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 HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

scholarly journals Enhanced Network Intrusion Detection System

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7835
Author(s):  
Ketan Kotecha ◽  
Raghav Verma ◽  
Prahalad V. Rao ◽  
Priyanshu Prasad ◽  
Vipul Kumar Mishra ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
High Detection Rate ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Network Intrusion Detection Systems ◽  
High Detection

A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alongside modelling, a comprehensive data analysis on the features of the dataset itself using our understanding of correlation, variance, and similar factors for a wider picture is done for better modelling. Furthermore, hypothetical ponderings are discussed for potential network intrusion detection systems, including suggestions on prospective modelling and dataset generation as well.

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