Black Hole attack Detection using fuzzy based IDS

In the past few years, an evolution in the wireless communication has been emerged, along with the evolution a new type with large potential application of wireless network appears, which is the Mobile Ad-Hoc Network (MANET). Black hole attack consider one of the most affected kind on MANET. Therefore, the use of intrusion detection system (IDS) has a major importance in the MANET protection. In this paper, an optimization of a fuzzy based intrusion detection system is proposed which automate the process of producing a fuzzy system by using an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the initialization of the FIS and then optimize this initialized system by using Genetic Algorithm (GA). In addition, a normal estimated fuzzy based IDS is introduces to see the effect of the optimization on the system. From this study, it is proven that the optimized proposed IDS perform better that the normal estimated systems

Analysis of Hybrid Soft Computing Techniques for Intrusion Detection on Network

Intrusion detection is an action towards security of a network when a system or network is being used inappropriately or without authorization. The use of Soft Computing Approaches in intrusion detection is an Appealing co ncept for two reasons: firstly, the Soft Computing Approaches achieve tractability, robustness, low solution cost, and better report with reality. Secondly, current techniques used in network security from intrusion are not able to cope with the dynamic and increasingly complex nature of network and their security. It is hoped that Soft Computing inspired approaches in this area will be able to meet this challenge. Here we analyze the approaches including the examination of efforts in hybrid system of SC su ch as neuro - fuzzy, fuzzy - genetic, neuro - genetic, and neuro - fuzzy - genetic used the development of the systems and outcome their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestio ns for future research

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

Fuzzy Logic based Intrusion Detection System against Black Hole Attack in Mobile Ad Hoc Networks

A Mobile Ad hoc NETwork (MANET) is a group of mobile nodes that rely on wireless network interfaces, without the use of fixed infrastructure or centralized administration. In this respect, these networks are very susceptible to numerous attacks. One of these attacks is the black hole attack and it is considered as one of the most affected kind on MANET. Consequently, the use of an Intrusion Detection System (IDS) has a major importance in the MANET protection. In this paper, a new scheme has been proposed by using an Adaptive Neuro Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) for mobile ad hoc networks to detect the black hole attack of the current activities. Evaluations using extracted database from a simulated network using the Network Simulator NS2 demonstrate the effectiveness of our approach, in comparison to an optimized IDS based ANFIS-GA

Automatic generation of fuzzy classification rules using granulation-based adaptive clustering

A central problem of fuzzy modelling is the generation of fuzzy rules that fit the data to the highest possible extent. In this study, we present a method for automatic generation of fuzzy rules from data. The main advantage of the proposed method is its ability to perform data clustering without the requirement of predefining any parameters including number of clusters. The proposed method creates data clusters at different levels of granulation and selects the best clustering results based on some measures. The proposed method involves merging clusters into new clusters that have a coarser granulation. To evaluate performance of the proposed method, three different datasets are used to compare performance of the proposed method to other classifiers: SVM classifier, FCM fuzzy classifier, subtractive clustering fuzzy classifier. Results show that the proposed method has better classification results than other classifiers for all the datasets used

Intrusion Detection System using Fuzzy Logic

Intrusion detection plays an important role in today’s computer and communication technology. As such it is very important to design time efficient Intrusion Detection System (IDS) low in both, False Positive Rate (FPR) and False Negative Rate (FNR), but high in attack detection precision. To achieve that, this paper proposes IDS model based on Fuzzy Logic. Proposed model consists of three parts, Input Reduction System (IRS), which uses Principal Component Analysis to reduce the dimensions of the system from 41 to 10, Classification System, which uses Fuzzy C Means to create data clusters based on training data and Pattern Recognition System based on Nearest Neighborhood method, which classifies new-coming data records to their respective clusters. Based on different attack types, the system performance in classification process is different and the best performance is achieved for PROBE attack, with 99.3% success rate, and the best performance in pattern recognition is achieved for U2R with 58.8% of success rate