tree
version=v3
num_class=1
num_tree_per_iteration=1
label_index=0
max_feature_idx=46
objective=binary sigmoid:1
feature_names=srcPort destPort bytesOut bytesIn pktsIn pktsOut tlsIssuerDn_null tlsSubject_C_cnt tlsSubject_ST_cnt tlsSubject_L_cnt tlsSubject_O_cnt tlsSubject_OU_cnt tlsSubject_CN_cnt tlsSubject_cnt tlsIssuerDn_cnt tlsSni_cnt srcAddress_cnt destAddress_cnt tlsVersion_cnt destPort_cnt bytesOut_cnt bytesIn_cnt pktsIn_cnt pktsOut_cnt bytesOut_pktsIn bytesIn_pktsOut bytesIn_bytesOut pktsIn_pktsOut tlsVersion_num vec_add_w2v_1 vec_add_w2v_2 vec_add_w2v_3 vec_add_w2v_4 vec_add_w2v_5 vec_add_w2v_6 vec_add_w2v_7 vec_add_w2v_8 tlsSubject_num tlsIssuerDn_num srcAddress_mean srcAddress_std srcAddress_max srcAddress_min destAddress_mean destAddress_std destAddress_max destAddress_min
feature_infos=[5:65535] [20:64780] [62:207419406] [310:360644320] [3:321668] [1:227660] [0:1] [3:20402] [3:23015] [3:25529] [3:20603] [3:28094] [3:14806] [3:2055] [3:3684] [3:433] [3:138] [3:138] [3:32523] [3:37260] [3:279] [3:363] [3:2630] [3:2753] [1.1698113207547169:27181.017857142859] [2.4214949137549757:25656] [0.0022480729395580028:346.70270270270271] [0.039628482972136225:60] [1:3] [-0.64770543575286865:0.28681665658950806] [-0.78400695323944092:0.1916244775056839] [-0.23685339093208313:0.71100395917892456] [0.11590141803026199:0.93095993995666504] [-0.14976069331169128:0.82910436391830444] [-0.68868273496627808:0.28120881319046021] [-0.52362531423568726:0.34690839052200317] [-0.48820772767066956:0.47870063781738281] [1:17] [1:8] [47.25:145.5] [55.154328932550705:106.41281642734582] [172:254] [0:16] [5:149.75] [2.6809513236909019:97.238109812974045] [10:212] [0:10]
tree_sizes=3322 3366 3363 3396 3383 3381 3407 3391 3395 3430 3408 3434 3397 3394 3409 3397 3405 3405 3418 3389 3422 3427 3399 3403 3397 3415 3396 3434 3440 3397 3415 3408 3405 3402 3426 3402 3407 3404 3410 3410 3438 3404 3404 3423 3414 3405 3426 3423 3417 3416 3413 3411 3412 3421 3411 3430 3417 3436 3419 3428 3417 3424 3425 3419 3443 3437 3429 3435 3431 3406 3421 3432 3439 3432 3424 3406 3406 3414 3422 3416 3426 3449 3430 3436 3441 3429 3437 3431 3418 3447 3437 3433 3421 3426 3441 3432 3430 3427 3412 3418 3437 3431 3446 3419 3426 3446 3427 3439 3413 3434 3431 3413 3426 3419 3448 3433 3435 3431 3431 3451 3442 3422 3456 3438 3451 3460 3440 3443 3449 3444 3443 3446 3454 3434 3443 3463 3445 3434 3447 3435 3450 3458 3452 3464 3460 3452 3441 3460 3456 3461 3450 3440 3455 3444 3464 3444 3457 3473 3456 3434 3458 3467 3461 3450 3438 3462 3476 3459 3450 3445 3476 3466 3448 3468 3451 3461 3452 3474 3470 3445 3448 3482 3464 3455 3434 3457 3479 3458 3449 3460 3473 3447 3444 3459 3478 3453 3458 3428 3459 3422 3450 3442 3457 3466 3450 3441 3473 3430 3470 3444 3469 3454 3477 3444 3474 3452 3459 3453 3457 3463 3485 3456 3472 3457 3472 3453 3471 3480 3450 3459 3456 3450 3458 3457 3476 3479 3460 3471 3469 3479 3465 3477 3477 3475 3477 3465 3469 3466 3477 3488 3464 3488 3492 3483 3490 3478 3488 3470 3480 3493 3474 3489 3475 3473 3470 3483 3468 3478 3502 3478 3484 3490 3500 3481 3486 3473 3489 3494 3479 3497 3469 3504 3494 3474 3491 3483 3483 3471 3475 3479 3468 3467 3499 3479 3475 3493 3488 3469 3479 3488 3498 3502 3498 3494 3496 3497 3483 3495 3501 3486 3498 3494 3504 3481 3481 3502 3514 3481 3499 3494 3500 3490 3496 3487 3487 3501 3511 3512 3482 3490 3509 3501 3505 3491 3511 3496 3483 3495 3504 3482 3485 3509 3495 3510 3519 3506 3497 3511 3505 3508 3490 3519 3493 3482 3514 3491 3496 3505 3486 3478 3501 3506 3516 3512 3507 3501 3491 3527 3489 3494 3502 3506 3510 3489 3512 3495 3503 3501 3515 3488 3504 3507 3496 3502 3503 3495 3511 3520 3521 3505 3490 3484 3494 3497 3511 3504 3495 3510 3519 3509 3500 3494 3507 3503 3504 3523 3508 3525 3517 3498 3502 3509 3510 3482 3500 3506 3513 3523 3510 3510 3533 3503 3515 3513 3502 3524 3499 3508 3519 3506 3532 3525 3528 3502 3517 3511 3518 3496 3500 3509 3510 3526 3506 3510 3512 3517 3513 3488 3513 3505 3524 3517 3494 3523 3516 3520 3499 3529 3517 3511 3528 3523 3515 3503 3499 3528 3510 3509 3519 3515 3511 3532 3503 3524 3510 3521 3512 3507 3525 3507 3528 3525 3519 3535 3519 3513 3510 3508 3495 3529 3537 3509 3537 3524 3531 3520 3513 3527 3524 3525 3521 3508 3533 3527 3501 3520 3502 3535 3527 3512 3512 3522 3513 3515 3501 3517 3514 3516 3523 3527 3551 3529 3524 3514 3520 3533 3513 3524 3556 3532 3523 3539 3523 3528 3533 3542 3519 3534 3540 3535 3540 3540 3514 3531 3536 3544 3524 3533 3539 3520 3534 3533 3543 3544 3541 3528 3547 3554 3528 3533 3533 3551 3561 3556 3540 3550 3551 3521 3547 3538 3536 3543 3538 3546 3525 3552 3564 3540 3557 3548 3545 3539 3536 3535 3546 3537 3542 3521 3556 3548 3541 3553 3535 3568 3549 3551 3570 3551 3559 3552 3559 3550 3549 3559 3568 3561 3571 3551 3549 3568 3548 3562 3545 3563 3557 3552 3541 3551 3548 3574 3563 3549 3553 3543 3557 3560 3570 3556 3554 3547 3559 3553 3536 3562 3555 3554 3565 3574 3565 3533 3545 3583 3545 3566 3549 3573 3574 3569 3574 3579 3551 3548 3571 3547 3543 3555 3558 3554 3577 3576 3541 3557 3547 3573 3556 3563 3557 3577 3564 3562 3567 3557 3568 3558 3570 3541 3571 3552 3567 3562 3566 3567 3589 3578 3562 3558 3569 3576 3566 3566 3562 3570 3575 3563 3579 3563 3574 3577 3582 3562 3565 3587 3586 3561 3549 3555 3563 3592 3550 3571 3565 3564 3579 3567 3578 3578 3579 3569 3566 3585 3570 3565 3567 3578 3587 3568 3583 3581 3556 3574 3576 3567 3561 3555 3577 3563 3562 3565 3578 3581 3570 3562 3566 3571 3575 3574 3581 3567 3579 3574 3584 3574 3586 3572 3578 3571 3576 3571 3549 3580 3580 3592 3586 3592 3587 3572 3568 3579 3572 3569 3584 3594 3583 3583 3559 3565 3574 3573 3584 3583 3592 3582 3583 3585 3579 3577 3565 3599 3564 3578 3562
Tree=0
num_leaves=31
num_cat=0
split_feature=28 19 9 1 0 22 3 15 12 27 26 0 0 12 0 0 14 3 37 6 22 14 24 0 13 0 14 12 0 15
split_gain=15764.8 1424.73 1257.06 948.225 932.332 835.857 648.721 516.432 441.352 411.525 375.574 304.938 260.554 225.202 279.339 414.999 201.922 165.735 158.161 152.055 149.702 358.56 189.922 126.565 98.3487 96.3447 90.6261 90.5519 94.7825 87.3549
threshold=1.0500000000000003 394.50000000000006 222.50000000000003 1285.5000000000002 48929.000000000007 1951.5000000000002 799.50000000000011 34.500000000000007 1.0000000180025095e-35 1.2849025974025976 0.17303202990768005 51735.500000000007 48929.000000000007 1.0000000180025095e-35 49836.500000000007 48929.000000000007 2869.5000000000005 3565.5000000000005 7.5000000000000009 1.0000000180025095e-35 1951.5000000000002 196.00000000000003 269.6733333333334 1463.0000000000002 45.500000000000007 49201.500000000007 406.00000000000006 13.500000000000002 52968.500000000007 55.500000000000007
decision_type=8 8 10 2 2 10 2 8 10 2 2 2 2 10 2 2 10 2 2 2 10 8 2 2 10 2 10 10 2 10
left_child=4 3 5 7 23 10 24 -2 -5 16 17 12 -7 14 15 -8 19 -3 -13 -6 -15 22 -22 -1 25 -4 -17 28 -23 -14
right_child=1 2 6 8 9 11 13 -9 -10 -11 -12 18 29 20 -16 26 -18 -19 -20 -21 21 27 -24 -25 -26 -27 -28 -29 -30 -31
leaf_value=-1.2944064308614052 -1.4797412007613604 -1.263570444344003 -1.4986079756771615 -1.2513243218614352 -1.3746468783021311 -1.5070449514454509 -1.5147924284637293 -1.2171839181413819 -1.5134357177945064 -1.4517782800548893 -1.4978944330427895 -1.4806904437247808 -1.2146891637395782 -1.5149729679651533 -1.503771681212571 -1.2364606850552027 -1.5015966859858279 -1.452844615351466 -1.2480579647650487 -1.2002290604584522 -1.2221539807709785 -1.3790500524184557 -1.4447324099193815 -1.5149383731948851 -1.5019525624399859 -1.2610049517339943 -1.4288334749304952 -1.5024488887779746 -1.5053156499054732 -1.4374547112162435
leaf_weight=6.1291378736495963 22.984267026185989 26.508521303534508 4.1371680647134772 17.161586046218872 15.169616237282751 8.5807930231094343 29.573090240359306 91.937068104743958 97.453292191028595 17.774499833583832 176.97885610163212 30.186004027724266 75.541624292731285 2877.1705463379622 100.05817578732967 31.565060049295425 5.2097671926021567 19.613241195678711 7.5081938952207556 581.96164110302925 15.782530024647711 30.032775580883026 21.2987541109
python实现的基于机器学习的恶意加密流量识别系统源码（高分毕业设计）.zip
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python实现的基于机器学习的恶意加密流量识别系统源码（高分毕业设计）.zip （139个子文件） 
fusai_w2v_add_8.model.bin 9KB fusai_w2v_add_8.model.bin 9KB config.cfg 88B config.cfg 68B config.cfg 67B test_1.csv 13.35MB test.csv 13.35MB train.csv 10.32MB train.csv 10.32MB test.csv 1KB data.csv 572B test_1.csv 294B .gitignore 47B .gitignore 47B Malicious-TLS-Detection-byRFandLGBM.iml 743B Malicious-TLS-Detection-byRFandLGBM.iml 743B RandomForestClassifier.joblib 8.66MB RandomForestClassifier.joblib 8.66MB LGBMClassifier.joblib 403KB LGBMClassifier.joblib 403KB
pic2.JPG 147KB dns.log 8.04MB conn_label.log 4.99MB conn.log 4.57MB files.log 1.18MB x509.log 893KB http.log 582KB ssl.log 509KB weird.log 15KB ntp.log 2KB packet_filter.log 254B README.EN.md 17KB README.zh_CN.md 16KB cnt_code_dict.pkl 1.45MB cnt_code_dict.pkl 1.45MB
pic1.png 386KB pic4.png 259KB pic3.png 180KB pic6.png 128KB pic7.PNG 31KB pic5.png 24KB connetion_tuple1.py 25KB connetion_tuple.py 24KB main_train.py 11KB process_log1.py 11KB process_log.py 11KB Dataset.py 9KB analyze_log1.py 8KB analyze_log.py 8KB label_log1.py 6KB label_log.py 6KB evaluate_data.py 6KB print_manager.py 4KB print_manager1.py 4KB project.py 4KB module2main.py 2KB pre_main.py 2KB evaluate_data1.py 2KB preprocess.py 1KB __label__.py 1KB pre_label.py 1KB read_file.py 888B read_file1.py 847B detect_lightGBM.py 839B config_manager1.py 806B config_manager.py 762B detect_randomforest.py 622B AlarmMetric.py 523B __init__.py 44B __init__.py 44B __init__.py 30B __init__.py 25B __init__.py 0B tf_random_forest.cpython-36.pyc 22KB connetion_tuple.cpython-37.pyc 19KB connetion_tuple1.cpython-37.pyc 19KB connetion_tuple.cpython-39.pyc 19KB connetion_tuple.cpython-37.pyc 18KB TFrandomforest.cpython-36.pyc 13KB main_train.cpython-37.pyc 11KB print_manager.cpython-39.pyc 6KB print_manager.cpython-37.pyc 6KB print_manager.cpython-37.pyc 6KB print_manager1.cpython-37.pyc 6KB Dataset.cpython-37.pyc 6KB __dataset__.cpython-36.pyc 6KB analyze_log.cpython-39.pyc 5KB analyze_log.cpython-37.pyc 5KB analyze_log1.cpython-37.pyc 5KB analyze_log.cpython-37.pyc 5KB Dataset.cpython-39.pyc 5KB project.cpython-37.pyc 4KB label_log.cpython-39.pyc 4KB label_log.cpython-37.pyc 4KB label_log1.cpython-37.pyc 4KB label_log.cpython-37.pyc 4KB evaluate_data.cpython-37.pyc 4KB dataset.cpython-36.pyc 2KB SplitData.cpython-36.pyc 2KB evaluate_data1.cpython-37.pyc 2KB共 139 条
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qq_589887662024-05-31资源很实用，对我启发很大，有很好的参考价值，内容详细。
