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==========================] - 2969s 2s/step - loss: 0.2642 - accuracy: 0.9627 |
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Loss: 0.26420989632606506 |
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Accuracy: 0.9626917243003845 |
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Predicting the class labels... |
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Classification report: |
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/anaconda/envs/azureml_py38/lib/python3.8/site-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior. |
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_warn_prf(average, modifier, msg_start, len(result)) |
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precision recall f1-score support |
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|
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9002Rat 1.00 0.71 0.83 133 |
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AnglerEK 1.00 0.67 0.80 12 |
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BandarChorRansomware 1.00 0.86 0.92 7 |
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Banload 0.97 0.85 0.90 362 |
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Bartalex 1.00 0.75 0.86 4 |
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BasBanke 1.00 1.00 1.00 1 |
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Bazarbackdoor 0.95 0.99 0.97 8498 |
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Benign 0.94 0.87 0.90 1298 |
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BitRAT 0.00 0.00 0.00 1 |
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Bizarro 1.00 0.80 0.89 10 |
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BlackMatter 1.00 1.00 1.00 1 |
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BlackholeEK 0.82 0.76 0.79 62 |
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CVE-2008-2551 1.00 0.67 0.80 3 |
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CVE-2015-0359 1.00 1.00 1.00 3 |
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CVE-2017-11882 0.98 0.46 0.62 138 |
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CertBreaker 1.00 1.00 1.00 2 |
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ClownicRansomware 0.00 0.00 0.00 1 |
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CobaltStrike 0.98 0.79 0.87 100 |
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CobraCarbonSystem 0.96 0.90 0.93 746 |
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CoreShell 0.93 0.89 0.91 45 |
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DearCryRansomware 1.00 1.00 1.00 1 |
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DecafRansomware 0.00 0.00 0.00 2 |
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DistTrack 1.00 0.88 0.94 17 |
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DnSpyTrojan 1.00 0.71 0.83 7 |
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Exaramel 0.00 0.00 0.00 1 |
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EyService 1.00 0.77 0.87 31 |
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FakeDivX 1.00 0.97 0.99 35 |
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Fanny 1.00 1.00 1.00 255 |
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Fareit 1.00 1.00 1.00 373 |
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FighterPOS 0.97 0.88 0.92 41 |
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FritzFrog 1.00 1.00 1.00 16 |
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Gamaredon 0.92 0.81 0.86 113 |
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GandCrab 1.00 1.00 1.00 5689 |
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GootLoader 1.00 1.00 1.00 3808 |
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Grief 1.00 1.00 1.00 8 |
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GrimPlant 0.99 0.97 0.98 384 |
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HavexRat 1.00 0.83 0.90 23 |
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HawkEyeKeylogger 1.00 1.00 1.00 25 |
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HeaderTip 0.82 1.00 0.90 9 |
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Hoplight 0.51 0.22 0.31 260 |
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Houdini 0.97 0.78 0.87 37 |
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HyperSSL 1.00 0.88 0.93 8 |
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IcedId 0.99 0.99 0.99 2103 |
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Infy 0.94 0.97 0.96 2754 |
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IsmAgent 0.94 0.89 0.91 18 |
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Jaff 0.96 0.96 0.96 25 |
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KRBanker 0.96 0.99 0.98 1846 |
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Keybase 0.97 0.91 0.94 261 |
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KghSpy 1.00 0.73 0.84 11 |
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Kobalos 1.00 0.67 0.80 6 |
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Korlia 0.95 0.88 0.91 192 |
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LockyRansomware 0.67 1.00 0.80 16 |
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LokiPasswordStealer 0.97 0.99 0.98 3246 |
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Magnat 1.00 0.71 0.83 35 |
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Maze 0.97 0.93 0.95 236 |
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MedusaLocker 1.00 1.00 1.00 1 |
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Mispadu 0.88 0.58 0.70 12 |
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Molerats 0.95 0.75 0.84 28 |
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MooBot 1.00 1.00 1.00 1 |
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Moqhao 1.00 0.80 0.89 10 |
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MosesStaff 1.00 1.00 1.00 2 |
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Nanhaishu 0.80 1.00 0.89 4 |
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Networm 1.00 1.00 1.00 1 |
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Nimrev 1.00 0.95 0.98 88 |
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NjRat 0.96 0.82 0.89 33 |
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NuclearEK 0.00 0.00 0.00 1 |
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Ohagi 0.97 0.97 0.97 244 |
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Okiru 0.93 0.87 0.90 31 |
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Oscorp 1.00 0.50 0.67 2 |
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Owowa 1.00 0.67 0.80 3 |
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PIVY 1.00 0.94 0.97 18 |
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PandaBanker 0.97 0.97 0.97 257 |
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PassCV 1.00 0.88 0.93 90 |
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Pay2Key 0.93 0.88 0.90 16 |
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Phorpiex 1.00 1.00 1.00 22 |
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Poseidon 1.00 1.00 1.00 14 |
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Powersniff 1.00 1.00 1.00 18 |
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ProjectSauron 1.00 0.82 0.90 11 |
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PwnPOS 1.00 1.00 1.00 8 |
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QakBot 0.98 0.99 0.99 3332 |
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QuantumRansomware 1.00 1.00 1.00 1 |
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QuasarRAT 1.00 1.00 1.00 1 |
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Ramdo 0.99 0.97 0.98 772 |
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RansomExx 0.99 0.96 0.98 148 |
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Razy 1.00 0.60 0.75 5 |
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Remcos 1.00 0.80 0.89 5 |
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Retefe 0.00 0.00 0.00 1 |
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RokRAT 1.00 0.33 0.50 6 |
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Rovnix 1.00 0.95 0.97 220 |
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Sakula 1.00 1.00 1.00 2093 |
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Sanya 1.00 0.75 0.86 8 |
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ShellCrew 1.00 0.97 0.99 38 |
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Shellbot 0.00 0.00 0.00 1 |
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ShimRAT 1.00 0.89 0.94 35 |
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Sierra 0.64 0.79 0.71 420 |
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Slave 1.00 1.00 1.00 17 |
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SmokeLoader 0.92 0.90 0.91 488 |
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SpiderpigRAT 0.97 0.89 0.92 333 |
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SshNet 0.79 0.65 0.71 17 |
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Stegoloader 0.94 0.84 0.89 19 |
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TeslaCrypt 1.00 0.75 0.86 4 |
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Tinba 1.00 0.72 0.84 18 |
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Tor2Mine 0.75 0.75 0.75 8 |
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UBoatRAT 0.86 0.75 0.80 8 |
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Upatre 0.98 0.99 0.98 1681 |
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Valyria 1.00 0.71 0.83 7 |
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Voho 1.00 0.83 0.91 6 |
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WellMail 0.50 0.74 0.60 38 |
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WellMess 0.50 0.26 0.34 38 |
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WinMM 0.93 0.95 0.94 676 |
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WinsLoader 1.00 1.00 1.00 14 |
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WpBruteBot 0.88 0.93 0.90 1419 |
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XFilesStealer 1.00 0.33 0.50 3 |
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XRat 1.00 1.00 1.00 2 |
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XTunnel 0.97 0.92 0.94 704 |
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XXMM 0.96 0.82 0.88 28 |
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XsPlus 1.00 0.96 0.98 26 |
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Zumanek 1.00 0.40 0.57 5 |
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zLoader 0.90 0.91 0.91 644 |
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|
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accuracy 0.96 47523 |
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macro avg 0.90 0.80 0.84 47523 |
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weighted avg 0.96 0.96 0.96 47523 |
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|
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Confusion matrix: |
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[[ 94 0 0 ... 0 0 1] |
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[ 0 8 0 ... 0 0 0] |
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[ 0 0 6 ... 0 0 0] |
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... |
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[ 0 0 0 ... 25 0 0] |
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[ 0 0 0 ... 0 2 0] |
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[ 0 0 0 ... 0 0 588]] |