scholarly journals Dendritic Cell Algorithm Enhancement Using Fuzzy Inference System for Network Intrusion Detection

2019 ◽  
Author(s):  
Noe Elisa ◽  
Longzhi Yang ◽  
Xin Fu ◽  
Nitin Naik
Keyword(s):  
Dendritic Cell ◽  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Network Intrusion Detection ◽  
Inference System ◽  
Network Intrusion ◽  
Dendritic Cell Algorithm

scholarly journals Fuzzy Controlled Network Intrusion Detection System (FC-NIDS)

2019 ◽  
Vol 9 (2) ◽  
pp. 228-235
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Fuzzy Inference System ◽  
Intrusion Detection System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Features Selection ◽  
Inference System ◽  
Testing Procedures ◽  
Network Intrusion

Intrusion Detection System (IDS) is the nearly all imperative constituent of computer network security. IDSs are designed to comprehend intrusion attempts in incoming network traffic shrewdly. It deals with big volume of data containing immaterial and outmoded features, which lead to delay in training as well as testing procedures. Therefore, to minimize the false alarm and computation complexity, the features selection technique for intrusion detection has been implemented. In this paper PCA (Principal Component Analysis) and Fuzzy Inference System (FIS) have been used on kdd99 dataset to develop FC-NIDS model. PCA is used to select the attacked features to minimize the computational work, while FIS is used to develop a fuzzy inference system for accuracy in prophecy using MATLAB. The results of the experiment are tested on UCI data sets as a standard bench-mark. It has been found efficient for true prediction of intrusion as well as to reduce the false alarm rate. The proposed fuzzy logic controller IDS (FC-NIDS), is passable to covenant with signature and anomaly based attacks to get enhanced intrusion detection, decreases false alarm and to optimize complexity.

scholarly journals Computation of Risk Severity of the Malicious Node using Adaptive Neuro Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 8 (1) ◽  
pp. 9-14
Author(s):  
R. Dharmarajan ◽  
V. Thiagarasu
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Malicious Nodes ◽  
Inference System ◽  
Detection Systems ◽  
Network Intrusion ◽  
Neuro Fuzzy ◽  
Network Intrusion Detection Systems

The Intrusion Detection System (IDS) can be employed broadly for safety network. Intrusion Detection Systems (IDSs) are commonly positioned alongside with other protecting safety mechanisms, such as authentication and access control, as a subsequent line of defence that guards data structures. In this paper, Adaptive Neuro Fuzzy Inference System has utilized to predict the risk severity of the malicious nodes found the previous classification phase.

scholarly journals A New Intrusion Detection System Using the Improved Dendritic Cell Algorithm

2020 ◽  
Author(s):  
Ehsan Farzadnia ◽  
Hossein Shirazi ◽  
Alireza Nowroozi
Keyword(s):  
Dendritic Cells ◽  
Dendritic Cell ◽  
Intrusion Detection ◽  
Detection System ◽  
Binary Classification ◽  
Data Sampling ◽  
Classification Problems ◽  
Peripheral Tissues ◽  
Network Intrusion ◽  
Dendritic Cell Algorithm

Abstract The dendritic cell algorithm (DCA) as one of the emerging evolutionary algorithms is based on the behavior of the specific immune agents, known as dendritic cells (DCs). DCA has several potentially beneficial features for binary classification problems. In this paper, we aim at providing a new version of this immune-inspired mechanism acts as a semi-supervised classifier, which can be a defensive shield in network intrusion detection problem. Till now, no strategy or idea has been adopted on the $Get_{Antigen()}$ function on the detection phase, but random sampling entails the DCA to provide undesirable results in several cycles at each time. This leads to uncertainty. Whereas it must be accomplished by biological behaviors of DCs in peripheral tissues, we have proposed a novel strategy that exactly acts based on its immunological functionalities of dendritic cells. The proposed mechanism focuses on two items: first, to obviate the challenge of needing to have a preordered antigen set for computing danger signal, and the second, to provide a novel immune-inspired idea for nonrandom data sampling. A variable functional migration threshold is also computed cycle by cycle that shows the necessity of the migration threshold flexibility. A significant criterion so-called capability of intrusion detection (CID) is used for tests. All the tests have been performed in a new benchmark dataset named UNSW-NB15. Experimental consequences demonstrate that the present schema as the best version among improved DC algorithms achieves 76.69% CID by 90% accuracy and outperforms its counterpart methods.

scholarly journals Using Adaptive Neuro-Fuzzy Inference System in Alert Management of Intrusion Detection Systems

2012 ◽  
Vol 4 (11) ◽  
pp. 32-38 ◽  
Author(s):  
Zahra Atashbar Orang ◽  
Ezzat Moradpour ◽  
Ahmad Habibizad Navin ◽  
Amir Azimi Alasti Ahrabim ◽  
Mir Kamal Mirnia
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Intrusion Detection Systems ◽  
Inference System ◽  
Detection Systems ◽  
Neuro Fuzzy

scholarly journals An Efficient Optimized Fuzzy Inference System based Intrusion Detection in Cloud Environment

2019 ◽  
Vol 8 (12) ◽  
pp. 4285-4295
Keyword(s):  
Intrusion Detection ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Detection System ◽  
Denial Of Service ◽  
Gravitational Search Algorithm ◽  
Detection Accuracy ◽  
Cloud Environment ◽  
Inference System ◽  
Fuzzy C Means

Security incidents namely, Denial of service (DoS), scanning, virus, malware code injection, worm and password cracking are becoming common in a cloud environment that affects the company and may produce an economic loss if not detected in time. These problems are handled by presenting an intrusion detection system (IDS) in the cloud. But, the existing cloud IDSs affect from low detection accuracy, high false detection rate and execution time. To tackle these issues, in this paper, gravitational search algorithm based fuzzy Inference system (GSA-FIS) is developed as intrusion detection. In this approach, fuzzy parameters are optimized using GSA. The proposed consist of two modules namely; Possibilistic Fuzzy C-Means (PFCM) algorithm based clustering, training based on GSA-FIS and testing process. Initially, the incoming data are pre-processed and clustered with the help of PFCM. PFCM is detecting the noise of fuzzy c-means clustering (FCM), conquer the coincident cluster problem of Possibilistic Fuzzy C-Means (PCM) and eradicate the row sum constraints of fuzzy Possibilistic c-means clustering (FPCM). After the clustering process, the clustered data are given to the optimized fuzzy Inference system (OFIS). Here, normal and abnormal data are identified by the Fuzzy score, while the training is done by the GSA through optimizing the entire fuzzy system. In this approach, four types of abnormal data are detected namely, probe, Remote to Local (R2L), User to Root (U2R), and DoS. Simulation results show that the performance of the proposed GSA-FIS based IDS outperforms that of the different scheme in terms of precision, recall and F-measure

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