IoTDatasetGenerationFramework/examples/classifier/BasicPacketClassifier.java

package classifier;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Iterator;
import java.util.LinkedList;
import java.util.Map.Entry;

import de.tu_darmstadt.tk.SmartHomeNetworkSim.core.Link;
import de.tu_darmstadt.tk.SmartHomeNetworkSim.core.Packet;
import de.tu_darmstadt.tk.SmartHomeNetworkSim.core.PacketSniffer;
import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Unsupervised Classifier Basis, which contains methods for transforming {@link Packet}s into {@link Instance}s.
 *
 * @author Andreas T. Meyer-Berg
 */
public abstract class BasicPacketClassifier implements PacketSniffer {

	/**
	 * True, if instances should be used for training
	 */
	protected boolean training = true;
	
	/**
	 * Attributes which should be taken into account
	 */
	protected ArrayList<Attribute> atts = new ArrayList<Attribute>();
	
	/**
	 * Collected Packets
	 */
	protected Instances dataset;
	
	/**
	 * CollectedPackets
	 */
	protected HashMap<Link, LinkedList<Packet>> collectedPackets = new HashMap<Link, LinkedList<Packet>>();
	
	/**
	 * HashMap for calculating transmission delay
	 */
	protected HashMap<Link, LinkedList<Packet>> lastPackets = new HashMap<Link, LinkedList<Packet>>();
	
	/**
	 * Map for the different Link names
	 */
	protected HashSet<String> link_mappings = new HashSet<String>();

	/**
	 * Map for the difference source device names
	 */
	protected HashSet<String> source_mappings = new HashSet<String>();
	
	/**
	 * Map for the different destination device names
	 */
	protected HashSet<String> destination_mappings = new HashSet<String>();
	
	/**
	 * Map for the protocol names
	 */
	protected HashSet<String> protocol_mappings = new HashSet<String>();
	
	/**
	 * Number of packets which are used to calculate the current transmission speed
	 */
	protected int NUMBER_OF_PACKETS = 200;
	
	/**
	 * Initializes the different maps
	 */
	public BasicPacketClassifier() {
		// Initialize Attribute list
		source_mappings.add("unknown");
		link_mappings.add("unknown");
		destination_mappings.add("unknown");
		protocol_mappings.add("unknown");
	}
	
	@Override
	public void processPackets(HashMap<Link, LinkedList<Packet>> packets) {
		if(training)
			try {
				training(packets);
			} catch (Exception e) {
				e.printStackTrace();
			}
		else
			classify(packets);
	}
	
	/**
	 * Estimates the current Packets per second (depending on the last 100 packets of the link)
	 * @param link Link which should be checked
	 * @param packet Packet which should investigated
	 * @return estimated number of packets per second
	 */
	protected double getEstimatedPacketsPerSecond(Link link, Packet packet) {
		/**
		 * Packets used to calculated the packets per second
		 */
		LinkedList<Packet> list = lastPackets.get(link);
		if(list == null) {
			/**
			 * Add list if not present
			 */
			list = new LinkedList<Packet>();
			lastPackets.put(link, list);
		}
		if(list.isEmpty()) {
			list.addLast(packet);
			// Default 1 packet per second
			return 1.0;
		}
		if(list.size() == NUMBER_OF_PACKETS){
			list.removeFirst();	
		}
		list.addLast(packet);
		/**
		 * elapsed time in milliseconds since last packet
		 */
		long elapsed_time = packet.getTimestamp()-list.getFirst().getTimestamp()/list.size();
		if(elapsed_time<=0)
			return Double.POSITIVE_INFINITY;
		/**
		 * Return number of packets per second
		 */
		return 1000.0/elapsed_time;
		
	}
	
	/**
	 * Returns the instance representation of the given packet and link
	 * @param link link the packet was sent on
	 * @param packet packet which should be transformed
	 * @param dataset distribution the packet is part of
	 * @return instance representation
	 */
	protected Instance packet2Instance(Link link, Packet packet, Instances dataset) {
		/**
		 * Instance for the given Packet
		 */
		DenseInstance instance = new DenseInstance(dataset.numAttributes());
		instance.setDataset(dataset);
		
		// link
		instance.setValue(0, stringToNominal(link_mappings, link.getName()));
		
		// source
		if(packet.getSource()==null) {
			instance.setValue(1, "unknown");
			instance.setValue(2, Double.NEGATIVE_INFINITY);
		}else if(packet.getSource().getOwner()==null){
			instance.setValue(1, "unknown");
			instance.setValue(2, packet.getSource().getPortNumber());
		}else {
			instance.setValue(1, stringToNominal(source_mappings, packet.getSource().getOwner().getName()));
			instance.setValue(2, packet.getSource().getPortNumber());
		}
		
		// Destination
		if(packet.getDestination()==null) {
			instance.setValue(3, "unknown");
			instance.setValue(4, Double.NEGATIVE_INFINITY);
		}else if(packet.getDestination().getOwner()==null){
			instance.setValue(3, "unknown");
			instance.setValue(4, packet.getDestination().getPortNumber());
		}else {
			instance.setValue(3, stringToNominal(destination_mappings, packet.getDestination().getOwner().getName()));
			instance.setValue(4, packet.getDestination().getPortNumber());
		}
		
		// Protocol name
		instance.setValue(5, stringToNominal(protocol_mappings, packet.getProtocolName()));
		
		// Packets per second
		instance.setValue(6, getEstimatedPacketsPerSecond(link, packet));
		
		return instance;
	}
	
	/**
	 * Transforms the String into an Number
	 * @param map
	 * @param s
	 * @return
	 */
	protected String stringToNominal(HashSet<String> map, String s) {
		return map.contains(s)?s:"unknown";
	} 
	
	/**
	 * Train the clusterer by collecting the packets
	 * 
	 * @param packets packets to be learned
	 */
	protected void training(HashMap<Link, LinkedList<Packet>> packets) {
		for(Entry<Link, LinkedList<Packet>> e:packets.entrySet()) {
			Link l = e.getKey();
			LinkedList<Packet> p = collectedPackets.get(l);
			if(p == null)
				collectedPackets.put(l, new LinkedList<Packet>(e.getValue()));
			else
				p.addAll(e.getValue());
		}
	}
	
	/**
	 * Finishes the collection and trains the clusterer on the collected packets
	 * 
	 * @throws Exception
	 */
	protected void finishDataCollection() throws Exception{
		atts.add(new Attribute("Link-Name", new LinkedList<String>(link_mappings)));//TODO:??
		atts.add(new Attribute("Source-Device", new LinkedList<String>(source_mappings)));
		atts.add(new Attribute("Source-Port-number", false));
		atts.add(new Attribute("Destination-Device", new LinkedList<String>(destination_mappings)));
		atts.add(new Attribute("Destination-Port-number", false));
		Attribute pn = new Attribute("Protocol-name", new LinkedList<String>(protocol_mappings));
		//pn.setWeight(10);
		atts.add(pn);
		Attribute pps = new Attribute("Packets-per-second", false);
		//pps.setWeight(20);
		atts.add(pps);
		//atts.add(new Attribute("Anomaly", false));

		/*
		atts = new ArrayList<Attribute>();
		atts.add(new Attribute("LN", new LinkedList<String>(link_mappings)));//TODO:??
		atts.add(new Attribute("SD", new LinkedList<String>(source_mappings)));
		atts.add(new Attribute("SPN", false));
		atts.add(new Attribute("DD", new LinkedList<String>(destination_mappings)));
		atts.add(new Attribute("DPN", false));
		atts.add(new Attribute("PN", new LinkedList<String>(protocol_mappings)));
		atts.add(new Attribute("PPS", false));
		atts.add(new Attribute("A", false));*/
		dataset = new Instances("Packets", atts, 100000);
		//dataset.setClassIndex(7);

		/**
		 * Add Instances to dataset
		 */
		for (Iterator<Entry<Link, LinkedList<Packet>>> it = collectedPackets.entrySet().iterator(); it.hasNext();) {
			Entry<Link, LinkedList<Packet>> entry = it.next();
			/**
			 * Link the packet was captured on
			 */
			Link l = entry.getKey();
			for (Iterator<Packet> itPacket = entry.getValue().iterator(); itPacket.hasNext();) {
				/**
				 * Packets to be added to the dataset
				 */
				Packet packet = (Packet) itPacket.next();
				dataset.add(packet2Instance(l, packet, dataset));
			}
		}
		
		trainModel(dataset);
	}
	
	/**
	 * Try to classify the given packets and detect anomalies
	 * @param packets packets to be classified
	 */
	protected void classify(HashMap<Link, LinkedList<Packet>> packets) {
		int tp = 0;
		int fp = 0;
		int tn = 0;
		int fn = 0;
		long start = Long.MAX_VALUE;
		long end = Long.MIN_VALUE;
		for (Iterator<Entry<Link, LinkedList<Packet>>> it = packets.entrySet().iterator(); it.hasNext();) {
			/**
			 * Link & its packets
			 */
			Entry<Link, LinkedList<Packet>> entry = it.next();
			/**
			 * Link the packets were captured on
			 */
			Link l = entry.getKey();
			for (Iterator<Packet> itPacket = entry.getValue().iterator(); itPacket.hasNext();) {
				/**
				 * Packet which should be checked
				 */
				Packet packet = (Packet) itPacket.next();
				start = Math.min(start, packet.getTimestamp());
				end = Math.max(end, packet.getTimestamp());
				/**
				 * Instance Representation
				 */
				Instance packet_instance = packet2Instance(l, packet, dataset);
				
				if(packet_instance == null)continue;
				try {
					classifyInstance(packet_instance, packet);	
					if(packet.getLabel()==0)
						tn++;
					else
						fn++;
				} catch (Exception e) {
					if(packet.getLabel()==0)
						fp++;
					else
						tp++;
				}
			}	
		}
		int n = tp+tn+fp+fn;
		if(n!=0) {
			System.out.println(getAlgoName()+" Performance: ["+start+"ms, "+end+"ms]");
			System.out.println("n: ");
			System.out.println("TP: "+tp);
			System.out.println("FP: "+fp);
			System.out.println("TN: "+tn);
			System.out.println("FN: "+fn);
			System.out.println("TPR: "+(tp/(tp+fn+0.0)));
			System.out.println("FPR: "+(fp/(fp+tn+0.0)));
			System.out.println("");
		}
	}
	
	/**
	 * Train the model using the given instances
	 * @param instances training set, which should be learned
	 */
	public abstract void trainModel(Instances instances);
	
	/**
	 * classifies the given instance
	 * @param instance instance which should be classified
	 * @param origin original packet, which was transformed into the instance
	 * @throws Exception if anomaly was detected
	 */
	public abstract void classifyInstance(Instance instance, Packet origin) throws Exception;
	
	/**
	 * Returns the timestep, after which the classifier should start classifying instead of training.
	 * @return timestep of the testing begin.
	 */
	public abstract long getClassificationStart();
	
	@Override
	public void setMode(boolean testing) {
		training = !testing;
		if(testing) {
			try {
				finishDataCollection();
			} catch (Exception e) {
				System.out.println("Clustering failed");
				e.printStackTrace();
			}	
		}
	}
	
	@Override
	public boolean getMode() {
		return !training;
	}
	
	/**
	 * Short String representation of the classifier
	 * @return
	 */
	public abstract String getAlgoName();
}