scholarly journals Intrusion Detecting System Based on Temporal Convolutional Network for In-Vehicle CAN Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dongxian Shi ◽  
Ming Xu ◽  
Ting Wu ◽  
Liang Kou
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Learning Theories ◽  
Network Intrusion Detection ◽  
Convolutional Network ◽  
Detection Model ◽  
Network Intrusion ◽  
Complicated Model ◽  
Abnormal Points

In recent years, deep learning theories, such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN), have been applied as effective methods for intrusion detection in the vehicle CAN network. However, the existing RNNs realize detection by establishing independent models for each CAN ID, which are unable to learn the potential characteristics of different IDs well, and have relatively complicated model structure and high calculation time cost. CNNs can achieve rapid detection by learning the characteristics of normal and attack CAN ID sequences and exhibit good performance, but the current methods do not locate abnormal points in the sequence. To solve the above problems, this paper proposes an in-vehicle CAN network intrusion detection model based on Temporal Convolutional Network, which is called Temporal Convolutional Network-Based Intrusion Detection System (TCNIDS). In TCNIDS, the CAN ID is serialized into a natural language sequence and a word vector is constructed for each CAN ID through the word embedding coding method to reduce the data dimension. At the same time, TCNIDS uses the parameterized Relu method to improve the temporal convolutional network, which can better learn the potential features of the normal sequence. The TCNIDS model has a simple structure and realizes the point anomaly detection at the message level by predicting the future sequence of normal CAN data and setting the probability strategy. The experimental results show that the overall detection rate, false alarm rate, and accuracy rate of TCNIDS under fuzzy attack, spoofing attack, and DoS attack are higher than those of the traditional temporal convolutional network intrusion detection model.

scholarly journals A Residual Learning-Based Network Intrusion Detection System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiarui Man ◽  
Guozi Sun
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Class Imbalance Problem ◽  
Data Set ◽  
Residual Learning ◽  
Network Intrusion

Neural networks have been proved to perform well in network intrusion detection. In order to acquire better features of network traffic, more learning layers are necessarily required. However, according to the results of the previous research, adding layers to the neural networks might fail to improve the classification results. In fact, after the number of layers has reached a certain threshold, performance of the model tends to degrade. In this paper, we propose a network intrusion detection model based on residual learning. After transforming the UNSW-NB15 data set into images, deeper convolutional neural networks with residual blocks are built to learn more critical features. Instead of the cross-entropy loss function, the modified focal loss is calculated to address the class imbalance problem in the training set and identify minor attacks in the testing set. Batch normalization and global average pooling are used to avoid overfitting and enhance the model. Experimental results show that the proposed model can improve attack detection accuracy compared with existing models.

scholarly journals CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guojie Liu ◽  
Jianbiao Zhang
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
False Positive Rate ◽  
Network Intrusion Detection ◽  
Test Results ◽  
Softmax Classifier ◽  
Detection Model ◽  
Network Intrusion

Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks.

An Effective Intrusion Detection Model Based on Random Forest and Neural Networks

2011 ◽  
Vol 267 ◽  
pp. 308-313 ◽  
Author(s):  
Shao Hong Zhong ◽  
Hua Jun Huang ◽  
Ai Bin Chen
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Intrusion Detection ◽  
Detection System ◽  
White Paper ◽  
Computationally Efficient ◽  
Detection Model ◽  
Detection Systems ◽  
Network Intrusion ◽  
Feature Selection Approach

This document explains and demonstrates how to prepare your camera-ready manuscript for Trans Tech Publications. The best is to read these instructions and follow the outline of this text. The text area for your manuscript must be 17 cm wide and 25 cm high (6.7 and 9.8 inches, resp.). Do not place any text outside this area. Use good quality, white paper of approximately 21 x 29 cm or 8 x 11 inches (please do not change the document setting from A4 to letter). Your manuscript will be reduced by approximately 20% by the publisher. Please keep this in mind when designing your figures and tables etc.Intrusion detection is a very important research domain in network security. Current intrusion detection systems (IDS) especially NIDS (Network Intrusion Detection System) examine all data features to detect intrusions. Also, many machine learning and data mining methods are utilized to fulfill intrusion detection tasks. This paper proposes an effective intrusion detection model that is computationally efficient and effective based on Random Forest based feature selection approach and Neural Networks (NN) model. We firstly utilize random forest method to select the most important features to eliminate the insignificant and/or useless inputs leads to a simplification of the problem, in order to faster and more accurate detection; Secondly, classic NN model is used to learn and detect intrusions using the selected important features. Experimental results on the well-known KDD 1999 dataset demonstrate the proposed hybrid model is actually effective.

scholarly journals FUSION OF RECIRCULATION NEURAL NETWORKS FOR REAL-TIME NETWORK INTRUSION DETECTION AND RECOGNITION

2014 ◽  
pp. 383-390
Author(s):  
Pavel Kachurka ◽  
Vladimir Golovko
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Real Time ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Traffic Data ◽  
Network Intrusion ◽  
Anomaly Detector ◽  
Detection And Recognition

Intrusion detection system is one of the essential security tools of modern information systems. Continuous development of new types of attacks re quires the development of intelligent approaches for intrusion detection capable to detect newest attacks. We present recirculation neural network based approach which lets to detect previously unseen attack types in real-time mode and to further correct recognition of this types. In this paper we use recirculation neural networks as an anomaly detector as well as a misuse detector, ensemble of anomaly and misuse detectors, fusion of several detectors for correct detection and recognition of attack types. The experiments held on both KDD’99 data and real network traffic data show promising results.

scholarly journals Random Forest Classifier Based Network Intrusion Detection System

2021 ◽  
Vol 9 (VI) ◽  
pp. 4603-4608
Author(s):  
Aadhar Dutta
Keyword(s):  
Random Forest ◽  
Intrusion Detection ◽  
Private Information ◽  
Intrusion Detection System ◽  
Detection System ◽  
Random Forest Classifier ◽  
Network Intrusion Detection ◽  
Detection Model ◽  
Digital World ◽  
Network Intrusion

In today's digital world, we all use the Internet and connect to a network, but all the data we send or receive, is safe? Some kind of attack is present in network packets that might access the computer's private information to the hacker. We cannot see and tell whether a network is safe to connect with or not, so we made a Network Intrusion Detection Model predict whether these network packets are secure or some attack is there on the package. We use Random Forest Classifier to obtain the maximum accuracy. To test our model in real-time, we have created a packet sniffer that would sniff out network packets, convert them into required features, and then try it in our model to predict the legitimacy of the network packet.

Sign in / Sign up

Export Citation Format

Share Document