عنوان المقالة:نهج جديد لتصنيف هجمات MANET بنظام ذكي نيتروسوفيكى يعتمد على الخوارزمية الجينية A Novel Approach for Classifying MANETs Attacks with a Neutrosophic Intelligent System based on Genetic Algorithm
Haitham Elwahsh, Mona Gamal, A. A. Salama, I.M. El-Henawy
Abstract
Recently designing an effective intrusion detection systems (IDS) within Mobile Ad Hoc Networks Security (MANETs) becomes
a requirement because of the amount of indeterminacy and doubt exist in that environment. Neutrosophic system is a discipline
thatmakes a mathematical formulation for the indeterminacy found in such complex situations. Neutrosophic rules compute with
symbols instead of numeric valuesmaking a good base for symbolic reasoning.These symbols should be carefully designed as they
form the propositions base for the neutrosophic rules (NR) in the IDS. Each attack is determined bymembership, nonmembership,
and indeterminacy degrees in neutrosophic system.This research proposes a MANETs attack inference by a hybrid framework of
Self-Organized FeaturesMaps (SOFM) and the genetic algorithms (GA). The hybrid utilizes the unsupervised learning capabilities
of the SOFM to define the MANETs neutrosophic conditional variables. The neutrosophic variables along with the training data
set are fed into the genetic algorithm to find the most fit neutrosophic rule set from a number of initial subattacks according to
the fitness function. This method is designed to detect unknown attacks in MANETs. The simulation and experimental results
are conducted on the KDD-99 network attacks data available in the UCI machine-learning repository for further processing in
knowledge discovery.The experiments cleared the feasibility of the proposed hybrid by an average accuracy of 99.3608 % which is
more accurate than other IDS found in literature.
أ.د. أحمد سلامة | Prof. Ahmed Salama | 9325