scholarly journals Machine Learning for 5G MIMO Modulation Detection

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1556
Author(s):  
Haithem Ben Chikha ◽  
Ahmad Almadhor ◽  
Waqas Khalid
Keyword(s):  
Machine Learning ◽  
Software Defined Radio ◽  
Mimo Systems ◽  
False Positive Rate ◽  
Cognitive Radios ◽  
Machine Learning Techniques ◽  
Destination Node ◽  
Detection Techniques ◽  
Positive Rate ◽  
Modulation Detection

Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI). At the destination node, we extract the higher-order statistics of the received signals as the discriminating features. After applying the principal component analysis technique, we carry out a comparative study between the random committee and the AdaBoost machine learning techniques (MLTs) at low signal-to-noise ratio. The efficiency metrics, including the true positive rate, false positive rate, precision, recall, F-Measure, and the time taken to build the model, are used for the performance comparison. The simulation results show that the use of the random committee MLT, compared to the AdaBoost MLT, provides gain in terms of both the modulation detection and complexity.

scholarly journals IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2857
Author(s):  
Laura Vigoya ◽  
Diego Fernandez ◽  
Victor Carneiro ◽  
Francisco Nóvoa
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
High Detection Rate ◽  
Security Vulnerabilities ◽  
Smart Systems ◽  
Learning Techniques ◽  
Positive Rate

With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

scholarly journals Detecting Website Defacements Based on Machine Learning Techniques and Attack Signatures

Computers ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 35 ◽  
Author(s):  
Xuan Dau Hoang ◽  
Ngoc Tuong Nguyen
Keyword(s):  
Machine Learning ◽  
Web Applications ◽  
False Positive Rate ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Web Pages ◽  
Government Organizations ◽  
Detection Model ◽  
Learning Techniques ◽  
Positive Rate

Defacement attacks have long been considered one of prime threats to websites and web applications of companies, enterprises, and government organizations. Defacement attacks can bring serious consequences to owners of websites, including immediate interruption of website operations and damage of the owner reputation, which may result in huge financial losses. Many solutions have been researched and deployed for monitoring and detection of website defacement attacks, such as those based on checksum comparison, diff comparison, DOM tree analysis, and complicated algorithms. However, some solutions only work on static websites and others demand extensive computing resources. This paper proposes a hybrid defacement detection model based on the combination of the machine learning-based detection and the signature-based detection. The machine learning-based detection first constructs a detection profile using training data of both normal and defaced web pages. Then, it uses the profile to classify monitored web pages into either normal or attacked. The machine learning-based component can effectively detect defacements for both static pages and dynamic pages. On the other hand, the signature-based detection is used to boost the model’s processing performance for common types of defacements. Extensive experiments show that our model produces an overall accuracy of more than 99.26% and a false positive rate of about 0.27%. Moreover, our model is suitable for implementation of a real-time website defacement monitoring system because it does not demand extensive computing resources.

scholarly journals Early Weed Detection Using Image Processing and Machine Learning Techniques in an Australian Chilli Farm

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 387
Author(s):  
Nahina Islam ◽  
Md Mamunur Rashid ◽  
Santoso Wibowo ◽  
Cheng-Yuan Xu ◽  
Ahsan Morshed ◽  
...  
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Weed Detection ◽  
Learning Techniques ◽  
Positive Rate ◽  
Uav Images

This paper explores the potential of machine learning algorithms for weed and crop classification from UAV images. The identification of weeds in crops is a challenging task that has been addressed through orthomosaicing of images, feature extraction and labelling of images to train machine learning algorithms. In this paper, the performances of several machine learning algorithms, random forest (RF), support vector machine (SVM) and k-nearest neighbours (KNN), are analysed to detect weeds using UAV images collected from a chilli crop field located in Australia. The evaluation metrics used in the comparison of performance were accuracy, precision, recall, false positive rate and kappa coefficient. MATLAB is used for simulating the machine learning algorithms; and the achieved weed detection accuracies are 96% using RF, 94% using SVM and 63% using KNN. Based on this study, RF and SVM algorithms are efficient and practical to use, and can be implemented easily for detecting weed from UAV images.

scholarly journals An Ensemble-Based Malware Detection Model Using Minimum Feature Set

MENDEL ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 1-10 ◽  
Author(s):  
Ivan Zelinka ◽  
Eslam Amer
Keyword(s):  
Machine Learning ◽  
False Positive Rate ◽  
Malware Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Detection Model ◽  
Learning Techniques ◽  
Proposed Model ◽  
Positive Rate ◽  
Minimum Number

Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.

Machine Learning for Detecting Scallops in AUV Benthic Images

2015 ◽  
pp. 22-40
Author(s):  
Prasanna Kannappan ◽  
Herbert G. Tanner ◽  
Arthur C. Trembanis ◽  
Justin H. Walker
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Template Matching ◽  
False Positive Rate ◽  
Image Data ◽  
Machine Learning Techniques ◽  
Detection And Counting ◽  
Attractive Option ◽  
Positive Rate ◽  
Histogram Of Gradients

A large volume of image data, in the order of thousands to millions of images, can be generated by robotic marine surveys aimed at assessment of organism populations. Manual processing and annotation of individual images in such large datasets is not an attractive option. It would seem that computer vision and machine learning techniques can be used to automate this process, yet to this date, available automated detection and counting tools for scallops do not work well with noisy low-resolution images and are bound to produce very high false positive rates. In this chapter, we hone a recently developed method for automated scallop detection and counting for the purpose of drastically reducing its false positive rate. In the process, we compare the performance of two customized false positive filtering alternatives, histogram of gradients and weighted correlation template matching.

scholarly journals Deep Learning Approach for Voice Pathology Detection and Classification

2021 ◽  
Vol 16 (4) ◽  
pp. 1-30
Author(s):  
Vikas Mittal ◽  
R. K. Sharma
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Kappa Index ◽  
Voice Signal ◽  
Classification Score ◽  
Positive Rate ◽  
Voice Pathology Detection ◽  
Sensitivity Specificity

A non-invasive cum robust voice pathology detection and classification architecture is proposed in the current manuscript. In place of the conventional feature-based machine learning techniques, a new architecture is proposed herein which initially performs deep learning-based filtering of the input voice signal, followed by a decision-level fusion of deep learning and a non-parametric learner. The efficacy of the proposed technique is verified by performing a comparative study with very recent work on the same dataset but based on different training algorithms.The proposed architecture has five different stages.The results are recorded in terms of nine (9) different classification score indices which are – mean average Precision, sensitivity, specificity, F1 score, accuracy, error, false-positive rate, Matthews Correlation Coefficient, and the Cohen’s Kappa index. The experimental results have shown that the use of machine learning classifier can get at most 96.12% accuracy, while the proposed technique achieved the highest accuracy of 99.14% in comparison to other techniques.

scholarly journals Comparison of malware detection techniques using machine learning algorithm

2019 ◽  
Vol 16 (1) ◽  
pp. 435 ◽  
Author(s):  
Nur Syuhada Selamat ◽  
Fakariah Hani Mohd Ali
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
False Positive Rate ◽  
Malware Detection ◽  
Virus Detection ◽  
Detection Accuracy ◽  
Decision Tree Algorithm ◽  
Security Threat ◽  
Detection Techniques ◽  
Positive Rate

<p>Currently, the volume of malware grows faster each year and poses a thoughtful global security threat. The number of malware developed increases as computers became interconnected, at an alarming rate in the 1990s. This scenario resulted the increment of malware. It also caused many protections are built to fight the malware. Unfortunately, the current technology is no longer effective to handle more advanced malware. Malware authors have created them to become more difficult to be evaded from anti-virus detection. In the current research, Machine Learning (ML) algorithm techniques became more popular to the researchers to analyze malware detection. In this paper, researchers proposed a defense system which uses three ML algorithm techniques comparison and select them based on the high accuracy malware detection. The result indicates that Decision Tree algorithm is the best detection accuracy compares to others classifier with 99% and 0.021% False Positive Rate (FPR) on a relatively small dataset.</p>

scholarly journals Challenges for Tractogram Filtering

2021 ◽  
pp. 149-168
Author(s):  
Daniel Jörgens ◽  
Maxime Descoteaux ◽  
Rodrigo Moreno
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
False Positive Rate ◽  
Machine Learning Techniques ◽  
Post Processing ◽  
Learning Techniques ◽  
Processing Step ◽  
Positive Rate ◽  
Neural Fiber ◽  
Modern Machine

AbstractTractography aims at describing the most likely neural fiber paths in white matter. A general issue of current tractography methods is their large false-positive rate. An approach to deal with this problem is tractogram filtering in which anatomically implausible streamlines are discarded as a post-processing step after tractography. In this chapter, we review the main approaches and methods from literature that are relevant for the application of tractogram filtering. Moreover, we give a perspective on the central challenges for the development of new methods, including modern machine learning techniques, in this field in the next few years.

Detecting DDoS Attacks in IoT Environment

2021 ◽  
Vol 15 (2) ◽  
pp. 145-180
Author(s):  
Yasmine Labiod ◽  
Abdelaziz Amara Korba ◽  
Nacira Ghoualmi-Zine
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Detection System ◽  
False Positive Rate ◽  
Cyber Attacks ◽  
Machine Learning Techniques ◽  
Ddos Attacks ◽  
High Detection Rate ◽  
Detection Techniques ◽  
Detection Model

With the great potential of internet of things (IoT) infrastructure in different domains, cyber-attacks are also rising commensurately. Distributed denials of service (DDoS) attacks are one of the cyber security threats. This paper will focus on DDoS attacks by adding the design of an intrusion detection system (IDS) tailored to IoT systems. Moreover, machine learning techniques will be investigated to distinguish the data representing flows of network traffic, which include both normal and DDoS traffic. In addition, these techniques will be used to help make a refined detection model for identifying different types of DDoS attacks. Furthermore, the performance of machine learning-based proposed solution is validated using N-BaIoT dataset and compared through different evaluation metrics. The experimental results show that the proposed IDS not only detects DDoS attacks types but also has a high detection rate and low false positive rate, which argues the usefulness of the proposed approach in comparison with several existing DDoS attacks detection techniques.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

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