GANIDS final project
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Ngen10000_Ind10000_resultBest500.txt
README.md
acega.py
acega.pyc
acega.txt
acegaconfig.py
acegaconfig.pyc
acegaconfig.txt
acegafunc.py
acegafunc.pyc
acegafunc.txt
acegatester.py
acegatester.txt
bsm.list
control_settings.txt
dict_w6_thu.list
evalFuncs.py
fastmax.py
ga_pesudo_code
ga_pseudo_mutationOfEliteGenes
gaconfig.py
gaconfig.pyc
gafunc.py
gafunc.pyc
ganids.py
ganids.pyc
gaproto.py
gaproto.pyc
gasnids.py
gasnids_tester.py
gasnids_tester.pyc
idsconfig.py
idsconfig.pyc
idsfunc.py
idsfunc.pyc
idsproto.py
idsproto.pyc
ipsw_w2_tue.list
ipsw_w34_w.list
ipsw_w3_wed.list
ipsw_w4_wed.list
mixed.list
mixed_all.list
mixed_pod_test.list
names
nept_w1_wed.list
nept_w3_thu.list
nept_w4_tue.list
nept_w5_thu.list
pscan.list
psw_w6_thu.list
reso
reso_test.rcd
result_pod.rcd
result_pod2.rcd
rules.rcd
rulesDump.rcd
rules_ipsweep.rcd
rules_neptune.rcd
rules_pod.rcd
rules_podfrTest.rcd
rules_portsweep.rcd
rules_teardrop.rcd
tcpdump.list
teard_w4_tue.list
teard_w5_mon.list
test_allpod.list
test_pod207.list
test_w1_fri_truth.list
test_w1_mon_truth.list
test_w1_thu_truth.list
test_w1_tue_truth.list
test_w1_wed_truth.list
test_w1mon.list
testerMod.py
testerMod.pyc
thirtytimes
w1_alltruth.list
w1_fri.list
w1_mon.list
w1_thu.list
w1_tue.list
w1_wed.list
w1_wednesday.list
w2_alltruth.list
w2_mon.list
w3_wed.list
w4_mon.list
w4_tue.list
w4_wed.list
w5_thu.list
w5_tue.list
w6_thu.list
w6_tue.list
w7_tcpdump.list
w7_tue.list
wm_alltruth.list

README.md

Project Title: "Elitism Enhancements for Genetic Algorithm Based Network Intrusion Detection System" (AceGA)

Please kindly obtain the published academic paper here: http://www.aicit.org/jcit/global/paper_detail.html?jname=JCIT&q=4159 or directly here: http://www.aicit.org/JCIT/ppl/JCIT4159PPL.pdf

Contact Info: tanapuch.wanwarang@dimensiondata.com / nik.muic@gmail.com

To run this program, you need to install deap (it's a python library). optional: install pypy, it's a flavor of python intepreter (Much faster and better performance).

I. Motivation

Network Security and Artificial intelligence are the two sub-fields in Computer Science that I fascinate and have much interest in. Thus, I would like to make a contribution to them by doing this project, as well as to learn and acquire knowledge as much as possible along the way.

By integrating Genetic Algorithms into Network Intrusion Detection Systems, my purpose could be realized.

II. Problem

Signature-based Network Intrusion Detection Systems (i.e. Profile-based, Rule-based) is able to detect and alert network administrators about the attack connections. But this NIDS approach suffers high false-negative result rates, because a signature-based Intrusion Detection System is only as good as its records of rules in its database. This is due to its inability to learn and identify new types of attacks.

III. Solution

A field in Artificial Intelligence: Evolutionary Algorithms, specifically Genetic Algorithms (GA), has an excellent approach to solve this problem. There are two general steps to apply GA to an NIDS, training stage and testing stage:

Training Stage: Firstly, GA can learn to make new rules (attack patterns) from existing identified attack records in a dataset.

Testing Stage: Secondly, the newly made rules can then be stored in an IDS database in order to strengthen the security level.