An Improved Feedforward Neural Network Using Salp Swarm Optimization Technique for the Design of Intrusion Detection System for Computer Network

2019 ◽  
pp. 867-875
Author(s):  
N. Neha ◽  
M. R. Gauthama Raman ◽  
Nivethitha Somu ◽  
R. Senthilnathan ◽  
V. Shankar Sriram
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Optimization Technique ◽  
Feedforward Neural Network ◽  
Swarm Optimization

Intrusion Detection System Using Grey Theory Combined with Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 864-867
Author(s):  
Liang Sun
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Feedforward Neural Network ◽  
Rapid Expansion ◽  
Grey Model ◽  
Grey Theory ◽  
Modern Computer ◽  
Detection Technology

In past few years, with the rapid expansion of computer networks, security has become an important issue for modern computer systems. Intrusion detection technology is an effective way to detect illegitimate use to ensure network security. In this paper, we proposes an intrusion detection system based on a feedforward neural network-aided grey model (FNNAGPM), by integrating a first-order single variable grey model (GM(1,1)) and a feedforward neural network. There are three phases in the system process, including initialization phase, GM(1,1) prediction phase and FNNAGPM prediction phase.

scholarly journals Performance Evaluation of Supervised Ensemble Cyber Situation Perception Models for Computer Network

2021 ◽  
Vol 12 (1) ◽  
pp. 1-14
Author(s):  
S.S. Olofintuyi ◽  
◽  
T.O. Omotehinwa ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Cyber Threats ◽  
Ensemble Algorithms

The trend at which cyber threats are gaining access to companies, industries and other sectors of the economy is becoming alarming, and this is posting a serious challenge to network administrators, governments and other business owners. A formidable intrusion detection system is needed to outplay the activities of the cyberattacks. An ensemble system is believed to perform better than a single classifier. With this fact, five different Machine Learning (ML) ensemble algorithms are suggested at the perception phase of Situation Awareness (SA) model for threat detection and the algorithms include; Artificial Neural Network Based Decision Tree (ANN based DT), Bayesian Based Artificial Neural Network (BN based ANN), J48 Based Naïve Bayes Model (J48 based NB), Decision Tree based Bayesian Network (BN) and Random Forest based on Support Vector Machine (RF based SVM). The efficiency and effectiveness of all the aforementioned algorithms were evaluated based on precision, recall and accuracy. ANN based DT gave 98.87% accuracy, BN based ANN gave 99.72% accuracy, J48 based NB gave 98.90% accuracy, DT based BN gave 89.92% accuracy and FR based SVM gave 98.40% accuracy. The implication of these results is that BN based ANN is more suitable in the perception phase of SA for threats detection. Keywords- Cyber-threats, Ensemble Algorithms, Computer Network, Intrusion Detection System, Machine Learning

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 DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

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