From: An artificial immunity approach to malware detection in a mobile platform
Parameter | Model | Description | Value/range |
---|---|---|---|
Number of malicious apps | AIS, mAIS | The number of malicious apps in our test set. | 5 |
Number of benign apps | AIS, mAIS | The number of benign apps in our test set | 5 |
Initial detector set | AIS, mAIS | Number of detectors randomly generated | 1000 |
Width of detector | AIS, mAIS | The range for each interval during detector generation | 1.0 |
Tao | AIS, mAIS | Number of standard deviations to add or subtract from the mean when detectors are split | 1 |
rns | AIS, mAIS | The r value for the “non-self” identifying detector set | +GEFeS (1…100)a −GEFeS (100…200) |
rs | mAIS | The r value for the “self” identifying detector set | +GEFeS (1…100)a −GEFeS (100…200) |
Non-self detector set size | AIS, mAIS | The number of non-self mature detectors. | (1…40000)b |
Self detector set size | mAIS | The number self mature detectors | (1…40000)b |
Number of malicious apps detected | AIS, mAIS | Standard AIS: The number of malicious apps the mature detector set matched. mAIS: the number of malicious apps our committee machine was able to successfully classify. | (0…5) |
Number of benign apps detected | AIS, mAIS | Standard AIS: The number of benign apps the mature detector set did not match. mAIS: The number of benign apps our committee machine was able to unsuccessfully classify. | (0…5) |
Accuracy | AIS, mAIS | Percentage of correctly classified apps. | (0…100%) |
True positive rate | AIS, mAIS | Percentage of malicious apps correctly classified as malicious | (0…100%) |
True negative rate | AIS, mAIS | Percentage of benign apps correctly classified as benign | (0…100%) |
False positive rate | AIS, mAIS | Percentage of classified benign apps incorrectly classified as malicious | (0…100%) |
False negative rate | AIS, mAIS | Percentage of malicious apps incorrectly classified as benign | (0…100%) |