Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction

2021 ◽  
pp. 388-414
Author(s):  
Jesus David Terrazas Gonzalez ◽  
Witold Kinsner
Keyword(s):  
Real Time ◽  
Denial Of Service ◽  
Synthetic Data ◽  
Window Size ◽  
Zero Crossing ◽  
Lévy Walks ◽  
Uniform Noise ◽  
The Time Domain ◽  
The Internet Of Things ◽  
Composite Signal

A comparison between the probability similarities of a Distributed Denial-of-Service (DDoS) dataset and Lévy walks is presented. This effort validates Lévy walks as a model resembling DDoS probability features. In addition, a method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks is demonstrated. The Smirnov transform is used to address a cybersecurity problem associated with the Internet-of-things (IoT). The synthetic Lévy-walk is merged with sections of distinct signals (uniform noise, Gaussian noise, and an ordinary sinusoid). Zero-crossing rate (ZCR) within a varying-size window is utilized to analyze both the composite signal and the DDoS dataset. ZCR identifies all the distinct sections in the composite signal and successfully detects the occurrence of the cyberattack. The ZCR value increases as the signal under analysis becomes more complex and produces steadier values as the varying window size increases. The ZCR computation directly in the time-domain is its most notorious advantage for real-time implementations.

Zero-Crossing Analysis of Lévy Walks and a DDoS Dataset for Real-Time Feature Extraction

2016 ◽  
Vol 8 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Jesus David Terrazas Gonzalez ◽  
Witold Kinsner
Keyword(s):  
Real Time ◽  
Denial Of Service ◽  
Synthetic Data ◽  
Window Size ◽  
Zero Crossing ◽  
Lévy Walks ◽  
Uniform Noise ◽  
The Time Domain ◽  
The Internet Of Things ◽  
Composite Signal

A comparison between the probability similarities of a Distributed Denial-of-Service (DDoS) dataset and Lévy walks is presented. This effort validates Lévy walks as a model resembling DDoS probability features. In addition, a method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks is demonstrated. The Smirnov transform is used to address a cybersecurity problem associated with the Internet-of-things (IoT). The synthetic Lévy-walk is merged with sections of distinct signals (uniform noise, Gaussian noise, and an ordinary sinusoid). Zero-crossing rate (ZCR) within a varying-size window is utilized to analyze both the composite signal and the DDoS dataset. ZCR identifies all the distinct sections in the composite signal and successfully detects the occurrence of the cyberattack. The ZCR value increases as the signal under analysis becomes more complex and produces steadier values as the varying window size increases. The ZCR computation directly in the time-domain is its most notorious advantage for real-time implementations.

Zero-Crossing Analysis and Information Divergence of Lévy Walks for Real-Time Feature Extraction

2016 ◽  
Vol 7 (4) ◽  
pp. 41-59
Author(s):  
Jesus David Terrazas Gonzalez ◽  
Witold Kinsner
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Engineering Problem ◽  
Complex Signals ◽  
Zero Crossing ◽  
Lévy Walks ◽  
Leibler Divergence ◽  
Information Divergence ◽  
The Time Domain

A method, based on the Smirnov transform, for generating synthetic data with the statistical properties of Lévy-walks is presented. This method can be utilized for generating arbitrary prescribed probability density functions (pdf). A cybersecurity engineering problem associated with Internet traffic is addressed. The synthetic Lévy-walks process is intertwined with sections of distinct characteristics creating a composite signal that is analyzed through zero-crossing rate (ZCR) within a varying-size window to identify sections. The advantages of the ZCR computation directly in the time-domain are appealing for real-time implementations. Moreover, the characterization of the degree of closeness, via the Kullback-Leibler divergence (KLD), among the pdfs of arbitrary processes (focusing on Lévy walks) and model pdfs is presented. The results obtained from the KLD experiments provide a categorical determination of the closeness degree. These results, a remarkable achievement in this research, are also promising to be used as features for classification of complex signals in real-time.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

Helicopter Rotor Blade Flexibility Simulation for Aeroelasticity and Flight Dynamics Applications

2014 ◽  
Vol 59 (4) ◽  
pp. 1-18 ◽  
Author(s):  
Ioannis Goulos ◽  
Vassilios Pachidis ◽  
Pericles Pilidis
Keyword(s):  
Real Time ◽  
Rotor Blade ◽  
Flight Dynamics ◽  
Integrated Approach ◽  
Flight Simulation ◽  
Response Characteristics ◽  
Helicopter Flight ◽  
Finite State ◽  
The Time Domain ◽  
Blade Loads

This paper presents a mathematical model for the simulation of rotor blade flexibility in real-time helicopter flight dynamics applications that also employs sufficient modeling fidelity for prediction of structural blade loads. A matrix/vector-based formulation is developed for the treatment of elastic blade kinematics in the time domain. A novel, second-order-accurate, finite-difference scheme is employed for the approximation of the blade motion derivatives. The proposed method is coupled with a finite-state induced-flow model, a dynamic wake distortion model, and an unsteady blade element aerodynamics model. The integrated approach is deployed to investigate trim controls, stability and control derivatives, nonlinear control response characteristics, and structural blade loads for a hingeless rotor helicopter. It is shown that the developed methodology exhibits modeling accuracy comparable to that of non-real-time comprehensive rotorcraft codes. The proposed method is suitable for real-time flight simulation, with sufficient fidelity for simultaneous prediction of oscillatory blade loads.

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