def __normalizeLeafDomain(domain): if isinstance(domain, (Data, AbstractRelationVariableLeaf)): return domain else: return AbstractType.normalize(domain).buildDataRepresentation()
def searchDataInMessage(self, data, message, addTags=True, dataLabels=None): """Search in the specified message any of the given data. These data will be searched as it but also under various format. >>> from netzob.all import * >>> message = RawMessage(b"Reversing protocols with Netzob") >>> sData = [ASCII("protocol")] >>> se = SearchEngine() >>> results = se.searchDataInMessage(sData, message) >>> print(results) 1 occurence(s) found. >>> for result in results: ... print(result) ... print(repr(result.searchTask.properties["data"])) Found ascii-bits(bigEndian) at [(80, 144)] of bitarray('01010010011001010111011001100101011100100111001101101001011011100110011100100000011100000111001001101111011101000110111101100011011011110110110001110011001000000111011101101001011101000110100000100000010011100110010101110100011110100110111101100010') protocol :parameter data: the data to search after. Data must be provided with their netzob type. :type data: a list of :class:`netzob.Model.Types.AbstractType.AbstractType`. :parameter message: the message in which the search will take place :type message: :class:`netzob.Model.Vocabulary.Messages.AbstractMessage` :keyword addTags: if set to True, visualization functions are added to the message to highlights found results. :type addTags: :class:`bool` :keyword dataLabels: an optionnal dict to attach to each data a label to simplify search results identification :type dataLabels: dict :return: a search results detailling where and how occurrences where found. Occurences are also identified in the message through dedicated visualization functions automaticaly added to the message. :rtype: :class:`netzob.Inference.Vocabulary.SearchEngine.SearchResults.SearchResults` """ if data is None or len(data) == 0: raise TypeError("At least one data should be specified.") if message is None: raise TypeError("Message cannot be None") searchTasks = [] for d in data: # normalize the given data normedData = AbstractType.normalize(d) # build search tasks props = dict() props['message'] = message props['data'] = d if dataLabels is not None and d in list(dataLabels.keys()): props['label'] = dataLabels[d] searchTasks.extend(self.__buildSearchTasks(normedData, props)) # fetch the content of the message and convert it to bitarray target = TypeConverter.convert(message.data, Raw, BitArray) # Generate search cases searchCases = itertools.product([target], searchTasks) searchResults = self.__search(searchCases) # If requested, we tag the results in the message using visualization functions # if addTags: # for searchResult in searchResults: # for (startPos, endPos) in searchResult.ranges: # self._logger.info("function from {} to {}".format(startPos, endPos)) # message.visualizationFunctions.append(HighlightFunction(startPos, endPos)) return searchResults