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output_unbalanced dataset.txt
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output_unbalanced dataset.txt
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Reading data from CSV file...
Found (5560) malwares in csv file.
Reading dataset files...
Found (129013) files to classify.
Found (5560) malware files.
Found (123453) safe files.
Features & Labels arrays' shapes, respectively: (129013, 8) (129013,)
Features Selection based on KBest:
scores for each attribute and the 4 attributes chosen:
[ 1830.199 63128.478 553.346 3884.877 4968.117 5421.709 5773.524
1302.717]
[[11 7 6 11]
[11 6 5 6]
[ 4 2 2 2]
[ 1 1 1 2]
[21 1 1 1]]
Features Selection based on Recursive Features Elimination:
RFE chose the the top 4 features:
Numbers Features: 4
Selected Features: [ True True True False False True False False]
Feature Ranking: [1 1 1 4 2 1 3 5]
Features Selection based on Extra trees classifier:
Feature ranking (ordered DESC) using extra trees classifier:
1. feature 1 (0.268104)
2. feature 7 (0.145481)
3. feature 6 (0.133961)
4. feature 2 (0.123148)
5. feature 3 (0.119823)
6. feature 0 (0.095896)
7. feature 4 (0.058895)
8. feature 5 (0.054692)
Features Selection based on Random Forest classifier:
Feature ranking (ordered DESC) using random forest classifier:
1. feature 1 (0.246922)
2. feature 7 (0.147647)
3. feature 2 (0.134835)
4. feature 6 (0.133174)
5. feature 3 (0.097858)
6. feature 0 (0.095572)
7. feature 4 (0.075077)
8. feature 5 (0.068916)
Training data shape (x, y): (103210, 8) (103210,)
Testing data shape (x, y): (25803, 8) (25803,)
-------------SVM Model-------------
SVM Evaluation parameters:
Accuracy is 96.984847 (in percentage)
Confusion Matrix:
[[24585 88]
[ 690 440]]
Recall score is 0.389381.
Precision score is 0.833333.
F1 score is 0.530760.
classification Report:
precision recall f1-score support
0 0.97 1.00 0.98 24673
1 0.83 0.39 0.53 1130
micro avg 0.97 0.97 0.97 25803
macro avg 0.90 0.69 0.76 25803
weighted avg 0.97 0.97 0.96 25803
-----------------------------------
-------------SVM, C value Model-------------
C value: 10
SVM, tuned C val Evaluation parameters:
Accuracy is 97.279386 (in percentage)
Confusion Matrix:
[[24594 79]
[ 623 507]]
Recall score is 0.448673.
Precision score is 0.865188.
F1 score is 0.590909.
classification Report:
precision recall f1-score support
0 0.98 1.00 0.99 24673
1 0.87 0.45 0.59 1130
micro avg 0.97 0.97 0.97 25803
macro avg 0.92 0.72 0.79 25803
weighted avg 0.97 0.97 0.97 25803
-----------------------------------
-------------SVM, C value Model-------------
C value: 100
SVM, tuned C val Evaluation parameters:
Accuracy is 97.469287 (in percentage)
Confusion Matrix:
[[24577 96]
[ 557 573]]
Recall score is 0.507080.
Precision score is 0.856502.
F1 score is 0.637021.
classification Report:
precision recall f1-score support
0 0.98 1.00 0.99 24673
1 0.86 0.51 0.64 1130
micro avg 0.97 0.97 0.97 25803
macro avg 0.92 0.75 0.81 25803
weighted avg 0.97 0.97 0.97 25803
-----------------------------------
-------------SVM, C value Model-------------
C value: 1000
SVM, tuned C val Evaluation parameters:
Accuracy is 97.740573 (in percentage)
Confusion Matrix:
[[24544 129]
[ 454 676]]
Recall score is 0.598230.
Precision score is 0.839752.
F1 score is 0.698708.
classification Report:
precision recall f1-score support
0 0.98 0.99 0.99 24673
1 0.84 0.60 0.70 1130
micro avg 0.98 0.98 0.98 25803
macro avg 0.91 0.80 0.84 25803
weighted avg 0.98 0.98 0.98 25803
-----------------------------------
-------------RF Model-------------
RF Evaluation parameters:
Accuracy is 98.879975 (in percentage)
Confusion Matrix:
[[24601 72]
[ 217 913]]
Recall score is 0.807965.
Precision score is 0.926904.
F1 score is 0.863357.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.93 0.81 0.86 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
5.61 Time to finish with default nJobs
-------------RF Model with nJobs-------------
N_Jobs: 10
RF Evaluation parameters:
Accuracy is 98.876100 (in percentage)
Confusion Matrix:
[[24598 75]
[ 215 915]]
Recall score is 0.809735.
Precision score is 0.924242.
F1 score is 0.863208.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.92 0.81 0.86 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
2.46 Time to finish with 10 nJobs
-------------RF Model with nJobs-------------
N_Jobs: 10
N_Estimators: 1000
RF Evaluation parameters:
Accuracy is 98.887726 (in percentage)
Confusion Matrix:
[[24596 77]
[ 210 920]]
Recall score is 0.814159.
Precision score is 0.922768.
F1 score is 0.865068.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.92 0.81 0.87 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.91 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
22.43 Time to finish with 10 nJobs and 1000 nEstimators
-------------Extra Trees Model-------------
ET Evaluation parameters:
Accuracy is 98.938108 (in percentage)
Confusion Matrix:
[[24605 68]
[ 206 924]]
Recall score is 0.817699.
Precision score is 0.931452.
F1 score is 0.870877.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.93 0.82 0.87 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.91 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
5.39 Time to finish ET with 100 nEstimators
-------------Extra Trees Model-------------
Number of estimators: 500
ET Evaluation parameters:
Accuracy is 98.899353 (in percentage)
Confusion Matrix:
[[24603 70]
[ 214 916]]
Recall score is 0.810619.
Precision score is 0.929006.
F1 score is 0.865784.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.93 0.81 0.87 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
25.73 Time to finish ET with 500 nEstimators
-------------Extra Trees Model-------------
Number of estimators: 1000
ET Evaluation parameters:
Accuracy is 98.899353 (in percentage)
Confusion Matrix:
[[24602 71]
[ 213 917]]
Recall score is 0.811504.
Precision score is 0.928138.
F1 score is 0.865911.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.93 0.81 0.87 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
51.93 Time to finish ET with 1000 nEstimators
-------------Extra Trees Model-------------
Number of jobs: 100
ET Evaluation parameters:
Accuracy is 98.860598 (in percentage)
Confusion Matrix:
[[24595 78]
[ 216 914]]
Recall score is 0.808850.
Precision score is 0.921371.
F1 score is 0.861451.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.92 0.81 0.86 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
0.41 Time to finish ET with 100 nJobs
-------------Extra Trees Model-------------
Number of jobs: 500
ET Evaluation parameters:
Accuracy is 98.837344 (in percentage)
Confusion Matrix:
[[24597 76]
[ 224 906]]
Recall score is 0.801770.
Precision score is 0.922607.
F1 score is 0.857955.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.92 0.80 0.86 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
0.49 Time to finish ET with 500 nJobs
-------------Extra Trees Model-------------
Number of jobs: 1000
ET Evaluation parameters:
Accuracy is 98.848971 (in percentage)
Confusion Matrix:
[[24606 67]
[ 230 900]]
Recall score is 0.796460.
Precision score is 0.930714.
F1 score is 0.858369.
classification Report:
precision recall f1-score support
0 0.99 1.00 0.99 24673
1 0.93 0.80 0.86 1130
micro avg 0.99 0.99 0.99 25803
macro avg 0.96 0.90 0.93 25803
weighted avg 0.99 0.99 0.99 25803
-----------------------------------
0.59 Time to finish ET with 1000 nJobs
-------------RFE Model-------------
Accuracy is 95.876448 (in percentage)
Confusion Matrix:
[[24545 128]
[ 936 194]]
Recall score is 0.171681.
Precision score is 0.602484.
F1 score is 0.267218.
classification Report:
precision recall f1-score support
0 0.96 0.99 0.98 24673
1 0.60 0.17 0.27 1130
micro avg 0.96 0.96 0.96 25803
macro avg 0.78 0.58 0.62 25803
weighted avg 0.95 0.96 0.95 25803
-----------------------------------
-------------GS, RF Model-------------
GS based on RF model Evaluation parameters:
Accuracy is 97.430531 (in percentage)
Confusion Matrix:
[[24630 43]
[ 620 510]]
Recall score is 0.451327.
Precision score is 0.922242.
F1 score is 0.606061.
classification Report:
precision recall f1-score support
0 0.98 1.00 0.99 24673
1 0.92 0.45 0.61 1130
micro avg 0.97 0.97 0.97 25803
macro avg 0.95 0.72 0.80 25803
weighted avg 0.97 0.97 0.97 25803
-----------------------------------
-------------GS, SVC Model-------------
... code was terminated after 75 hours of running and no results ...
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