def __init__(self, modhandler, url, password): self.cwd_vector = None self.path = None self.proxy = None self.modhandler = modhandler self.post_data = {} self.current_mode = None self.use_current_path = True self.available_modes = self.params.get_parameter_choices('mode') mode = self.params.get_parameter_value('mode') if mode: self.modes = [mode] else: self.modes = self.available_modes proxy = self.params.get_parameter_value('proxy') if proxy: self.mprint( '[!] Proxies can break weevely requests, if possibile use proxychains' ) self.proxy = {'http': proxy} Module.__init__(self, modhandler, url, password)
def __init__(self, **kwargs): self._name = 'Stacked_'+kwargs['ae_type'] kwargs['dvc'] = torch.device('cpu') Module.__init__(self, **kwargs) self._feature, self._output = self.Sequential(out_number = 2) self.opt() self.Stacked()
def __init__(self, modhandler, url, password): self.cwd_vector = None self.path = None self.proxy = None self.modhandler = modhandler self.post_data = {} self.current_mode = None self.use_current_path = True self.available_modes = self.params.get_parameter_choices('mode') mode = self.params.get_parameter_value('mode') if mode: self.modes = [ mode ] else: self.modes = self.available_modes proxy = self.params.get_parameter_value('proxy') if proxy: self.mprint('[!] Proxies can break weevely requests, if possibile use proxychains') self.proxy = { 'http' : proxy } Module.__init__(self, modhandler, url, password)
def __init__(self, **kwargs): if 'name' in kwargs.keys(): kwargs['_name'] = kwargs['name'] del kwargs['name'] if '_name' not in kwargs.keys(): kwargs['_name'] = 'DBN' # 检测是否设置单元类型 - 用于预训练 if 'v_type' not in kwargs.keys(): kwargs['v_type'] = ['Gaussian'] if 'h_type' not in kwargs.keys(): kwargs['h_type'] = ['Gaussian'] Module.__init__(self, **kwargs) if type(self.h_type) != list: self.h_type = [self.h_type] # 如果未定义 hidden_func 则按单元类型给定 - 用于微调 if hasattr(self, 'hidden_func') == False: self.hidden_func = [] for tp in self.h_type: if tp in ['Gaussian', 'g']: self.hidden_func.append('a') elif tp in ['Binary', 'b']: self.hidden_func.append('s') else: raise Exception("Unknown h_type!") self._feature, self._output = self.Sequential(out_number=2) self.opt() self.Stacked()
def __init__(self, modhandler, url, password): self.rand_post_addr = ''.join([choice('abcdefghijklmnopqrstuvwxyz') for i in xrange(4)]) self.rand_post_port = ''.join([choice('abcdefghijklmnopqrstuvwxyz') for i in xrange(4)]) Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): Module.__init__(self, modhandler, url, password) self.file_content = None self.rand_post_name = ''.join( [choice('abcdefghijklmnopqrstuvwxyz') for i in xrange(4)])
def __init__(self, **kwargs): self._name = 'CNN' Module.__init__(self, **kwargs) Conv_Module.__init__(self, **kwargs) self.layers = self.Convolutional() self.fc = self.Sequential() self.opt()
def __init__( self, modhandler , url, password): self.probe_filename = ''.join(choice(letters) for i in xrange(4)) + '.html' self.found_url = None self.found_dir = None Module.__init__(self, modhandler, url, password)
def __init__( self, modhandler , url, password): self.probe_filename = ''.join(choice(letters) for i in xrange(4)) + '.html' self.found_url = None self.found_dir = None Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.rand_post_addr = ''.join( [choice('abcdefghijklmnopqrstuvwxyz') for i in xrange(4)]) self.rand_post_port = ''.join( [choice('abcdefghijklmnopqrstuvwxyz') for i in xrange(4)]) Module.__init__(self, modhandler, url, password)
def __init__(self, cfg=None, batch_norm=True, use_bias=False, load_pre=None, init_weights=True, **kwargs): if type(cfg) == str: arch = cfgs[cfg] else: arch = cfg default = { 'img_size': [3, 224, 224], 'conv_struct': arch, 'conv_func': 'ReLU(True)', 'res_func': 'ReLU(True)', 'struct': [-1, 1000], 'dropout': 0, 'hidden_func': 'ReLU(True)' } for key in default.keys(): if key not in kwargs.keys(): kwargs[key] = default[key] self.batch_norm = batch_norm self.use_bias = use_bias if type(cfg) == str: self._name = cfg.upper() else: self._name = 'ResNet' Module.__init__(self, **kwargs) Conv_Module.__init__(self, **kwargs) if type(cfg) == str: blocks = self.Convolutional() index = block_id[cfg] self.conv1, self.bn1, self.relu, self.maxpool = \ blocks[0].conv_layers[0], blocks[0].conv_layers[1], blocks[0].act_layer, blocks[0].pool_layer self.layer1 = nn.Sequential(*blocks[index[0]:index[1]]) self.layer2 = nn.Sequential(*blocks[index[1]:index[2]]) self.layer3 = nn.Sequential(*blocks[index[2]:index[3]]) self.layer4 = nn.Sequential(*blocks[index[3]:index[4]]) self.layers = blocks.children() elif type(cfg) == list: self.conv_struct = cfg self.layers = self.Convolutional() self.fc = self.Sequential() self.opt() if init_weights: self._initialize_weights() if load_pre == True or type(load_pre) == str: self.load_pre(cfg, batch_norm, load_pre)
def __init__(self, **kwargs): if 'name' in kwargs.keys(): kwargs['_name'] = kwargs['name'] del kwargs['name'] if '_name' not in kwargs.keys(): kwargs['_name'] = 'Stacked_'+kwargs['ae_type'] if 'decoder_func' not in kwargs.keys(): kwargs['decoder_func'] = 'a' Module.__init__(self, **kwargs) self._feature, self._output = self.Sequential(out_number = 2) self.opt() self.Stacked()
def __init__(self, modhandler, url, password): self.encoder_callable = False self.md5_callable = False self.payload = None self.vector = None self.interpreter = None self.transfer_dir = None self.transfer_url_dir = None self.lastreadfile = '' Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.encoder_callable = False self.md5_callable = False self.payload = None self.vector = None self.interpreter = None self.transfer_dir = None self.transfer_url_dir = None self.lastreadfile = '' Module.__init__(self, modhandler, url, password)
def __init__(self, **kwargs): default = { 'struct2': None, # 解码部分的结构,默认为编码部分反向 'hidden_func2': None, 'n_category': None, 'dropout': 0.0, 'exec_dropout': [False, True], 'lr': 1e-3 } for key in default.keys(): if key not in kwargs: kwargs[key] = default[key] kwargs['dvc'] = torch.device('cpu') self._name = 'MMDGM_VAE' Module.__init__(self, **kwargs) # q(z|x) self.qx = self.Sequential(struct=self.struct[:2], hidden_func=self.hidden_func) self.qy = self.Sequential(struct=[self.n_category, self.struct[1]], hidden_func=self.hidden_func) self.Q = self.Sequential(struct=self.struct[1:-1], hidden_func=self.hidden_func[1:]) self.z_mu = self.Sequential(struct=self.struct[-2:], hidden_func='a') self.z_logvar = self.Sequential(struct=self.struct[-2:], hidden_func='a') # p(x|z) if self.struct2 is None: self.struct2 = self.struct.copy() self.struct2.reverse() if self.hidden_func2 is None: if type(self.hidden_func) == str: self.hidden_func = [self.hidden_func] self.hidden_func2 = self.hidden_func.copy() self.hidden_func2.reverse() self.pz = self.Sequential(struct=self.struct2[:2], hidden_func=self.hidden_func2) self.py = self.Sequential(struct=[self.n_category, self.struct2[1]], hidden_func=self.hidden_func2) self.P = self.Sequential(struct=self.struct2[1:], hidden_func=self.hidden_func2[1:]) self.opt()
def __init__(self, module_list, **kwargs): ''' _loss: 附加损失 loss: 全部损失 ''' name = '' for i in range(len(module_list)): if hasattr(module_list[i], 'name'): name += module_list[i].name if i < len(module_list) - 1: name += '-' self.name = name Module.__init__(self, **kwargs) self.modules = nn.Sequential(*module_list) self.module_list = module_list self.opt(False)
def __init__(self, cfg = None, batch_norm = False, use_bias = True, load_pre = None, init_weights=True, **kwargs): if type(cfg) == str: arch = cfgs[cfg] else: arch = cfg default = {'img_size': [3, 224, 224], 'conv_struct': arch, 'conv_func': 'ReLU(True)', 'struct': [-1, 4096, 4096, 1000], 'dropout': [0, 0.5, 0.5], 'hidden_func': 'ReLU(True)' } for key in default.keys(): if key not in kwargs.keys(): kwargs[key] = default[key] self.batch_norm = batch_norm self.use_bias = use_bias if type(cfg) == str: self._name = cfg.upper() elif type(cfg) == list: self._name = 'VGG' self.conv_struct = cfg Module.__init__(self,**kwargs) Conv_Module.__init__(self,**kwargs) self.features = self.Convolutional('layers', auto_name = False) self.classifier = self.Sequential() self.opt() if init_weights: self._initialize_weights() if load_pre == True or type(load_pre) == str: self.load_pre(cfg, batch_norm, load_pre)
def __init__(self, modhandler, url, password): self.structure = {} Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.chunksize = 5000 self.substitutive_wl = [] Module.__init__(self, modhandler, url, password)
def __init__( self, modhandler , url, password): self.chunksize = 5000 self.substitutive_wl = [] Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.ifaces = {} Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): Module.__init__(self, modhandler, url, password) self.file_content = None self.rand_post_name = ''.join([choice('abcdefghijklmnopqrstuvwxyz') for i in xrange(4)])
def __init__(self, modhandler, url, password): Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): Module.__init__(self, modhandler, url, password) self.usersinfo = {}
def __init__(self, modhandler, url, password): self.pathdict = {} Module.__init__(self, modhandler, url, password)
def __init__( self, modhandler , url, password): Module.__init__(self, modhandler, url, password) self.usersfiles = {}
def __init__(self, modhandler, url, password): self.list = [] Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.reqlist = RequestList(modhandler) Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.list = [] Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.last_vector = None self.done = False Module.__init__(self, modhandler, url, password)
def __init__(self, modhandler, url, password): self.pathdict = {} Module.__init__(self, modhandler, url, password)
def __init__( self, modhandler , url, password): self.structure = {} Module.__init__(self, modhandler, url, password)
def __init__( self, modhandler , url, password): self.last_vector = None self.done = False Module.__init__(self, modhandler, url, password)
P(arg='cmd', help='Shell command', required=True, pos=0), P(arg='stderr', help='Print standard error', default=True, type=bool) ) def __init__( self, modhandler , url, password): self.payload = None self.cwd_vector = None try: modhandler.load('shell.php') except ModuleException, e: raise else: Module.__init__(self, modhandler, url, password) def __execute_probe(self, vector): try: rand = random.randint( 11111, 99999 ) response = self.run_module( "echo %d" % rand, True, vector.payloads[0] ) if response == str(rand): self.params.set_and_check_parameters({'vector' : vector.name}) return True except: #pass
def __init__(self, **kwargs): self._name = 'DNN' Module.__init__(self, **kwargs) self._feature, self._output = self.Sequential(out_number = 2) self.opt()