scholarly journals Infrared Signature Suppression Systems in Modern Military Helicopters

2017 ◽  
Vol 2017 (3) ◽  
pp. 63-83
Author(s):  
Mateusz Paszko
Keyword(s):  
Infrared Emission ◽  
Detection Methods ◽  
Exhaust Gases ◽  
Detection Systems ◽  
Different Types ◽  
Infrared Signature ◽  
Ground Fire ◽  
Low Altitude ◽  
Potential Enemy ◽  
Suppression Systems

Abstract Helicopters play an important role in air-to-ground fire covering and the short-distance air-to-air fights, as well as the anti-tank missions and battlefield force transferring. The detection and survivability of helicopters on a battlefield significantly depends on their infrared emissions level, as well as the methods, equipment and systems used by potential enemy. The automatic detection systems, recognition and identification of flying objects use among other the thermo-detection methods, which rely on detecting the infrared radiation emitted by the tracked object. Furthermore, due to low-altitude and relatively low flight speed, today’s combat assets like missile weapons equipped with infrared guidance systems are one of the most important threats to the helicopters performing combat missions. Especially meaningful in a helicopter aviation is infrared emission by exhaust gases, egressed to the surroundings. Due to high temperature, exhaust gases are a major factor in detectability of a helicopter performing air combat operations. In order to increase the combat effectiveness and survivability of military helicopters, several different types of the infrared suppressor (IRS) have been developed. This paper reviews contemporary developments in this discipline, with particular examples of the infrared signature suppression systems.

scholarly journals Standalone Behaviour-Based Attack Detection Techniques for Distributed Software Systems via Blockchain

2021 ◽  
Vol 11 (12) ◽  
pp. 5685
Author(s):  
Hosam Aljihani ◽  
Fathy Eassa ◽  
Khalid Almarhabi ◽  
Abdullah Algarni ◽  
Abdulaziz Attaallah
Keyword(s):  
Attack Detection ◽  
Detection Methods ◽  
Software Systems ◽  
Distributed Software ◽  
Detection Techniques ◽  
Detection Systems ◽  
Novel Approach ◽  
Validation Experiment ◽  
Critical Issues ◽  
Concrete Solution

With the rapid increase of cyberattacks that presently affect distributed software systems, cyberattacks and their consequences have become critical issues and have attracted the interest of research communities and companies to address them. Therefore, developing and improving attack detection techniques are prominent methods to defend against cyberattacks. One of the promising attack detection methods is behaviour-based attack detection methods. Practically, attack detection techniques are widely applied in distributed software systems that utilise network environments. However, there are some other challenges facing attack detection techniques, such as the immutability and reliability of the detection systems. These challenges can be overcome with promising technologies such as blockchain. Blockchain offers a concrete solution for ensuring data integrity against unauthorised modification. Hence, it improves the immutability for detection systems’ data and thus the reliability for the target systems. In this paper, we propose a design for standalone behaviour-based attack detection techniques that utilise blockchain’s functionalities to overcome the above-mentioned challenges. Additionally, we provide a validation experiment to prove our proposal in term of achieving its objectives. We argue that our proposal introduces a novel approach to develop and improve behaviour-based attack detection techniques to become more reliable for distributed software systems.

scholarly journals Detecting Toe-Off Events Utilizing a Vision-Based Method

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 329 ◽  
Author(s):  
Yunqi Tang ◽  
Zhuorong Li ◽  
Huawei Tian ◽  
Jianwei Ding ◽  
Bingxian Lin
Keyword(s):  
Wearable Sensors ◽  
Gait Pattern ◽  
Video Data ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Public Database ◽  
Video Frames ◽  
Different Types ◽  
Events Detection ◽  
Good Detection

Detecting gait events from video data accurately would be a challenging problem. However, most detection methods for gait events are currently based on wearable sensors, which need high cooperation from users and power consumption restriction. This study presents a novel algorithm for achieving accurate detection of toe-off events using a single 2D vision camera without the cooperation of participants. First, a set of novel feature, namely consecutive silhouettes difference maps (CSD-maps), is proposed to represent gait pattern. A CSD-map can encode several consecutive pedestrian silhouettes extracted from video frames into a map. And different number of consecutive pedestrian silhouettes will result in different types of CSD-maps, which can provide significant features for toe-off events detection. Convolutional neural network is then employed to reduce feature dimensions and classify toe-off events. Experiments on a public database demonstrate that the proposed method achieves good detection accuracy.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

scholarly journals Hybrid classification model to detect advanced intrusions using data mining techniques

2018 ◽  
Vol 7 (2.4) ◽  
pp. 10
Author(s):  
V Mala ◽  
K Meena
Keyword(s):  
Data Mining ◽  
Learning Algorithm ◽  
Detection System ◽  
Classification Model ◽  
Detection Methods ◽  
Data Mining Techniques ◽  
Detection Systems ◽  
Intruder Detection ◽  
Hybrid Classification ◽  
Using Data

Traditional signature based approach fails in detecting advanced malwares like stuxnet, flame, duqu etc. Signature based comparison and correlation are not up to the mark in detecting such attacks. Hence, there is crucial to detect these kinds of attacks as early as possible. In this research, a novel data mining based approach were applied to detect such attacks. The main innovation lies on Misuse signature detection systems based on supervised learning algorithm. In learning phase, labeled examples of network packets systems calls are (gave) provided, on or after which algorithm can learn about the attack which is fast and reliable to known. In order to detect advanced attacks, unsupervised learning methodologies were employed to detect the presence of zero day/ new attacks. The main objective is to review, different intruder detection methods. To study the role of Data Mining techniques used in intruder detection system. Hybrid –classification model is utilized to detect advanced attacks.

Parameters of automated fraud detection techniques during online transactions

2018 ◽  
Vol 25 (3) ◽  
pp. 702-720 ◽  
Author(s):  
Vipin Khattri ◽  
Deepak Kumar Singh
Keyword(s):  
Detection System ◽  
Fraud Detection ◽  
Security System ◽  
Research Papers ◽  
Content Type ◽  
Detection Techniques ◽  
Online Transactions ◽  
Detection Systems ◽  
Different Types ◽  
Depth Study

Purpose This paper aims to provide information of parameters and techniques used in the automated fraud detection system during online transaction. With the increase in the use of online transactions, the concerns regarding data security have also increased. To tackle the frauds, lot of research has been done and plethora of papers are available on the related topics. The purpose of this paper is to provide the clear pathway for researchers to move in the direction of development of automated fraud detection system to prevent the fraud during online transaction. Design/methodology/approach This literature review analyses and compares the different types of techniques for detecting fraud during online transaction. An in-depth study of the most prominent journals has been done and the core methodology of the papers has been presented. This article also shed some light on different types of parameters used in fraud detection techniques during online transaction. Findings There are vast varieties of various fraud detection techniques, and every technique has completed task in its own way. After studying approximately 41 research papers, 14 books and four reports, in total 30 parameters have been identified and a detailed study of the parameters has been presented. The parameters are also listed with their details that how these parameters are used in the security system for detecting online transaction fraud. Research limitations/implications This paper provides empirical insight about the parameters and their prominence in the development of automated fraud detection security system of online transaction. This paper encourages the researchers to development of improved fraud detection system. Practical implications This paper will pave the way for researchers to do a focused research on the fraud detection methodologies. The analysis will help in zeroing down the most prevalent topic of research in this field. The researchers will be able to understand the internal details of parameters and techniques used in the fraud detection systems. This literature also helps the research to think in a variety of ways that how these parameters will be used in the development of fraud detection system. Originality/value This paper is one of the most comprehensive reviews in its field. It tries and attempts to fill a void created because of lack of compilation of the laid fraud detection parameters.

scholarly journals Red Cell Distribution Width as a Novel Marker for Different Types of Atrial Fibrillation in Low and High Altitude

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Kaiyue Han ◽  
Xiaoling Su ◽  
Jiang Liu ◽  
Fengcai Yao ◽  
FeiYan Lu
Keyword(s):  
Atrial Fibrillation ◽  
Regression Analysis ◽  
High Altitude ◽  
Multivariate Logistic Regression Analysis ◽  
Red Cell ◽  
Cell Distribution ◽  
Multivariate Logistic Regression ◽  
Distribution Width ◽  
Different Types ◽  
Low Altitude

Background. Increased red cell distribution width (RDW) can predict the incidence and mortality of cardiovascular diseases. However, there are limited data on the relationship between RDW and altitude and the subtype of atrial fibrillation (AF). We investigated the effects of altitude on RDW in patients with different types of AF. Methods. A total of 303 patients with nonvalvular AF were included. Of these, 156 lived in low altitude (77 paroxysmal AF, PAF; 79 persistent AF, PeAF) and 147 in high altitude (77 paroxysmal AF, PAF; 70 persistent AF, PeAF). In these groups, baseline characteristics, complete blood counts, serum biochemistry, and echocardiography were evaluated. Multivariate logistic regression analysis was conducted to determine the independent predictors of AF at the different altitudes. Results. In both low and high altitudes, RDW and left atrial diameter (LAD) were higher in AF than control subjects (P<0.05) and higher in persistent AF than paroxysmal AF (P<0.05). Compared with any groups (PAF group, PeAF group, or control group) of low-altitude, RDW and LAD were found higher in high-altitude corresponding groups. Multivariate logistic regression analysis demonstrated that RDW, mean corpuscular volume (MCV), and LAD levels independently associated with AF patients in low altitude (RDW, OR 1.687, 95% CI 1.021–2.789; P<0.05), while in high altitude, RDW, MCV, creatinine (Cr), and LAD were independent predictors for AF patients (RDW, OR 1.755, 95% CI 1.179–2.613; P<0.05). Conclusion. Elevated RDW levels may be an independent risk marker for nonvalvular AF, affected by type of AF and altitude.

scholarly journals Reporter Phage-Based Detection of Bacterial Pathogens: Design Guidelines and Recent Developments

Viruses ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 944 ◽  
Author(s):  
Susanne Meile ◽  
Samuel Kilcher ◽  
Martin J. Loessner ◽  
Matthew Dunne
Keyword(s):  
Viral Genome ◽  
Pathogen Detection ◽  
Bacterial Pathogens ◽  
Design Guidelines ◽  
Detection Methods ◽  
Clinical Samples ◽  
Cell Infection ◽  
Reporter Protein ◽  
Detection Systems ◽  
Conventional Culture

Fast and reliable detection of bacterial pathogens in clinical samples, contaminated food products, and water supplies can drastically improve clinical outcomes and reduce the socio-economic impact of disease. As natural predators of bacteria, bacteriophages (phages) have evolved to bind their hosts with unparalleled specificity and to rapidly deliver and replicate their viral genome. Not surprisingly, phages and phage-encoded proteins have been used to develop a vast repertoire of diagnostic assays, many of which outperform conventional culture-based and molecular detection methods. While intact phages or phage-encoded affinity proteins can be used to capture bacteria, most phage-inspired detection systems harness viral genome delivery and amplification: to this end, suitable phages are genetically reprogrammed to deliver heterologous reporter genes, whose activity is typically detected through enzymatic substrate conversion to indicate the presence of a viable host cell. Infection with such engineered reporter phages typically leads to a rapid burst of reporter protein production that enables highly sensitive detection. In this review, we highlight recent advances in infection-based detection methods, present guidelines for reporter phage construction, outline technical aspects of reporter phage engineering, and discuss some of the advantages and pitfalls of phage-based pathogen detection. Recent improvements in reporter phage construction and engineering further substantiate the potential of these highly evolved nanomachines as rapid and inexpensive detection systems to replace or complement traditional diagnostic approaches.

Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review

2019 ◽  
Author(s):  
Mingtao Wu ◽  
Young B. Moon
Keyword(s):  
Intrusion Detection ◽  
Manufacturing Systems ◽  
High Volume ◽  
Cyber Attacks ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
False Alarms ◽  
Alert Correlation ◽  
Acoustic Image ◽  
Detection Systems

Abstract Cyber-physical manufacturing system is the vision of future manufacturing systems where physical components are fully integrated through various networks and the Internet. The integration enables the access to computation resources that can improve efficiency, sustainability and cost-effectiveness. However, its openness and connectivity also enlarge the attack surface for cyber-attacks and cyber-physical attacks. A critical challenge in defending those attacks is that current intrusion detection methods cannot timely detect cyber-physical attacks. Studies showed that the physical detection provides a higher accuracy and a shorter respond time compared to network-based or host-based intrusion detection systems. Moreover, alert correlation and management methods help reducing the number of alerts and identifying the root cause of the attack. In this paper, the intrusion detection research relevant to cyber-physical manufacturing security is reviewed. The physical detection methods — using side-channel data, including acoustic, image, acceleration, and power consumption data to disclose attacks during the manufacturing process — are analyzed. Finally, the alert correlation methods — that manage the high volume of alerts generated from intrusion detection systems via logical relationships to reduce the data redundancy and false alarms — are reviewed. The study show that the cyber-physical attacks are existing and rising concerns in industry. Also, the increasing efforts in cyber-physical intrusion detection and correlation research can be utilized to secure the future manufacturing systems.

scholarly journals Automatic Fire and Smoke Detection Method for Surveillance Systems Based on Dilated CNNs

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1241
Author(s):  
Yakhyokhuja Valikhujaev ◽  
Akmalbek Abdusalomov ◽  
Young Im Cho
Keyword(s):  
Cost Effective ◽  
Classification Performance ◽  
Detection Methods ◽  
False Alarms ◽  
Surveillance Systems ◽  
Smoke Detection ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Unseen Data ◽  
Vision Detection

The technologies underlying fire and smoke detection systems play a crucial role in ensuring and delivering optimal performance in modern surveillance environments. In fact, fire can cause significant damage to lives and properties. Considering that the majority of cities have already installed camera-monitoring systems, this encouraged us to take advantage of the availability of these systems to develop cost-effective vision detection methods. However, this is a complex vision detection task from the perspective of deformations, unusual camera angles and viewpoints, and seasonal changes. To overcome these limitations, we propose a new method based on a deep learning approach, which uses a convolutional neural network that employs dilated convolutions. We evaluated our method by training and testing it on our custom-built dataset, which consists of images of fire and smoke that we collected from the internet and labeled manually. The performance of our method was compared with that of methods based on well-known state-of-the-art architectures. Our experimental results indicate that the classification performance and complexity of our method are superior. In addition, our method is designed to be well generalized for unseen data, which offers effective generalization and reduces the number of false alarms.

scholarly journals Review of Infrared signature suppression systems using optical blocking method

2019 ◽  
Vol 15 (3) ◽  
pp. 432-439 ◽  
Author(s):  
Nidhi Baranwal ◽  
Shripad P. Mahulikar
Keyword(s):  
Infrared Signature ◽  
Suppression Systems
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