scholarly journals Autonomous Abnormal Behaviour Detection Using Trajectory Analysis

2019 ◽  
Vol 9 (4) ◽  
pp. 2403
Author(s):  
Muhammed Shuaau ◽  
Ka Fei Thang ◽  
Nai Shyan Lai
Keyword(s):  
Anomaly Detection ◽  
Trajectory Analysis ◽  
False Positive Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Asia Pacific ◽  
Detection Accuracy ◽  
Abnormal Behaviour ◽  
Positive Rate ◽  
Reception Area

<span lang="EN-GB">Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%.</span>

Autonomous Anomaly Detection System for Crime Monitoring

2019 ◽  
Vol 16 (8) ◽  
pp. 3410-3418
Author(s):  
Muhammed Shuaau ◽  
Ka Fei Thang
Keyword(s):  
Anomaly Detection ◽  
Trajectory Analysis ◽  
Detection System ◽  
False Positive Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Asia Pacific ◽  
Detection Accuracy ◽  
Positive Rate ◽  
Reception Area

Autonomous anomaly detection has attracted significant amount of attention in the past decade due to increased security concerns all around the world. The volume of data reported by surveillance cameras has outrun human capacity and there exists a greater need for anomaly detection systems for crime monitoring. This project proposes a solution to this problem in a reception area context by using trajectory analysis. Trajectory extraction is proposed by using Gaussian Mixture Models and Kalman Filter for data association. Then trajectory analysis is performed on extracted trajectories to detect four different anomalies which are entering staff area, running, loitering and squatting down. The proposed anomaly detection method is tested on datasets recorded at Asia Pacific University’s reception area. The proposed algorithms were able to achieve a detection accuracy of 89% and a false positive rate of 4.52%. The results presented show the effectiveness of the proposed method.

scholarly journals Identity Verification in Virtual Education Using Biometric Analysis Based on Keystroke Dynamics

TecnoLógicas ◽  
2020 ◽  
Vol 23 (47) ◽  
pp. 197-211
Author(s):  
Daniel Escobar Grisales ◽  
Juan. C. Vásquez-Correa ◽  
Jesús F. Vargas-Bonilla ◽  
Juan Rafael Orozco-Arroyave
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Virtual Education ◽  
Identity Verification ◽  
Biometric Analysis ◽  
Positive Rate ◽  
Multiple Issues ◽  
Verification Strategy

Virtual education has become one of the tools most widely used by students at all educational levels, not just because of its convenience and flexibility, but also because it can expand educational coverage. All these benefits also bring along multiple issues in terms of security and reliability in the evaluation the of student’s knowledge because traditional identity verification strategies, such as the combination of username and password, do not guarantee that the student enrolled in the course really takes the exam. Therefore, a system with a different type of verification strategy should be designed to differentiate valid users from impostors. This study proposes a new verification system based on distances computed among Gaussian Mixture Models created with different writing task. The proposed approach is evaluated in two different modalities namely intrusive verification and non-intrusive verification. The intrusive mode provides a false positive rate of around 16 %, while the non-intrusive mode provides a false positive rate of 12 % In addition, the proposed strategy for non-intrusive verification is compared to a work previously reported in the literature and the results show that our approach reduces the equal error rate in about 24.3 %. The implemented strategy does not need additional hardware; only the computer keyboard is required to complete the user verification, which makes the system attractive, flexible, and practical for virtual education platforms.

scholarly journals Hybrid Internal Anomaly Detection System for IoT: Reactive Nodes with Cross-Layer Operation

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Nanda Kumar Thanigaivelan ◽  
Ethiopia Nigussie ◽  
Seppo Virtanen ◽  
Jouni Isoaho
Keyword(s):  
Anomaly Detection ◽  
False Positive ◽  
Detection System ◽  
False Positive Rate ◽  
Security Analysis ◽  
Abnormal Behavior ◽  
Test Bed ◽  
Positive Rate ◽  
Parent Node ◽  
Anomaly Detection System

We present a hybrid internal anomaly detection system that shares detection tasks between router and nodes. It allows nodes to react instinctively against the anomaly node by enforcing temporary communication ban on it. Each node monitors its own neighbors and if abnormal behavior is detected, the node blocks the packets of the anomaly node at link layer and reports the incident to its parent node. A novel RPL control message, Distress Propagation Object (DPO), is formulated and used for reporting the anomaly and network activities to the parent node and subsequently to the router. The system has configurable profile settings and is able to learn and differentiate between the nodes normal and suspicious activities without a need for prior knowledge. It has different subsystems and operation phases that are distributed in both the nodes and router, which act on data link and network layers. The system uses network fingerprinting to be aware of changes in network topology and approximate threat locations without any assistance from a positioning subsystem. The developed system was evaluated using test-bed consisting of Zolertia nodes and in-house developed PandaBoard based gateway as well as emulation environment of Cooja. The evaluation revealed that the system has low energy consumption overhead and fast response. The system occupies 3.3 KB of ROM and 0.86 KB of RAM for its operations. Security analysis confirms nodes reaction against abnormal nodes and successful detection of packet flooding, selective forwarding, and clone attacks. The system’s false positive rate evaluation demonstrates that the proposed system exhibited 5% to 10% lower false positive rate compared to simple detection 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 Deep-Learning-Based Road Crack Detection Frameworks for Dashcam-captured Images under Different Illumination Conditions

2021 ◽  
Author(s):  
Da-Ren Chen ◽  
Wei-Min Chiu
Keyword(s):  
Object Detection ◽  
Large Scale ◽  
Crack Detection ◽  
State Of The Art ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Machine Learning Techniques ◽  
Detection Accuracy ◽  
The Road ◽  
Art Object

Abstract Machine learning techniques have been used to increase detection accuracy of cracks in road surfaces. Most studies failed to consider variable illumination conditions on the target of interest (ToI), and only focus on detecting the presence or absence of road cracks. This paper proposes a new road crack detection method, IlumiCrack, which integrates Gaussian mixture models (GMM) and object detection CNN models. This work provides the following contributions: 1) For the first time, a large-scale road crack image dataset with a range of illumination conditions (e.g., day and night) is prepared using a dashcam. 2) Based on GMM, experimental evaluations on 2 to 4 levels of brightness are conducted for optimal classification. 3) the IlumiCrack framework is used to integrate state-of-the-art object detecting methods with CNN to classify the road crack images into eight types with high accuracy. Experimental results show that IlumiCrack outperforms the state-of-the-art R-CNN object detection frameworks.

scholarly journals Evaluation of speaker de-identification based on voice gender and age conversion

2018 ◽  
Vol 69 (2) ◽  
pp. 138-147 ◽  
Author(s):  
Jiří Přibil ◽  
Anna Přibilová ◽  
Jindřich Matoušek
Keyword(s):  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Scale Transformation ◽  
English Speakers ◽  
Detection Accuracy ◽  
Frequency Scale ◽  
Open Set ◽  
Gender And Age ◽  
Prosody Modification ◽  
Apparent Age

AbstractTwo basic tasks are covered in this paper. The first one consists in the design and practical testing of a new method for voice de-identification that changes the apparent age and/or gender of a speaker by multi-segmental frequency scale transformation combined with prosody modification. The second task is aimed at verification of applicability of a classifier based on Gaussian mixture models (GMM) to detect the original Czech and Slovak speakers after applied voice deidentification. The performed experiments confirm functionality of the developed gender and age conversion for all selected types of de-identification which can be objectively evaluated by the GMM-based open-set classifier. The original speaker detection accuracy was compared also for sentences uttered by German and English speakers showing language independence of the proposed method.

scholarly journals Efficient Detection of Large-Scale Multimedia Network Information Data Anomalies Based on the Rule-Extracting Matrix Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jie Zhao
Keyword(s):  
Large Scale ◽  
False Positive Rate ◽  
Detection Accuracy ◽  
Network Information ◽  
Matrix Algorithm ◽  
Efficient Detection ◽  
Sample Data ◽  
Multimedia Social Networks ◽  
Positive Rate ◽  
Rule Extracting

With the continuous development of multimedia social networks, online public opinion information is becoming more and more popular. The rule extraction matrix algorithm can effectively improve the probability of information data to be tested. The network information data abnormality detection is realized through the probability calculation, and the prior probability is calculated, to realize the detection of abnormally high network data. Practical results show that the rule-extracting matrix algorithm can effectively control the false positive rate of sample data, the detection accuracy is improved, and it has efficient detection performance.

scholarly journals An Enhanced Multimedia Video Surveillance Security Using Wavelet Encryption Framework

2020 ◽  
Author(s):  
Velliangiri S
Keyword(s):  
Wavelet Transform ◽  
False Positive Rate ◽  
Digital Data ◽  
Detection Accuracy ◽  
Detection Time ◽  
Arbitrary Permutation ◽  
Secret Keys ◽  
Positive Rate ◽  
Cae System ◽  
Distortion Rate

Multimedia digital data include medical record and financial documents, which are not guaranteed with security. The concerns for security of multimedia digital data is been a widespread issue in the field of cybernetics. With increasing malwares in video payloads, the proposed study aims to reduce the embedding of malwares using Pseudo Arbitrary Permutation based Cellular Automata Encryption (PAP-CAE) System in video payloads. This method reduces the malware attacks and distortion rate by permuting the secret keys with Pseudo arbitrary permutation. Before the application of PAP-CAE, 2D wavelet transform is applied on the multimedia files that compresses the complex files into different scales and position to be transmitted via a network with reduced size. Simultaneously, it performs the process of decryption and decompression to retrieve the original files. The proposed method is evaluated against existing methods to test its efficacy in terms of detection accuracy, detection time of malwares and false positive rate. The result shows that the proposed method is effective against the detection of malwares in multimedia video files.

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