Fig 1.
Working process of fuzzing.
Table 1.
Data extraction form items.
Fig 2.
Flowchart of the systematic review process.
Table 2.
Selected primary studies in the field of machine learning based fuzzing.
Table 3.
Distribution of research literature based on machine learning for different steps of fuzzing.
Table 4.
Distribution of machine learning arithmetics for fuzzing.
Table 5.
Pre-processing methods for machine learning techniques in fuzzing.
Table 6.
Evaluation metrics and details for machine learning models in fuzzing.
Table 7.
Evaluation metrics and details for fuzzers based on machine learning.
Table 8.
Analysis of hyperparameters setting of machine learning models for fuzzing.
Fig 3.
Comparison of Accuracy between different models.
Fig 4.
Comparison of Precision between different models.
Fig 5.
Comparison of Recall between different models.
Fig 6.
Comparison of Loss between different models.
Table 9.
Results of cove coverage improvement of fuzzers based on machine learning.
Table 10.
Results of Machine learning-based fuzzers compared to baselines in unique crashes and bugs discovery.
Table 11.
Statistics on the number of vulnerabilities found on LAVA-M dataset by machine learning-based fuzzers and traditional fuzzers.
Table 12.
Results of efficiency improvement of fuzzers based on machine learning.