning for IT-Security Tools and Software development has been gaining more attention in recent years. The data landscape to train Machine Learning models is more accessible for cybersecurity researchers and experts.
Machine Learning algorithms applied to tabular datasets for classification problems can be applied for malware analysis and detection. Labelled data is therefore required to train supervised ML algorithms for binary classification models to detect malware and multiclass classification for malware type analysis. This intro lesson will give a brief overview of some common malware types.
Another challenge is to build a robust machine learning model for classification tasks, as in most real world cases the data is highly imbalanced. An overfitted Machine Learning model will most likely fail to detect malware, high cost and negative business impact are worst case scenarios and consequences of applying overfitted Machine Learning model for malware detection.
In order to gain acceptance for applying Machine Learning models within the malware analysis process, it is recommended to use model evaluation metrics like average precision score, balanced accuracy score and precision recall curve. This introductory lesson will enable you to get a deep understanding of the mentioned evaluation metrics and differentiate these metrics with precision, accuracy, and receiver operating characteristic curve.
At the end of the day cybersecurity experts will need a robust machine learning model to embed into the IT-Security Tools and Software development process which can consistently detect or analyse malware and outperform conventional software solutions.
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#confusionmatrix #malwaretypes #malwaredetection
#supervisedmachinelearning #machinelearning #recall
#malwaredetection2022 #malwaredetection #f1score
#cybersecurity #malware
Related Phrases:
Machine Learning, Malware Detection, Cybersecurity 2022, Machine Learning, Malware Detection Techniques, CatBoost, Randomforest, Malware Analysis, Confusion Matrix, Average Precision Score, Precision Recall Curve
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10 июл 2024
