Multi-View Vehicle Recognition Based on WRT-SVM

2013 ◽  
Vol 694-697 ◽  
pp. 1987-1992 ◽  
Author(s):  
Xing Gang Wu ◽  
Cong Guo
Keyword(s):  
False Positive Rate ◽  
Identification Problem ◽  
Classification Problem ◽  
Random Trees ◽  
Support Vector ◽  
Image Size ◽  
Scale Invariant ◽  
Vehicle Recognition ◽  
Positive Rate ◽  
Image Pairs

Proposed an approach to identify vehicles considering the variation in image size, illumination, and view angles under different cameras using Support Vector Machine with weighted random trees (WRT-SVM). With quantizing the scale-invariant features of image pairs by the weighted random trees, the identification problem is formulated as a same-different classification problem. Results show the efficiency of building the randomized tree due to the weights of the samples and the control of the false-positive rate of the identify system.

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 An Approach Based on the Improved SVM Algorithm for Identifying Malware in Network Traffic

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bo Liu ◽  
Jinfu Chen ◽  
Songling Qin ◽  
Zufa Zhang ◽  
Yisong Liu ◽  
...  
Keyword(s):  
Network Traffic ◽  
Cyber Security ◽  
False Positive ◽  
False Positive Rate ◽  
Support Vector ◽  
Traffic Classification ◽  
Svm Algorithm ◽  
Network Traffic Classification ◽  
Positive Rate ◽  
Function Selection

Due to the growth and popularity of the internet, cyber security remains, and will continue, to be an important issue. There are many network traffic classification methods or malware identification approaches that have been proposed to solve this problem. However, the existing methods are not well suited to help security experts effectively solve this challenge due to their low accuracy and high false positive rate. To this end, we employ a machine learning-based classification approach to identify malware. The approach extracts features from network traffic and reduces the dimensionality of the features, which can effectively improve the accuracy of identification. Furthermore, we propose an improved SVM algorithm for classifying the network traffic dubbed Optimized Facile Support Vector Machine (OFSVM). The OFSVM algorithm solves the problem that the original SVM algorithm is not satisfactory for classification from two aspects, i.e., parameter optimization and kernel function selection. Therefore, in this paper, we present an approach for identifying malware in network traffic, called Network Traffic Malware Identification (NTMI). To evaluate the effectiveness of the NTMI approach proposed in this paper, we collect four real network traffic datasets and use a publicly available dataset CAIDA for our experiments. Evaluation results suggest that the NTMI approach can lead to higher accuracy while achieving a lower false positive rate compared with other identification methods. On average, the NTMI approach achieves an accuracy of 92.5% and a false positive rate of 5.527%.

scholarly journals Image quality classification algorithm based on InceptionV3 and SVM

2019 ◽  
Vol 277 ◽  
pp. 02036
Author(s):  
Yu Li ◽  
Lizhuang Liu
Keyword(s):  
Image Quality ◽  
Data Augmentation ◽  
Classification Problem ◽  
Support Vector ◽  
Image Size ◽  
Image Description ◽  
Training Strategy ◽  
Video Images ◽  
Quality Classification ◽  
Proposed Model

In this work we investigate the use of deep learning for image quality classification problem. We use a pre-trained Convolutional Neural Network (CNN) for image description, and the Support Vector Machine (SVM) model is trained as an image quality classifier whose inputs are normalized features extracted by the CNN model. We report on different design choices, ranging from the use of various CNN architectures to the use of features extracted from different layers of a CNN model. To cope with the problem of a lack of adequate amounts of distorted picture data, a novel training strategy of multi-scale training, which is selecting a new image size for training after several batches, combined with data augmentation is introduced. The experimental results tested on the actual monitoring video images shows that the proposed model can accurately classify distorted images.

scholarly journals A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1210 ◽  
Author(s):  
Khraisat ◽  
Gondal ◽  
Vamplew ◽  
Kamruzzaman ◽  
Alazab
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

A Novel Hybrid LE and SVM with CV in Intrusion Detection

2014 ◽  
Vol 644-650 ◽  
pp. 2572-2576
Author(s):  
Qing Liu ◽  
Yun Kai Zhang ◽  
Qing Ru Li
Keyword(s):  
Intrusion Detection ◽  
False Positive Rate ◽  
Likelihood Estimation ◽  
Support Vector ◽  
Training Time ◽  
Detection Algorithms ◽  
Lower False Positive Rate ◽  
Proposed Model ◽  
Positive Rate ◽  
Rbf Kernel

A support vector machine (SVM) model combined Laplacian Eigenmaps (LE) with Cross Validation (CV) is proposed for intrusion detection. In the proposed model, a classifier is adopted to estimate whether an action is an attack or not. Maximum Likelihood Estimation (MLE) is used to estimate the intrinsic dimensions, and LE is used as a preprocessor of SVM to reduce the dimensions of feature vectors then training time is shortened. In order to improve the performance of SVM, CV is used to optimize the parameters of SVM in RBF kernel function. Compared with other detection algorithms, the experimental results show that the proposed model has the advantages: shorter training time, higher accuracy rate and lower false positive rate.

Deep-learning and radiomics ensemble classifier for false positive reduction in brain metastases segmentation

2021 ◽  
Author(s):  
Zi Yang ◽  
Mingli Chen ◽  
Mahdieh Kazemimoghadam ◽  
Lin Ma ◽  
Strahinja Stojadinovic ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Metastases ◽  
False Positive ◽  
False Positive Rate ◽  
Ensemble Classifier ◽  
Support Vector ◽  
Svm Classifier ◽  
Siamese Network ◽  
Proposed Model ◽  
Positive Rate

Abstract Stereotactic radiosurgery (SRS) is now the standard of care for brain metastases (BMs) patients. The SRS treatment planning process requires precise target delineation, which in clinical workflow for patients with multiple (>4) BMs (mBMs) could become a pronounced time bottleneck. Our group has developed an automated BMs segmentation platform to assist in this process. The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive segmentations, mainly caused by the injected contrast during MRI acquisition. To address this problem and further improve the segmentation performance, a deep-learning and radiomics ensemble classifier was developed to reduce the false-positive rate in segmentations. The proposed model consists of a Siamese network and a radiomic-based support vector machine (SVM) classifier. The 2D-based Siamese network contains a pair of parallel feature extractors with shared weights followed by a single classifier. This architecture is designed to identify the inter-class difference. On the other hand, the SVM model takes the radiomic features extracted from 3D segmentation volumes as the input for twofold classification, either a false-positive segmentation or a true BM. Lastly, the outputs from both models create an ensemble to generate the final label. The performance of the proposed model in the segmented mBMs testing dataset reached the accuracy (ACC), sensitivity (SEN), specificity (SPE) and area under the curve (AUC) of 0.91, 0.96, 0.90 and 0.93, respectively. After integrating the proposed model into the original segmentation platform, the average segmentation false negative rate (FNR) and the false positive over the union (FPoU) were 0.13 and 0.09, respectively, which preserved the initial FNR (0.07) and significantly improved the FPoU (0.55). The proposed method effectively reduced the false-positive rate in the BMs raw segmentations indicating that the integration of the proposed ensemble classifier into the BMs segmentation platform provides a beneficial tool for mBMs SRS management.

A Novel Method Based on Clustering Algorithm and SVM for Anomaly Intrusion Detection of Wireless Sensor Networks

2011 ◽  
Vol 121-126 ◽  
pp. 3745-3749
Author(s):  
Zheng Hong Xiao ◽  
Zhi Gang Chen ◽  
Xiao Heng Deng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
False Positive Rate ◽  
Wireless Sensor ◽  
Support Vector ◽  
High Detection Rate ◽  
Positive Rate ◽  
Anomaly Intrusion Detection

Based on the principle that the same class is adjacent, an anomaly intrusion detection method based on K-means and Support Vector Machine (SVM) is presented. In order to overcome the disadvantage that k-means algorithm requires initializing parameters, this paper proposes an improved K-means algorithm with a strategy of adjustable parameters. According to the location of wireless sensor networks (WSN), we can obtain clustering results by applying improved K-means algorithm to WSN, and then SVM algorithm is applied to different clusters for anomaly intrusion detection. Simulation results show that the proposed method can detect abnormal behaviors efficiently and has high detection rate and low false positive rate than the current typical intrusion detection schemes of WSN.

A Realistic Seizure Prediction Study Based on Multiclass SVM

2017 ◽  
Vol 27 (03) ◽  
pp. 1750006 ◽  
Author(s):  
Bruno Direito ◽  
César A. Teixeira ◽  
Francisco Sales ◽  
Miguel Castelo-Branco ◽  
António Dourado
Keyword(s):  
Statistical Significance ◽  
False Positive Rate ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Feature Sets ◽  
Scalp Eeg ◽  
Positive Rate ◽  
Eeg Recordings ◽  
Multiclass Svm

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.

scholarly journals Function Similarity Using Family Context

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1163
Author(s):  
Paul Black ◽  
Iqbal Gondal ◽  
Peter Vamplew ◽  
Arun Lakhotia
Keyword(s):  
False Positive Rate ◽  
Family Context ◽  
Support Vector ◽  
Unexpected Finding ◽  
Initial Experiment ◽  
Unrelated Pair ◽  
Svm Model ◽  
Positive Rate ◽  
A New Technique ◽  
Function Similarity

Finding changed and similar functions between a pair of binaries is an important problem in malware attribution and for the identification of new malware capabilities. This paper presents a new technique called Function Similarity using Family Context (FSFC) for this problem. FSFC trains a Support Vector Machine (SVM) model using pairs of similar functions from two program variants. This method improves upon previous research called Cross Version Contextual Function Similarity (CVCFS) e epresenting a function using features extracted not just from the function itself, but also, from other functions with which it has a caller and callee relationship. We present the results of an initial experiment that shows that the use of additional features from the context of a function significantly decreases the false positive rate, obviating the need for a separate pass for cleaning false positives. The more surprising and unexpected finding is that the SVM model produced by FSFC can abstract function similarity features from one pair of program variants to find similar functions in an unrelated pair of program variants. If validated by a larger study, this new property leads to the possibility of creating generic similar function classifiers that can be packaged and distributed in reverse engineering tools such as IDA Pro and Ghidra.

scholarly journals A Refined QSO Selection Method Using Diagnostics

2011 ◽  
Vol 7 (S285) ◽  
pp. 344-346
Author(s):  
Dae-Won Kim ◽  
Pavlos Protopapas ◽  
Markos Trichas ◽  
Michael Rowan-Robinson ◽  
Roni Khardon ◽  
...  
Keyword(s):  
False Positive Rate ◽  
Large Magellanic Cloud ◽  
Time Variability ◽  
Support Vector ◽  
High Confidence ◽  
Photometric Redshifts ◽  
Diagnostic Features ◽  
Machine Model ◽  
Additional Information ◽  
Positive Rate

AbstractWe present 663 QSO candidates in the Large Magellanic Cloud (LMC) that were selected using multiple diagnostics. We started with a set of 2,566 QSO candidates selected using the methodology presented in our previous work based on time variability of the MACHO LMC light curves. We then obtained additional information for the candidates by cross-matching them with the Spitzer SAGE, the 2MASS, the Chandra, the XMM, and an LMC UBVI catalogues. Using that information, we specified diagnostic features based on mid-IR colours, photometric redshifts using SED template fitting, and X-ray luminosities, in order to discriminate more high-confidence QSO candidates in the absence of spectral information. We then trained a one-class Support Vector Machine model using those diagnostics features. We applied the trained model to the original candidates, and finally selected 663 high-confidence QSO candidates. We cross-matched those 663 QSO candidates with 152 newly-confirmed QSOs and 275 non-QSOs in the LMC fields, and found that the false positive rate was less than 1%.

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