Network Anomaly Detection application customized for CIRCE framework (available here: https://github.com/ANRGUSC/CIRCE). CIRCE accepts pipelined computations described in a form of a Directed Acyclic Graph (DAG). It requires that each task in a graph is written as a Python function in a separate file.
Network Anomaly Detection: Task Graph
The application task graph, shown below, is intended for dispersed computing. It is inspired from Hashdoop [1, 2], where a MapReduce framework is used for anomaly detection. We have modified the codes from  to suit our purpose.
Generating the input files
Convert the pcap file to a text file using Ipsumdump as follows:
ipsumdump -tsSdDlpF -r botnet-capture-20110810-neris.pcap > botnet_summary.ipsum
Single input file, 1botnet.ipsum, is given in a repository.
local_pro.py: Process the Ipsum file locally and split the traffic into multiple independent streams based on the hash value of the IP adresses.
aggregate<SPLIT_ID>.py: SPLIT_ID (0, 1 or 2, in our case) uniquely idenfifies the split. This script aggregates traffic for a particular traffic split from different monitoring nodes.
simple_detector<SPLIT_ID>.py: A simple threshold based anomaly detector for the particular split.
astute_detector<SPLIT_ID>.py: An implementation of ASTUTE anomaly detector  from the repository .
fusion_center<SPLIT_ID>.py: Combine the detected anomalies by different detectors for the particular split.
global_fusion.py: Collect all the anomalies from different splits and combine the detected anomalies.
This code is meant to be executed with CIRCE. You can also test the application on your machine. First change the input/output file paths in each Python script and run the following command for each task, in the order given by DAG:
 Romain Fontugne, Johan Mazel, and Kensuke Fukuda. "Hashdoop: A mapreduce framework for network anomaly detection." Computer Communications Workshops (INFOCOM WORKSHOPS), IEEE Conference on. IEEE, 2014.
 Fernando Silveira, Christophe Diot, Nina Taft, and Ramesh Govindan. "ASTUTE: Detecting a different class of traffic anomalies." ACM SIGCOMM Computer Communication Review 40.4 (2010): 267-278.
This material is based upon work supported by Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001117C0053. Any views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.