ID2T-toolkit-BotnetTraffic/code/ID2TLib/CommunicationProcessor.py

from lea import Lea
from Attack.MembersMgmtCommAttack import MessageType
from Attack.MembersMgmtCommAttack import Message

# needed because of machine inprecision. E.g A time difference of 0.1s is stored as >0.1s
EPS_TOLERANCE = 1e-13  # works for a difference of 0.1, no less

######### TODO: WIKI ADD VALUE RANGES ##########

class CommunicationProcessor():
    """
    Class to process parsed input CSV/XML data and retrieve a mapping or other information.
    """

    def __init__(self, packets:list, mtypes:dict, nat:bool):
        self.packets = packets
        self.mtypes = mtypes
        self.nat = nat

    def set_mapping(self, packets: list, mapped_ids: dict):
        """
        Set the selected mapping for this communication processor.

        :param packets: all packets contained in the mapped time frame
        :param mapped_ids: the chosen IDs
        """
        self.packets = packets
        self.local_init_ids = set(mapped_ids.keys())

    def find_interval_most_comm(self, number_ids: int, max_int_time: float):
        """
        Finds a time interval of the given seconds where the given number of IDs commuicate the most.
        If NAT is active, the most communication is restricted to the most communication by the given number of initiating IDs.
        If NAT is inactive, the intervall the most overall communication, that has at least the given number of initiating IDs in it, is chosen.
        
        :param number_ids: The number of IDs that are to be considered
        :param max_int_time: A short description of the attack.
        :return: A triple consisting of the IDs, as well as start and end idx with respect to the given packets. 
        """
        packets = self.packets
        mtypes = self.mtypes

        def get_nez_comm_counts(comm_counts: dict):
            """
            Filters out all msg_counts that have 0 as value
            """
            nez_comm_counts = dict()
            for id_ in comm_counts.keys():
                count = comm_counts[id_]
                if count > 0:
                    nez_comm_counts[id_] = count
            return nez_comm_counts

        def greater_than(a: float, b: float):
            """
            A greater than operator desgined to handle slight machine inprecision up to EPS_TOLERANCE.
            :return: True if a > b, otherwise False
            """
            return b - a < -EPS_TOLERANCE

        def change_comm_counts(comm_counts: dict, idx: int, add=True):
            """
            Changes the communication count, stored in comm_counts, of the initiating ID with respect to the
            packet specified by the given index. If add is True, 1 is added to the value, otherwise 1 is subtracted.
            """
            change = 1 if add else -1
            mtype = mtypes[int(packets[idx]["Type"])]
            id_src, id_dst = packets[idx]["Src"], packets[idx]["Dst"]
            if mtype in {MessageType.SALITY_HELLO, MessageType.SALITY_NL_REQUEST}:
                if id_src in comm_counts:
                    comm_counts[id_src] += change
                elif change > 0:
                    comm_counts[id_src] = 1
            elif mtype in {MessageType.SALITY_HELLO_REPLY, MessageType.SALITY_NL_REPLY}:
                if id_dst in comm_counts:
                    comm_counts[id_dst] += change
                elif change > 0:
                    comm_counts[id_dst] = 1

        def get_comm_count_first_ids(comm_counts: list):
            """
            Finds the IDs that communicate among themselves the most with respect to the given message counts.
            :param msg_counts: a sorted list of message counts where each entry is a tuple of key and value
            :return: The picked IDs and their total message count as a tuple
            """
            # if order of most messages is important, use an additional list
            picked_ids = {}
            total_comm_count = 0

            # iterate over every message count
            for i, comm in enumerate(comm_counts):
                count_picked_ids = len(picked_ids)

                # if enough IDs have been found, stop
                if count_picked_ids >= number_ids:
                    break

                picked_ids[comm[0]] = comm[1]
                total_comm_count += comm[1]

            return picked_ids, total_comm_count


        # first find all possible intervals that contain enough IDs that initiate communication
        idx_low, idx_high = 0, 0
        comm_counts = {}
        possible_intervals = []
        general_comm_sum, cur_highest_sum = 0, 0

        # Iterate over all packets from start to finish and process the info of each packet
        # If time of packet within time interval, update the message count for this communication
        # If time of packet exceeds time interval, substract from the message count for this communication
        # Similar to a Sliding Window approach
        while True:
            if idx_high < len(packets):
                cur_int_time = float(packets[idx_high]["Time"]) - float(packets[idx_low]["Time"])
     
            # if current interval time exceeds time interval, save the message counts if appropriate, or stop if no more packets
            if greater_than(cur_int_time, max_int_time) or idx_high >= len(packets):
                # get all message counts for communications that took place in the current intervall
                nez_comm_counts = get_nez_comm_counts(comm_counts)
                # if we have enough IDs as specified by the caller, mark as possible interval
                if len(nez_comm_counts) >= number_ids:
                    if self.nat:
                        possible_intervals.append((nez_comm_counts, idx_low, idx_high-1))
                    elif general_comm_sum >= cur_highest_sum:
                        cur_highest_sum = general_comm_sum
                        possible_intervals.append({"IDs": nez_comm_counts, "CommSum": general_comm_sum, "Start": idx_low, "End": idx_high-1})
                        general_comm_sum = 0

                if idx_high >= len(packets):
                    break

            # let idx_low "catch up" so that the current interval time fits into the interval time specified by the caller
            while greater_than(cur_int_time, max_int_time):
                change_comm_counts(comm_counts, idx_low, add=False)
                idx_low += 1
                cur_int_time = float(packets[idx_high]["Time"]) - float(packets[idx_low]["Time"])

            # consume the new packet at idx_high and process its information
            change_comm_counts(comm_counts, idx_high)
            idx_high += 1
            general_comm_sum += 1

        if self.nat:
            # now find the interval in which as many IDs as specified communicate the most in the given time interval
            summed_intervals = []
            sum_intervals_idxs = []
            cur_highest_sum = 0

            # for every interval compute the sum of id_counts of the first most communicative IDs and eventually find
            # the interval(s) with most communication and its IDs
            # on the side also store the communication count of the individual IDs
            for j, interval in enumerate(possible_intervals):
                comm_counts = interval[0].items()
                sorted_comm_counts = sorted(comm_counts, key=lambda x: x[1], reverse=True)
                picked_ids, comm_sum = get_comm_count_first_ids(sorted_comm_counts)

                if comm_sum == cur_highest_sum:
                    summed_intervals.append({"IDs": picked_ids, "CommSum": comm_sum, "Start": interval[1], "End": interval[2]})
                elif comm_sum > cur_highest_sum:
                    summed_intervals = []
                    summed_intervals.append({"IDs": picked_ids, "CommSum": comm_sum, "Start": interval[1], "End": interval[2]})
                    cur_highest_sum = comm_sum
            return summed_intervals
        else:
            return possible_intervals


    def det_id_roles_and_msgs(self):
        """
        Determine the role of every mapped ID. The role can be initiator, responder or both.
        On the side also connect corresponding messages together to quickly find out
        which reply belongs to which request and vice versa.

        :return: a triple as (initiator IDs, responder IDs, messages)
        """

        mtypes = self.mtypes
        # setup initial variables and their values
        respnd_ids = set()
        # msgs --> the filtered messages, msg_id --> an increasing ID to give every message an artificial primary key
        msgs, msg_id = [], 0
        # keep track of previous request to find connections
        prev_reqs = {}
        local_init_ids = self.local_init_ids
        external_init_ids = set()

        # process every packet individually 
        for packet in self.packets:
            id_src, id_dst, msg_type, time = packet["Src"], packet["Dst"], int(packet["Type"]), float(packet["Time"])
            # if if either one of the IDs is not mapped, continue
            if (id_src not in local_init_ids) and (id_dst not in local_init_ids):
                continue

            # convert message type number to enum type
            msg_type = mtypes[msg_type]

            # process a request
            if msg_type in {MessageType.SALITY_HELLO, MessageType.SALITY_NL_REQUEST}:
                if not self.nat and id_dst in local_init_ids and id_src not in local_init_ids:
                    external_init_ids.add(id_src)
                elif id_src not in local_init_ids:
                    continue
                else:
                    # process ID's role
                    respnd_ids.add(id_dst)
                # convert the abstract message into a message object to handle it better
                msg_str = "{0}-{1}".format(id_src, id_dst)
                msg = Message(msg_id, id_src, id_dst, msg_type, time)
                msgs.append(msg)
                prev_reqs[msg_str] = msg_id
                msg_id += 1

            # process a reply
            elif msg_type in {MessageType.SALITY_HELLO_REPLY, MessageType.SALITY_NL_REPLY}:
                if not self.nat and id_src in local_init_ids and id_dst not in local_init_ids:
                    # process ID's role
                    external_init_ids.add(id_dst)
                elif id_dst not in local_init_ids:
                    continue
                else: 
                    # process ID's role
                    respnd_ids.add(id_src)
                # convert the abstract message into a message object to handle it better
                msg_str = "{0}-{1}".format(id_dst, id_src)
                # find the request message ID for this response and set its reference index
                refer_idx = prev_reqs[msg_str]
                msgs[refer_idx].refer_msg_id = msg_id
                msg = Message(msg_id, id_src, id_dst, msg_type, time, refer_idx)
                msgs.append(msg)
                # remove the request to this response from storage
                del(prev_reqs[msg_str])
                msg_id += 1

        # store the retrieved information in this object for later use
        self.respnd_ids = sorted(respnd_ids)
        self.external_init_ids = sorted(external_init_ids)
        self.messages = msgs

        # return the retrieved information
        return self.local_init_ids, self.external_init_ids, self.respnd_ids, self.messages

    def det_ext_and_local_ids(self, prob_rspnd_local: int):
        """
        Map the given IDs to a locality (i.e. local or external} considering the given probabilities.

        :param comm_type: the type of communication (i.e. local, external or mixed)
        :param prob_rspnd_local: the probabilty that a responder is local
        """
        external_ids = set()
        local_ids = self.local_init_ids.copy()
        
        # set up probabilistic chooser
        rspnd_locality = Lea.fromValFreqsDict({"local": prob_rspnd_local*100, "external": (1-prob_rspnd_local)*100})

        for id_ in self.external_init_ids:
            external_ids.add(id_)

        # determine responder localities
        for id_ in self.respnd_ids:
            if id_ in local_ids or id_ in external_ids:
                continue 
            
            pos = rspnd_locality.random() 
            if pos == "local":
                local_ids.add(id_)
            elif pos == "external":
                external_ids.add(id_)

        self.local_ids, self.external_ids = local_ids, external_ids
        return self.local_ids, self.external_ids