Electro-optically modulated lossy-mode resonance

Sensitivity, selectivity, reliability, and measurement range of a sensor are vital parameters for its wide applications. Fast growing number of various detection systems seems to justify worldwide efforts to enhance one or some of the parameters. Therefore, as one of the possible solutions, multi-domain sensing schemes have been proposed. This means that the sensor is interrogated simultaneously in, e.g., optical and electrochemical domains. An opportunity to combine the domains within a single sensor is given by optically transparent and electrochemically active transparent conductive oxides (TCOs), such as indium tin oxide (ITO). This work aims to bring understanding of electro-optically modulated lossy-mode resonance (LMR) effect observed for ITO-coated optical fiber sensors. Experimental research supported by numerical modeling allowed for identification of the film properties responsible for performance in both domains, as well as interactions between them. It has been found that charge carrier density in the semiconducting ITO determines the efficiency of the electrochemical processes and the LMR properties. The carrier density boosts electrochemical activity but reduces capability of electro-optical modulation of the LMR. It has also been shown that the carrier density can be tuned by pressure during magnetron sputtering of ITO target. Thus, the pressure can be chosen as a parameter for optimization of electro-optical modulation of the LMR, as well as optical and electrochemical responses of the device, especially when it comes to label-free sensing and biosensing.

THE USE OF DATA MINING IN DETECTING CREDIT CARD FRAUD

Abstract— Credit card fraud is a serious problem for e-commerce retailers with UK merchants reporting losses of $574.2M in 2020. As a result, effective fraud detection systems must be in place to ensure that payments are processed securely in an online environment. From the literature, the detection of credit card fraud is challenging due to dataset imbalance (genuine versus fraudulent transactions), real-time processing requirements, and the dynamic behavior of fraudsters and customers. It is proposed in this paper that the use of machine learning could be an effective solution for combating credit card fraud.According to research, machine learning techniques can play a role in overcoming the identified challenges while ensuring a high detection rate of fraudulent transactions, both directly and indirectly. Even though both supervised and unsupervised machine learning algorithms have been suggested, the flaws in both methods point to the necessity for hybrid approaches.

Research and development automatically generate detection rules for IDS based on machine learning technology

Abstract—Nowadays, there have been many signature-based intrusion detection systems deployed and widely used. These systems are capable of detecting known attacks with low false alarm rates, fast detection times, and little system resource requirements. However, these systems are less effective against new attacks that are not included in the ruleset. In addition, recent studies provide a new approach to the problem of detecting unknown types of network attacks based on machine learning and deep learning. However, this new approach requires a lot of resources, processing time and has a high false alarm rate. Therefore, it is necessary to find a solution that combines the advantages of the two approaches above in the problem of detecting network attacks. In this paper, the authors present a method to automatically generate network attack detection rules for the IDS system based on the results of training machine learning models. Through testing, the author proves that the system that automatically generates network attack detection rules for IDS based on machine learning meets the requirements of increasing the ability to detect new types of attacks, ensuring automatic effective updates of new signs of network attacks. Tóm tắt—Ngày nay, đã có nhiều hệ thống phát hiện xâm nhập dựa trên chữ ký được triển khai và sử dụng rộng rãi. Các hệ thống này có khả năng phát hiện các cuộc tấn công đã biết với tỷ lệ báo động giả thấp, thời gian phát hiện nhanh và yêu cầu ít tài nguyên hệ thống. Tuy nhiên, các hệ thống này kém hiệu quả khi chống lại các cuộc tấn công mới không có trong tập luật. Các nghiên cứu gần đây cung cấp một cách tiếp cận mới cho vấn đề phát hiện các kiểu tấn công mạng mới dựa trên học máy và học sâu. Tuy nhiên, cách tiếp cận này đòi hỏi nhiều tài nguyên, thời gian xử lý. Vì vậy, cần tìm ra giải pháp kết hợp ưu điểm của hai cách tiếp cận trên trong bài toán phát hiện tấn công mạng. Trong bài báo này, nhóm tác giả trình bày phương pháp tự động sinh luật phát hiện tấn công mạng cho hệ thống phát hiện xâm nhập dựa trên kết quả huấn luyện mô hình học máy. Qua thử nghiệm, tác giả chứng minh rằng phương pháp này đáp ứng yêu cầu tăng khả năng phát hiện chính xác các kiểu tấn công mới, đảm bảo tự động cập nhật hiệu quả các dấu hiệu tấn công mạng mới vào tập luật.

IMPLEMENTASI ALGORITMA YOLO DAN TESSERACT OCR PADA SISTEM DETEKSI PLAT NOMOR OTOMATIS

Currently, vehicle number plate detection systems in general still use the manual method. This will take a lot of time and human effort. Thus, an automatic vehicle number plate detection system is needed because the number of vehicles that continues to increase will burden human labor. In addition, the methods used for vehicle number plate detection still have low accuracy because they depend on the characteristics of the object being used. This study develops a YOLO-based automatic vehicle number plate detection system. The dataset used is a pretrained YOLOv3 model of 700 data. Then proceed with the number plate text extraction process using the Tesseract Optical Character Recognition (OCR) library and the results obtained will be stored in the database. This system is web-based and API so that it can be used online and on the cross-platform. The test results show that the automatic number plate detection system reaches 100% accuracy with sufficient lighting and a threshold of 0.5 and for the results using the Tesseract library, the detection results are 92.32% where the system is successful in recognizing all characters on the license plates of cars and motorcycles. in the form of Alphanumeric characters of 7-8 characters.

On the Detection Capabilities of Signature-Based Intrusion Detection Systems in the Context of Web Attacks

Signature-based Intrusion Detection Systems (SIDS) play a crucial role within the arsenal of security components of most organizations. They can find traces of known attacks in the network traffic or host events for which patterns or signatures have been pre-established. SIDS include standard packages of detection rulesets, but only those rules suited to the operational environment should be activated for optimal performance. However, some organizations might skip this tuning process and instead activate default off-the-shelf rulesets without understanding its implications and trade-offs. In this work, we help gain insight into the consequences of using predefined rulesets in the performance of SIDS. We experimentally explore the performance of three SIDS in the context of web attacks. In particular, we gauge the detection rate obtained with predefined subsets of rules for Snort, ModSecurity and Nemesida using seven attack datasets. We also determine the precision and rate of alert generated by each detector in a real-life case using a large trace from a public webserver. Results show that the maximum detection rate achieved by the SIDS under test is insufficient to protect systems effectively and is lower than expected for known attacks. Our results also indicate that the choice of predefined settings activated on each detector strongly influences its detection capability and false alarm rate. Snort and ModSecurity scored either a very poor detection rate (activating the less-sensitive predefined ruleset) or a very poor precision (activating the full ruleset). We also found that using various SIDS for a cooperative decision can improve the precision or the detection rate, but not both. Consequently, it is necessary to reflect upon the role of these open-source SIDS with default configurations as core elements for protection in the context of web attacks. Finally, we provide an efficient method for systematically determining which rules deactivate from a ruleset to significantly reduce the false alarm rate for a target operational environment. We tested our approach using Snort’s ruleset in our real-life trace, increasing the precision from 0.015 to 1 in less than 16 h of work.

Machine Learning Approach for Intrusion Detection Systems as a Cyber Security Strategy for Small and Medium Enterprises

Small and medium enterprises (SMEs) are businesses that account for a large percentage of the economy in many countries, but they lack cyber security. The present study examines different supervised machine learning methods with a focus on intrusion detection systems (IDSs) that will help in improving SMEs’ security. The algorithms that are tested through a real dataset, are Naïve Bayes, Sequential minimal optimization (SMO), C4.5 decision tree, and Random Forest. The experiments are run using the Waikato Environment for Knowledge Analyses (WEKA) 3.8.4 tools and the metrics used to evaluate the results were: accuracy, false-positive rate (FPR), and total time to train and build a classification model. The results obtained from the original dataset with 130 features show a high value of accuracy, but the computation time to build the classification model was notably high for the cases of C4.5 (1 hr. and 20 mins) and SMO algorithm (4 hrs. and 20 mins). the Information Gain (IG) method was used and the result was impressive. The time needed to train the model was reduced in the order of a few minutes and the accuracy was high (above 95%). In the end, challenges that SMEs can have for choosing an IDS such as lack of scalability and autonomic self-adaptation, can be solved by using a correct methodology with machine learning techniques.

Lava Volume from Remote Sensing Data: Comparisons with Reverse Petrological Approaches for Two Types of Effusive Eruption

Five effusive eruptions of Piton de la Fournaise (La Réunion) are analyzed to investigate temporal trends of erupted mass and sulfur dioxide (SO2) emissions. Daily SO2 emissions are acquired from three ultraviolet (UV) satellite instruments (the Ozone Monitoring Instrument (OMI), the Ozone Mapping and Profiler Suite (OMPS), and the Tropospheric Monitoring Instrument (TROPOMI)) and an array of ground-based UV spectrometers (Network for Observation of Volcanic and Atmospheric Change (NOVAC)). Time-averaged lava discharge rates (TADRs) are obtained from two automatic satellite-based hot spot detection systems: MIROVA and MODVOLC. Assuming that the lava volumes measured in the field are accurate, the MIROVA system gave the best estimation of erupted volume among the methods investigated. We use a reverse petrological method to constrain pre-eruptive magmatic sulfur contents based on observed SO2 emissions and lava volumes. We also show that a direct petrological approach using SO2 data might be a viable alternative for TADR estimation during cloudy weather that compromises hot spot detection. In several eruptions we observed a terminal increase in TADR and SO2 emissions after initial emission of evolved degassed magma. We ascribe this to input of deeper, volatile-rich magma into the plumbing system towards the end of these eruptions. Furthermore, we find no evidence of volatile excess in the five eruptions studied, which were thus mostly fed by shallow degassed magma.

DEEP LEARNING: NEW APPROACH FOR DETECTING SCHOLAR EXAMS FRAUD

Cheating in exams is a worldwide phenomenon that hinders efforts to assess the skills and growth of students. With scientific and technological progress, it has become possible to develop detection systems in particular a system to monitor the movements and gestures of the candidates during the exam. Individually or collectively. Deep learning (DL) concepts are widely used to investigate image processing and machine learning applications. Our system is based on the advances in artificial intelligence, particularly 3D Convolutional Neural Network (3D CNN), object detector methods, OpenCV and especially Google Tensor Flow, to provides a real-time optimized Computer Vision. The proposal approach, we provide a detection system able to predict fraud during exams. Using the 3D CNN to generate a model from 7,638 selected images and objects detector to identify prohibited things. These experimental studies provide a detection performance with 95% accuracy of correlation between the training and validation data set.

Detection and Recognition of Drones Based on a Deep Convolutional Neural Network Using Visible Imagery

Drones are becoming increasingly popular not only for recreational purposes but also in a variety of applications in engineering, disaster management, logistics, securing airports, and others. In addition to their useful applications, an alarming concern regarding physical infrastructure security, safety, and surveillance at airports has arisen due to the potential of their use in malicious activities. In recent years, there have been many reports of the unauthorized use of various types of drones at airports and the disruption of airline operations. To address this problem, this study proposes a novel deep learning-based method for the efficient detection and recognition of two types of drones and birds. Evaluation of the proposed approach with the prepared image dataset demonstrates better efficiency compared to existing detection systems in the literature. Furthermore, drones are often confused with birds because of their physical and behavioral similarity. The proposed method is not only able to detect the presence or absence of drones in an area but also to recognize and distinguish between two types of drones, as well as distinguish them from birds. The dataset used in this work to train the network consists of 10,000 visible images containing two types of drones as multirotors, helicopters, and also birds. The proposed deep learning method can directly detect and recognize two types of drones and distinguish them from birds with an accuracy of 83%, mAP of 84%, and IoU of 81%. The values of average recall, average accuracy, and average F1-score were also reported as 84%, 83%, and 83%, respectively, in three classes.