From c4eb649d28ef05a9a586832f6ce0205777dd7c1c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Simon=20Kl=C3=BCttermann?= Date: Sun, 19 Sep 2021 14:38:23 +0200 Subject: [PATCH] initial commit --- __pycache__/stroo.cpython-39.pyc | Bin 0 -> 3146 bytes main.py | 14 +++++ stroo.py | 103 +++++++++++++++++++++++++++++++ useragents.npz | Bin 0 -> 167109 bytes 4 files changed, 117 insertions(+) create mode 100644 __pycache__/stroo.cpython-39.pyc create mode 100644 main.py create mode 100644 stroo.py create mode 100644 useragents.npz diff --git a/__pycache__/stroo.cpython-39.pyc b/__pycache__/stroo.cpython-39.pyc new file mode 100644 index 0000000000000000000000000000000000000000..8d9d41b11752513a73a40bb49573ba3099de9828 GIT binary patch literal 3146 zcmZuzNpB=c6^@8pDqXhA_Zg6rR`zP=(IOmlE@|hbF(tIznyz~;KhAd&~FUi+FW)$k0CB z`aJ%R$JoE=<7Tt)@fmufFi0kO&I%SXPPE8*AwtX0R&Iw5cy{gBub|0DDA9xnp}{)iJ(1;MzoDi6OE7IYxXtY6Ov1D>S?C= z3x2|1usiI8KSIZq)=t8)?y}JVF8Xcs<^v29*0QG?b8Kw!d%?cV{vzIEe-(%J6ZVR! z0hYWHo!Gk9@eBknJAP=m|MR!^t0LX5n@UZaZMofOwVlqYiioP(s*k(jgB*$_RZ%}& z!8!j$XS4UqiHeJ6>p@;6ao#)xak&TA;f5m zeYhfxUb7Y1iNyaME&2rp%}&@{Xf9zR&a`Nam7d<2TdjcYtk!B_$1`!le~Wd8o7!$c z8yn6>BxF`j{Qlxs+=vx8ozQ289(W0P`?4*80JM9p{sv;g=9XqHbei+GbUT>a&vDTB$Gh02D zZtJ$*1Mpkxe9*ctVa>x2w0FMLS})07gPqw%Qk6%kGA7fNf0QP=QfdS8z6D3p?g@G` zHrfD=-ki$?1Ou)JzP;5*?>FqH*1-D?@oD$$L*pK;;=Sr!05fM@(1F3Qa~g7z+N8}_ zo>S}ec?(15WTi}>cWzwQX(>BV)}0e86(0vn#;>7GUhCm`2OC0hc-ZktDB>g(lSyb{ z_Z!l7!)y7zg)Pnd7#O$VZSpsH+~b0e_?X|qm--%N6Iyb!k*uGgH+M0lEQGB>WB}YJ zq;<+dTiVjWzhlxA3tj2KCri+m;&S#$lNEKI`bDZHsh{L=)A)Op^7WoyR5H!|xReW& zCk}47Y(&%5CY&g9AYZVEwFsk#BXn3K05ld^k{|%2o73*&*lPCx?jQP#X2SozPsaDF zy~R~mvn6KIk|xqP+3#nsCX4$JJ2tcMaaN|%m$8mtlYb|$JICDJIp!+kp^g^_R^F(O zA-VcK4JNPrfG87<*CFXeQC4O;ipanXfsVN@Lek~4QJ>o`7rd2k$VkRO_drlow(Nj9 z{Bvf_VX6b`9MOmfqin+toI5Byw-LcDl?Q9KJxR+{#X9xNN)>UQ>0`f1DwQsLw4eI5 zs*W)E5vgaA@YsjJv)QyS% z1p>%|iGe9W^$fHyU)CH|?VNCBQ!b!F-XjfFKpZ#+E@cMF3TLPZs27)9B@ES?e4OTc(!}R`e>>RJkly&V*d0r;i+;IjFVE=OgS`YatIL za`m`%Naog+>t}dq1h;09320ye(Ost19uh;UzpB(T6WrIZxZC?Y@GtRU5&)1hZKyoo zEdvwuKk-p-r>eXHmx&Zb`F7`mYHVn$G@m{WL^zBSogKwGtIE)uHfgk*#mxo5D70f4 z7ok-&Y3J`3WvL~PWGcFo28+f)>X3KgkxYQ zgOE5rYHCC@-ZfXLCs;QX-h^)FJVWR=q4WE)p5jf>?8h~Z$>ZZxf%_;e8+@9~hR;7z zuI>i(xXN-onD#QQK7=@(4f8d91+ytx_7EhbAs{}%Uw=D2mpUse>;8@MF+M|47dK#P z9$L%BErj(nH}LbZkO?FOdjojgq@Nh!dlPjFzyG_=Sg7A%4JtPU(!2lGOvMeycst_x zi?@QNY_1w*VT+=A-Z|y8sE>_K;ac-7Ph(YX6)Cbp(p(3*x=$NOnQKFu05US%(B>-W eI`2UrxeqCAHdN|Sd4(s&+_Q$(a4=jSefJv-RKbM+ literal 0 HcmV?d00001 diff --git a/main.py b/main.py new file mode 100644 index 0000000..8d35c0d --- /dev/null +++ b/main.py @@ -0,0 +1,14 @@ +import numpy as np + +from stroo import * + + +f=np.load("useragents.npz") + +model=train_model(f["train"],n=2) + +print(model.eval(f["testx"],f["testy"])) + + + + diff --git a/stroo.py b/stroo.py new file mode 100644 index 0000000..835bca0 --- /dev/null +++ b/stroo.py @@ -0,0 +1,103 @@ + +from tqdm import tqdm +from sortedcontainers import SortedList as slist + +import numpy as np + +from sklearn.metrics import roc_curve,auc +import keras + + +def ngrams(q,n=3): + return [q[i:i+n] for i in range(len(q)-n)] + +def multigramm(q,n=3): + ret=set() + print("listing ngrams") + for zw in tqdm(q): + ret=ret|set(ngrams(zw,n=n)) + return list(ret) + +def ngramtrafo(q,gram,n=3): + ret=[] + gram=slist(gram)#speed up. Trust me, you want this + print("converting to vector") + for qq in tqdm(q): + g=ngrams(qq,n=n) + ac=[0 for i in range(len(gram))] + for gg in g: + try: + dex=gram.index(gg) + except:continue + if dex>0:ac[dex]=1 + ret.append(ac) + return np.array(ret) + + + +class stroo(): + """simple merge class for tf model and ngrams""" + def __init__(s,model,grams,n,m): + """model:tf object + grams: list of ngrams + n: len of each ngram + m: mean of trained data for tf model""" + + s.model=model + s.grams=grams + s.n=n + s.m=m + + def predict(s,data): + """generate normality scores for the provided data""" + ng=ngramtrafo(data,s.grams, n=s.n) + p=s.model.predict(ng) + p=(p-s.m)**2 + return p + + + def eval(s,x,y): + """evaluates a model by a list of strings x that are normal(y=0) or abnormal (y=1)""" + p=s.predict(x) + fpr,tpr,_=roc_curve(y,p) + return auc(fpr,tpr) + + + +def train_model(data,n=3): + """trains an oneoff network for a list of (normal) strings data by using (n)grams, returns a model""" + ngrams=multigramm(data,n=n) + data=ngramtrafo(data,ngrams,n=n) + + + #tensorflow stuff (not at all optimised) + inp=keras.Input(data.shape[1:]) + q=inp + q=keras.layers.Dense(10,activation="relu",use_bias=False)(q) + q=keras.layers.Dense(4,activation="relu",use_bias=False)(q) + q=keras.layers.Dense(1,activation="relu",use_bias=False)(q) + + model=keras.models.Model(inp,q) + + model.compile("adam","mse") + model.fit(data,np.ones(len(data),dtype="float"), + batch_size=100, + epochs=50, + validation_split=0.1) + + pm=np.mean(model.predict(data)) + + return stroo(model,ngrams,n=n,m=pm) + + + + + + + + + + + + + diff --git a/useragents.npz b/useragents.npz new file mode 100644 index 0000000000000000000000000000000000000000..65470c9bf224745d1732087fc6475b5a07734e05 GIT binary patch literal 167109 zcmY&<2T)U6)UI9&C;}=fN)^3`AVoxa0EMewrHQCOXhJ|r=%EOtT$LgM!K+fFMMOZP zhF%k;g_?k90t5&>V1NLD^n}O#-@G^T?@1;z`|LR32kY-Rw^!|Ov|EF_r)V}`){MXiCYn78d>1+8| 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