scholarly journals A Comparative Study of Three Detection Techniques for Leifsonia xyli Subsp. xyli, the Causal Pathogen of Sugarcane Ratoon Stunting Disease

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Qibin Wu ◽  
Yong-Bao Pan ◽  
Dinggang Zhou ◽  
Michael P. Grisham ◽  
Shiwu Gao ◽  
...  
Keyword(s):  
Recombinant Plasmid ◽  
Detection System ◽  
Conventional Polymerase Chain Reaction ◽  
Conventional Pcr ◽  
Ratoon Stunting Disease ◽  
Detection Techniques ◽  
Real Time Quantitative Pcr ◽  
Causal Pathogen ◽  
Time Required ◽  
Degradation Diagnosis

The ratoon stunting disease (RSD), caused by the bacterium Leifsonia xyli subsp. xyli (Lxx), is one of the most economically devastating diseases impacting sugarcane. RSD causes significant yield losses and variety degradation. Diagnosis of RSD is challenging because it does not exhibit any discernible internal and external symptoms. Moreover, the Lxx bacteria are very small and difficult to isolate, cultivate, and detect. In this study, conventional polymerase chain reaction (PCR), real-time quantitative PCR (RT-qPCR), and Lxx-loop-mediated isothermal amplification (Lxx-LAMP) were utilized to specifically detect the presence of Lxx pathogens in the juice from Lxx-infected sugarcane stalks and an Lxx-pMD18-T recombinant plasmid. The results showed that Lxx was a highly specific causal pathogen for RSD. All three techniques provided great reproducibility, while Lxx-LAMP had the highest sensitivity. When the DNA extract from Lxx-infected sugarcane juice was used as a template, Lxx-LAMP was 10 and 100 times more sensitive than RT-qPCR and conventional PCR, respectively. When the Lxx-pMD18-T recombinant plasmid was used as a template, Lxx-LAMP was as sensitive as RT-qPCR but was 10 times more sensitive than conventional PCR. Based on the Lxx-LAMP detection system established, adding 0.4 μM loop primers (LF/LP) can accelerate the reaction and reduce the total time required. In addition, the optimal amount of Bst DNA polymerase for Lxx-LAMP reactions was determined to be 6.0 U. The results provide technical support for the detection of RSD Lxx pathogen that will help manage sugarcane RSD.

scholarly journals Building an Effective Intrusion Detection System using combined Signature and Anomaly Detection Techniques

2019 ◽  
Vol 8 (10) ◽  
pp. 429-435 ◽  
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection System ◽  
False Positive Rate ◽  
Hybrid Approach ◽  
Detection Techniques ◽  
Data Mining Approach ◽  
Positive Rate ◽  
Security Infrastructure ◽  
Time Required

Intrusion Detection Systems (IDS) are providing better solution to the current issues and thus became an important element of any security infrastructure to detect various threats so as to prevent widespread harm. The basic aim of IDS is to detect attacks and their nature and prevent damage to the computer systems. Several different approaches for intrusion detection have been reported in the literature. These approaches are broadly categorized into three approaches: I) Signature-based approach II) Anomaly based approach and III) Hybrid approach that combines signature and anomaly detection approaches. Hybrid approach has been found to be superior to either signature based or anomaly based approaches. Several different algorithms are available for hybrid approach. This paper suggests the combined approach using signature and anomaly detection techniques. The signature based is build using genetic algorithm as filter based feature selection and J48 as classifier and data mining approach is used to build anomaly based IDS. The performance of combined IDS is evaluated on well known datasets such as KDD Cup 99, UGR 16 and Kyoto 2006+ etc. The experimental results presented here are encouraging and show superiority of combined IDS to detect network anomalies with respect to time required building the model, detection rate, accuracy and false positive rate.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

scholarly journals Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3052
Author(s):  
Mas Ira Syafila Mohd Hilmi Tan ◽  
Mohd Faizal Jamlos ◽  
Ahmad Fairuz Omar ◽  
Fatimah Dzaharudin ◽  
Suramate Chalermwisutkul ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Early Detection ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Predictive Analytics ◽  
Detection System ◽  
Imaging Techniques ◽  
Ganoderma Boninense ◽  
Machine Learning Classification ◽  
Detection Techniques

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.

Development of laboratory x-ray fluorescence holography equipment

2003 ◽  
Vol 18 (6) ◽  
pp. 1471-1473 ◽  
Author(s):  
Yukio Takahashi ◽  
Kouichi Hayashi ◽  
Kimio Wakoh ◽  
Naomi Nishiki ◽  
Eiichiro Matsubara
Keyword(s):  
Single Crystal ◽  
Count Rate ◽  
Detection System ◽  
High Count ◽  
Synchrotron Source ◽  
High Count Rate ◽  
X Ray ◽  
Graphite Monochromator ◽  
Time Required

Laboratory x-ray fluorescence holography equipment was developed. A single-bent graphite monochromator with a large curvature and a high-count-rate x-ray detection system were applied in this equipment. To evaluate the performance of this equipment, a hologram pattern of a gold single crystal was measured. It took two days, which was about one-third the time required for the previous measurements using the conventional x-ray source and several times that using the synchrotron source. The quality of the hologram pattern is as good as that obtained using the synchrotrons. Clear atomic images on (002) are reconstructed.

scholarly journals Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5446
Author(s):  
Hyojung Ahn ◽  
Inchoon Yeo
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Short Term Memory ◽  
Detection System ◽  
Noisy Environments ◽  
Acoustic Data ◽  
Detection Techniques ◽  
Machine Operation ◽  
Detection Technology ◽  
Environment Noise

As the workforce shrinks, the demand for automatic, labor-saving, anomaly detection technology that can perform maintenance on advanced equipment such as vehicles has been increasing. In a vehicular environment, noise in the cabin, which directly affects users, is considered an important factor in lowering the emotional satisfaction of the driver and/or passengers in the vehicles. In this study, we provide an efficient method that can collect acoustic data, measured using a large number of microphones, in order to detect abnormal operations inside the machine via deep learning in a quick and highly accurate manner. Unlike most current approaches based on Long Short-Term Memory (LSTM) or autoencoders, we propose an anomaly detection (AD) algorithm that can overcome the limitations of noisy measurement and detection system anomalies via noise signals measured inside the mechanical system. These features are utilized to train a variety of anomaly detection models for demonstration in noisy environments with five different errors in machine operation, achieving an accuracy of approximately 90% or more.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

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.

A Review of Intrusion Detection Systems in Cloud Computing

2019 ◽  
pp. 54-83
Author(s):  
Chiba Zouhair ◽  
Noreddine Abghour ◽  
Khalid Moussaid ◽  
Amina El Omri ◽  
Mohamed Rida
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Detection System ◽  
Security Requirements ◽  
Intrusion Detection Systems ◽  
Cloud Environment ◽  
Detection Techniques ◽  
Detection Systems ◽  
Cloud Architecture ◽  
Data Source

Security is a major challenge faced by cloud computing (CC) due to its open and distributed architecture. Hence, it is vulnerable and prone to intrusions that affect confidentiality, availability, and integrity of cloud resources and offered services. Intrusion detection system (IDS) has become the most commonly used component of computer system security and compliance practices that defends cloud environment from various kinds of threats and attacks. This chapter presents the cloud architecture, an overview of different intrusions in the cloud, the challenges and essential characteristics of cloud-based IDS (CIDS), and detection techniques used by CIDS and their types. Then, the authors analyze 24 pertinent CIDS with respect to their various types, positioning, detection time, and data source. The analysis also gives the strength of each system and limitations in order to evaluate whether they carry out the security requirements of CC environment or not.

Hybrid Intrusion Detection Framework for Ad hoc networks

2016 ◽  
Vol 10 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Abdelaziz Amara Korba ◽  
Mehdi Nafaa ◽  
Salim Ghanemi
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Ad Hoc Networks ◽  
Intrusion Detection System ◽  
Ad Hoc ◽  
Detection System ◽  
Security Framework ◽  
Detection Techniques ◽  
Wide Range ◽  
Hoc Networks

In this paper, a cluster-based hybrid security framework called HSFA for ad hoc networks is proposed and evaluated. The proposed security framework combines both specification and anomaly detection techniques to efficiently detect and prevent wide range of routing attacks. In the proposed hierarchical architecture, cluster nodes run a host specification-based intrusion detection system to detect specification violations attacks such as fabrication, replay, etc. While the cluster heads run an anomaly-based intrusion detection system to detect wormhole and rushing attacks. The proposed specification-based detection approach relies on a set of specifications automatically generated, while anomaly-detection uses statistical techniques. The proposed security framework provides an adaptive response against attacks to prevent damage to the network. The security framework is evaluated by simulation in presence of malicious nodes that can launch different attacks. Simulation results show that the proposed hybrid security framework performs significantly better than other existing mechanisms.

scholarly journals Monitoring of rotavirus in treated wastewater in Tehran with a monthly interval, in 2017–2018

2020 ◽  
Vol 18 (6) ◽  
pp. 1065-1072
Author(s):  
Shadi Tavakoli Nick ◽  
Seyed Reza Mohebbi ◽  
Seyed Masoud Hosseini ◽  
Hamed Mirjalali ◽  
Masoud Alebouyeh
Keyword(s):  
Rna Extraction ◽  
Conventional Polymerase Chain Reaction ◽  
Treated Wastewater ◽  
Harsh Environments ◽  
Monthly Interval ◽  
Rt Pcr ◽  
Conventional Pcr ◽  
Wastewater Treatment Process ◽  
Polymerase Chain ◽  
Elution Method

Abstract Rotaviruses are among the major causes of viral acute gastroenteritis in newborns and children younger than 5 years worldwide. The ability of rotaviruses to remain infectious in harsh environments as well as in the wastewater treatment process makes them one of the most prevalent enteric viruses. The current study aimed to determine the presence of rotavirus genomes and to analyze them phylogenetically in secondary treated wastewater (TW) samples. In total, 13 TW samples were collected from September 2017 to August 2018. Viral concentration was carried out using the absorption-elution method, and after RNA extraction and cDNA synthesis, real-time and conventional polymerase chain reaction (PCR) were performed. A phylogenetic tree was drawn using Maximum Likelihood and Tamura 3-parameter using MEGA v.6 software. Rotavirus genomes were detected in 7/13 (53.8%) and 3/13 (23.07%) samples using reverse transcription (RT)-PCR and conventional PCR, respectively. Accordingly, phylogenetic analysis revealed G4P[8], G9P[4], and G9P[8] genotypes among the samples. The presence of rotavirus in secondary TW samples discharged into surface water emphasizes the importance of monitoring and assessing viral contamination in the water sources used for agricultural and recreational purposes.

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