Network Intrusion Detection Framework Based on Whale Swarm Algorithm and Artificial Neural Network in Cloud Computing

2018 ◽  
pp. 56-65 ◽  
Author(s):  
Ahmed Mohammed Fahad ◽  
Abdulghani Ali Ahmed ◽  
Mohd Nizam Mohmad Kahar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Artificial Neural ◽  
Swarm Algorithm

Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System

2018 ◽  
Vol 27 (2) ◽  
pp. 135-147 ◽  
Author(s):  
Rafath Samrin ◽  
Devara Vasumathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Similarity Measures ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Benchmark Database ◽  
Artificial Neural

AbstractDespite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is trained with the utilization of ANN and its structure is stored. In the testing process, the data are tested by choosing the most suitable ANN classifier, which corresponds to the closest cluster to the test data, according to distance or similarity measures. For experimental evaluation, we used the benchmark database, and the results clearly demonstrated that the proposed technique outperformed the existing technique by having better accuracy.

scholarly journals Proposed Network Intrusion Detection System ‎In Cloud Environment Based on Back ‎Propagation Neural Network

2017 ◽  
Vol 26 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Shawq Malik Mehibs ◽  
Soukaena Hassan Hashim
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Back Propagation ◽  
Network Intrusion Detection ◽  
Cloud Environment ◽  
High Detection Rate ◽  
Network Intrusion

Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over the internet. This low-cost service fulfills the basic requirements of users. Because of the open nature and services introduced by cloud computing intruders impersonate legitimate users and misuse cloud resource and services. To detect intruders and suspicious activities in and around the cloud computing environment, intrusion detection system used to discover the illegitimate users and suspicious action by monitors different user activities on the network .this work proposed based back propagation artificial neural network to construct t network intrusion detection in the cloud environment. The proposed module evaluated with kdd99 dataset the experimental results shows promising approach to detect attack with high detection rate and low false alarm rate

scholarly journals Classification of Intrusion using Artificial Neural Network with GWO

2020 ◽  
Vol 9 (4) ◽  
pp. 599-606
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Data Set ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Artificial Neural

In the present milieu of connected world, where security is the major concern, Intrusion Detection System is the prominent area of research to deal with various types of attacks in network. Intrusion detection systems (IDS) finds the dynamic and malicious traffic of network, in accordance to the aspect of network. Various form of IDS has been developed working on distinctive approaches. One popular approach is machine learning in which various algorithms like ANN, SVM etc. have been used. But the most prominent method used is ANN. The performance of the ANN can significantly be improved by combining it with different metaheuristic algorithms. In present work, GWO is used to optimize ANN. For this KDD-99 data-set is used to classify various types of attacks i.e. denial of service (DOS), normal and other form of attack. The present paper provides detailed analysis of the performance of Artificial Neural Network and optimized Artificial Neural Network with GA, PSO and GWO. The research shows that ANN with GWO outperform as compared to others (ANN, ANN with PSO and ANN with GA).

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