scholarly journals An Improved K -Means Clustering Intrusion Detection Algorithm for Wireless Networks Based on Federated Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bin Xie ◽  
Xinyu Dong ◽  
Changguang Wang
Keyword(s):  
Wireless Networks ◽  
Intrusion Detection ◽  
Wireless Network ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Network Data ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Network Intrusion

The existing wireless network intrusion detection algorithms based on supervised learning confront many challenges, such as high false detection rate, difficulty in finding unknown attack behaviors, and high cost in obtaining labeled training data sets. This paper presents an improved k -means clustering algorithm for detecting intrusions on wireless networks based on Federated Learning. The proposed algorithm allows multiple participants to train a global model without sharing their private data and can expand the amount of data in the training model and protect the local data of each participant. Furthermore, the cosine distance of multiple perspectives is introduced in the algorithm to measure the similarity between network data objects in the improved k -means clustering process, making the clustering results more reasonable and the judgment of network data behavior more accurate. The AWID, an open wireless network attack data set, is selected as the experimental data set. Its dimensionality reduces by the method of principal component analysis (PCA). Experimental results show that the improved k -means clustering intrusion detection algorithm based on Federated Learning has better performance in detection rate, false detection rate, and detection of unknown attack types.

scholarly journals A Bayesian Classification Intrusion Detection Method Based on the Fusion of PCA and LDA

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Zhidong Shen ◽  
Yuhao Zhang ◽  
Weiying Chen
Keyword(s):  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Principal Component ◽  
Bayesian Classification ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Linear Discriminant

The rapid development of network technology is facing severe security threats while bringing convenience to people. How to build a secure network environment has become an important guarantee for social development. Intrusion detection plays an important role in the field of network security. With the complexity and diversification of networks, intrusion detection systems also need to be constantly improved and developed to match external environmental changes. The innovative work of this paper is as follows: principal component analysis and linear discriminant analysis are used to reduce the dimensionality of the data set, which avoids unnecessary detection content and improves detection efficiency and accuracy. The principal component analysis method, linear discriminant analysis algorithm, and Bayesian classification are combined to construct the PCA-LDA-BC classification algorithm, and the intrusion detection model is established based on this algorithm. The simulation experiment was carried out on the algorithm CICIDS2017 data set proposed in this paper. From the experimental results, it can be analysed that in the intrusion detection of missing data, the improved algorithm is compared with the traditional naive Bayesian classification algorithm, the detection rate is improved, and the false detection rate and the missed alarm rate are reduced. In terms of intrusion detection for various types of attacks, the detection rate, false detection rate, and missed alarm rate have been improved accordingly. It is proved that the algorithm has certain validity and feasibility.

Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN

2019 ◽  
Vol 22 (13) ◽  
pp. 2907-2921 ◽  
Author(s):  
Xinwen Gao ◽  
Ming Jian ◽  
Min Hu ◽  
Mohan Tanniru ◽  
Shuaiqing Li
Keyword(s):  
Boundary Layer ◽  
Defect Detection ◽  
Detection Rate ◽  
Layer Structure ◽  
Detection System ◽  
Identification Accuracy ◽  
Shield Tunnel ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.

Enhance Network Intrusion Detection System by Exploiting BR Algorithm as an Optimal Feature Selection

2015 ◽  
pp. 17-32 ◽  
Author(s):  
Soukaena Hassan Hashem
Keyword(s):  
Intrusion Detection ◽  
Wireless Network ◽  
Intrusion Detection System ◽  
Missing Values ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Wireless Data ◽  
Data Set ◽  
Network Intrusion ◽  
Network Intrusion Detection System

This chapter aims to build a proposed Wire/Wireless Network Intrusion Detection System (WWNIDS) to detect intrusions and consider many of modern attacks which are not taken in account previously. The proposal WWNIDS treat intrusion detection with just intrinsic features but not all of them. The dataset of WWNIDS will consist of two parts; first part will be wire network dataset which has been constructed from KDD'99 that has 41 features with some modifications to produce the proposed dataset that called modern KDD and to be reliable in detecting intrusion by suggesting three additional features. The second part will be building wireless network dataset by collecting thousands of sessions (normal and intrusion); this proposed dataset is called Constructed Wireless Data Set (CWDS). The preprocessing process will be done on the two datasets (KDD & CWDS) to eliminate some problems that affect the detection of intrusion such as noise, missing values and duplication.

A Model of Detector Generation Based on Immune Recognition and Redundancy Optimization

2013 ◽  
Vol 457-458 ◽  
pp. 783-787
Author(s):  
Ya Ping Jiang ◽  
Jun Wei Zhao ◽  
Yue Xia Tian
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Network Intrusion Detection ◽  
Immune Recognition ◽  
Defense System ◽  
False Detection ◽  
Simulation Experiments ◽  
Network Intrusion ◽  
Prevention System ◽  
Redundancy Optimization

The theory of modern immunology provides a novel idea to study network intrusion detection and defense system. With the concepts of self, nonself, close degree and membership in an intrusion detection and prevention system presented in this paper, a model of detector generation based on immune recognition and redundancy optimization is proposed, in which detectors are generated by clone selection, genetic variation and evolutionary algorithm, as well as the improved redundancy optimization algorithm. The simulation experiments show that the model has higher detection rate and lower false detection rate.

scholarly journals A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System

Electronics ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 1689
Author(s):  
Abel A. Reyes ◽  
Francisco D. Vaca ◽  
Gabriel A. Castro Aguayo ◽  
Quamar Niyaz ◽  
Vijay Devabhaktuni
Keyword(s):  
Machine Learning ◽  
Wireless Networks ◽  
Intrusion Detection ◽  
Wireless Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Two Stage ◽  
Network Intrusion ◽  
Network Intrusion Detection System

The growth of wireless networks has been remarkable in the last few years. One of the main reasons for this growth is the massive use of portable and stand-alone devices with wireless network connectivity. These devices have become essential on the daily basis in consumer electronics. As the dependency on wireless networks has increased, the attacks against them over time have increased as well. To detect these attacks, a network intrusion detection system (NIDS) with high accuracy and low detection time is needed. In this work, we propose a machine learning (ML) based wireless network intrusion detection system (WNIDS) for Wi-Fi networks to efficiently detect attacks against them. The proposed WNIDS consists of two stages that work together in a sequence. An ML model is developed for each stage to classify the network records into normal or one of the specific attack classes. We train and validate the ML model for WNIDS using the publicly available Aegean Wi-Fi Intrusion Dataset (AWID). Several feature selection techniques have been considered to identify the best features set for the WNIDS. Our two-stage WNIDS achieves an accuracy of 99.42% for multi-class classification with a reduced set of features. A module for eXplainable Artificial Intelligence (XAI) is implemented as well to understand the influence of features on each type of network traffic records.

Network Intrusion Detection Methods Based on Deep Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiangwen Li ◽  
Shuang Zhang
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Network Intrusion Detection ◽  
Test Time ◽  
Data Set ◽  
Network Intrusion ◽  
Kdd Cup 99

: To detect network attacks more effectively, this study uses Honeypot techniques to collect the latest network attack data and proposes network intrusion detection classification models based on deep learning combined with DNN and LSTM models. Experiments showed that the data set training models gave better results than the KDD CUP 99 training model’s detection rate and false positive rate. The DNN-LSTM intrusion detection algorithm proposed in this study gives better results than KDD CUP 99 training model. Compared to other algorithms such as LeNet, DNN-LSTM intrusion detection algorithm exhibits shorter classification test time along with better accuracy and recall rate of intrusion detection.

A novel infrared moving target detection algorithm based on multiscale codebook model

2016 ◽  
Vol 14 (04) ◽  
pp. 1650023
Author(s):  
Lei Liu ◽  
Yayun Zhou ◽  
He Li ◽  
Wei Huang ◽  
Minjie Cui
Keyword(s):  
Target Detection ◽  
Detection Rate ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Detection Algorithm ◽  
Moving Target ◽  
False Detection Rate ◽  
False Detection ◽  
Moving Target Detection ◽  
Fusion Operation

Traditional target detection algorithm based on codebook model only use pixel information of video image while spatial scale information of image is ignored, so the detection result usually has high false detection rate and the target’s characteristics is not obvious. To overcome this difficulty, a novel infrared (IR) moving target detection algorithm based on multiscale codebook model is presented in this paper. The main principle of this algorithm is to make full use of image pixel information and scale information for moving target detection. First, by Gauss pyramid image hierarchical model, the IR video is stratified into three layers, namely the original layer, the second layer and the top layer. Second, background codebook model is built for each layer image, the main feature information is discovered to update background codebook models, and then moving target in IR video is detected according to the updated background model. Finally, the fusion operation is done on detection results of three layers to get the final detection result. Compared with traditional detection algorithm based on codebook model, this IR target detection algorithm combines image pixel information and scale characteristics. By using this novel algorithm, the experiments on some real world IR images are performed. The whole algorithm implementing processes and results are analyzed, and this novel detection algorithm is evaluated from the two aspects: subjective evaluation and objective evaluation. From the experiment results, we can see that the proposed method has better detection effects, richer target information and lower false detection rate.

scholarly journals A Computer Vision-Based Dual Network Approach for Indoor Fall Detection

2020 ◽  
Vol 5 (8) ◽  
pp. 939-943 ◽  
Author(s):  
Lu Zhang ◽  
Chun Fang ◽  
Ming Zhu
Keyword(s):  
Fall Detection ◽  
The Elderly ◽  
Video Stream ◽  
Detection Algorithm ◽  
False Detection Rate ◽  
False Detection ◽  
Data Set ◽  
Family Activities ◽  
Safety Risks ◽  
Indoor Scenarios

In order to strengthen the monitoring of the elderly and reduce the safety risks caused by falls, a video-based indoor fall detection algorithm using a dual network structure is proposed. Firstly, for the recorded video stream, we apply the fine-tuned YOLACT network to extract the contours of the human body, and then design a simple convolutional neural network to distinguish the categories of different family activities (including bending, standing, sitting and lying), and finally make a fall decision. When a lying position is detected on the floor region, it is considered as a fall. Experiments show that the proposed algorithm can successfully detect fall events in different indoor scenarios, and have a low false detection rate on the constructed data set.

scholarly journals A Reliable Network Intrusion Detection Approach Using Decision Tree with Enhanced Data Quality

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Azidine Guezzaz ◽  
Said Benkirane ◽  
Mourade Azrour ◽  
Shahzada Khurram
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
Data Quality ◽  
Detection Rate ◽  
Intrusion Detection Systems ◽  
Network Intrusion Detection ◽  
Network Data ◽  
Detection Systems ◽  
Network Intrusion ◽  
Detection Approach

Due to the recent advancements in the Internet of things (IoT) and cloud computing technologies and growing number of devices connected to the Internet, the security and privacy issues are important to be resolved and protect the data and computer network. To provide security, a real-time monitoring of the network data and resources is needed. Intrusion detection systems have been used to monitor, detect, and alert an intrusion event in real time. Recently, the intrusion detection systems (IDS) incorporate several machine learning (ML) techniques. One of the techniques is decision tree, which can take reliable network measures and make good decisions by increasing the detection rate and accuracy. In this paper, we propose a reliable network intrusion detection approach using decision tree with enhanced data quality. Specifically, network data preprocessing and entropy decision feature selection is carried out for enhancing the data quality and relevant training; then, a decision tree classifier is built for reliable intrusion detection. Experimental study on two datasets shows that the proposed model can reach robust results. Actually, our model achieves 99.42% and 98.80% accuracy with NSL-KDD and CICIDS2017 datasets, respectively. The novel approach gives many advantages compared to the other models in term of accuracy (ACC), detection rate (DR), and false alarm rate (FAR).

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