scholarly journals Applying a Neural Network Ensemble to Intrusion Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 177-188 ◽  
Author(s):  
Simone A. Ludwig
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Confusion Matrix ◽  
Ensemble Method ◽  
Machine Learning Techniques ◽  
Neural Network Ensemble ◽  
Data Set ◽  
The Neural Network ◽  
Learning Machine

Abstract An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated.

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.

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.

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).

A Review on Intrusion Detection System using Artificial Intelligence Approach

2018 ◽  
Vol 4 (8) ◽  
pp. 6
Author(s):  
Apoorva Deshpande
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
High Detection Rate ◽  
Learning Techniques

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by researchers at different times.

Intelligent Radial Basis Function Neural Network for Intrusion Detection in Battle Field

2018 ◽  
pp. 151-173
Author(s):  
Kirupa Ganapathy
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
False Alarm ◽  
Radial Basis Function ◽  
Learning Process ◽  
Basis Function ◽  
Detection System ◽  
The Neural Network ◽  
Training Samples ◽  
Radial Basis

Defense at boundary is nowadays well equipped with perimeter protection, cameras, fence sensors, radars etc. However, in battlefield there is more feasibility of entering of a non-native human and unknowing stamping of the explosives placed in the various paths by the native soldiers. There exists no alert system in the battlefield for the soldiers to identify the intruder or the explosives in the field. Therefore, there is a need for an automated intelligent intrusion detection system for battlefield monitoring. This chapter proposes an intelligent radial basis function neural network (RBFNN) technique for intrusion detection and explosive identification. The proposed intelligent RBFNN implements some intellectual components in the algorithm to make the neural network think before learning the training samples. Involvement of intellectual components makes the learning process simple, effective and efficient. The proposed technique helps to reduce false alarm and encourages timely detection thereby providing extensive support for the native soldiers and save the life of the mankind.

Anomaly-Based Intrusion Detection Systems for Mobile Ad Hoc Networks

2021 ◽  
Vol 12 (2) ◽  
pp. 11-32
Author(s):  
Sharada Ramakrishna Valiveti ◽  
Anush Manglani ◽  
Tadrush Desai
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Ad Hoc Networks ◽  
Ad Hoc ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Heterogeneous Environments ◽  
Topology Management ◽  
Data Set ◽  
Hoc Networks

Ad hoc networks are used in heterogeneous environments like tactical military applications, where no centrally coordinated infrastructure is available. The network is required to perform self-configuration, dynamic topology management, and ensure the self-sustainability of the network. Security is hence of paramount importance. Anomaly-based intrusion detection system (IDS) is a distributed activity carried out by all nodes of the network in a cooperative manner along with other related network activities like routing, etc. Machine learning and its advances have found a promising place in anomaly detection. This paper describes the journey of defining the most suitable routing protocol for implementing IDS for tactical applications, along with the selection of the related suitable data set. The paper also reviews the latest machine learning techniques, implementation capabilities, and limitations.

Intrusion detection algorithm based on image enhanced convolutional neural network

2021 ◽  
pp. 1-12
Author(s):  
Qian Wang ◽  
Wenfang Zhao ◽  
Jiadong Ren
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Binary Classification ◽  
Original Data ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Softmax Classifier

Intrusion Detection System (IDS) can reduce the losses caused by intrusion behaviors and protect users’ information security. The effectiveness of IDS depends on the performance of the algorithm used in identifying intrusions. And traditional machine learning algorithms are limited to deal with the intrusion data with the characteristics of high-dimensionality, nonlinearity and imbalance. Therefore, this paper proposes an Intrusion Detection algorithm based on Image Enhanced Convolutional Neural Network (ID-IE-CNN). Firstly, based on the image processing technology of deep learning, oversampling method is used to increase the amount of original data to achieve data balance. Secondly, the one-dimensional data is converted into two-dimensional image data, the convolutional layer and the pooling layer are used to extract the main features of the image to reduce the data dimensionality. Third, the Tanh function is introduced as an activation function to fit nonlinear data, a fully connected layer is used to integrate local information, and the generalization ability of the prediction model is improved by the Dropout method. Finally, the Softmax classifier is used to predict the behavior of intrusion detection. This paper uses the KDDCup99 data set and compares with other competitive algorithms. Both in the performance of binary classification and multi-classification, ID-IE-CNN is better than the compared algorithms, which verifies its superiority.

scholarly journals Survey for the Prediction of Chronic Kidney Disease using Machine Learning

2019 ◽  
pp. 154-158
Author(s):  
Pooja Sharma ◽  
Saket J Swarndeep
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Kidney Diseases ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Network System ◽  
Data Set ◽  
Neural Network System

According the 2010 global burden of disease study, Chronic Kidney Diseases (CKD) was ranked 18th in the list of causes of total no. of deaths worldwide. 10% of the population worldwide is affected by CKD. The prediction of CKD can become a boon for the population to predict the health. Various method and techniques are undergoing the research phase for developing the most accurate CKD prediction system. Using Machine Learning techniques is the most promising one in this area due to its computing function and Machine Learning rules. Existing Systems are working well in predicting the accurate result but still more attributes of data and complicity of health parameter make the root layer for the innovation of new approaches. This study focuses on a novel approach for improving the prediction of CKD. In recent time Neural network system has discovered its use in disease diagnoses, which is depended upon prediction from symptoms data set. Chronic kidney disease detection system using neural network is shown here. This system of neural network accepts disease-symptoms as input and it is trained according to various training algorithms. After neural network is trained using back propagation algorithms, this trained neural network system is used for detection of kidney disease in the human body.

scholarly journals Network Intrusion Detection and Measuring the Data Set Performance by Machine Learning Technique (MLT)

2019 ◽  
Vol 8 (4) ◽  
pp. 11806-11809
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Computer Network ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Traffic Information ◽  
Data Set ◽  
Identification Rate ◽  
Network Intrusion ◽  
Learning Techniques

Intrusion Detection System (IDS) is the most mainstream approach to protect a computer network from different malicious activities to identify an intrusion. There have been a lot of attempts towards more exceptional performance specifically in IDSs which depends on Data Mining (DM) and Machine Learning Techniques (MLT). Though there is a destructive issue in that available assessment, DataSet (DS), called KDD DS, can't reflect current network circumstances and the most recent attack situations. As far as we could know, there is no possible assessment DS. We present a novel evaluation DS in this paper, called Kyoto, based on the 5 years of actual traffic information, which derived from different sorts of honey pots. This Kyoto DS is utilized for testing and assessing distinctive MLT has examined in this work. The attention was on unprocessed measurements True +ve (TrPo), False +ve (FaPo), True – ve (TrNa), and False – ve (FaNa) to assess execution and to improve the identification rate of IDS.

scholarly journals Application of Temperature Prediction Based on Neural Network in Intrusion Detection of IoT

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xuefei Liu ◽  
Chao Zhang ◽  
Pingzeng Liu ◽  
Maoling Yan ◽  
Baojia Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Defense Mechanism ◽  
Detection System ◽  
The Internet ◽  
Adaptive Capabilities ◽  
The Neural Network ◽  
The Internet Of Things

The security of network information in the Internet of Things faces enormous challenges. The traditional security defense mechanism is passive and certain loopholes. Intrusion detection can carry out network security monitoring and take corresponding measures actively. The neural network-based intrusion detection technology has specific adaptive capabilities, which can adapt to complex network environments and provide high intrusion detection rate. For the sake of solving the problem that the farmland Internet of Things is very vulnerable to invasion, we use a neural network to construct the farmland Internet of Things intrusion detection system to detect anomalous intrusion. In this study, the temperature of the IoT acquisition system is taken as the research object. It has divided which into different time granularities for feature analysis. We provide the detection standard for the data training detection module by comparing the traditional ARIMA and neural network methods. Its results show that the information on the temperature series is abundant. In addition, the neural network can predict the temperature sequence of varying time granularities better and ensure a small prediction error. It provides the testing standard for the construction of an intrusion detection system of the Internet of Things.

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