Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-border E-commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.

Designing and Evaluating an Automatic Forensic Model for Fast Response of Cross-Border E-Commerce Security Incidents

The rapid development of cross-border e-commerce over the past decade has accelerated the integration of the global economy. At the same time, cross-border e-commerce has increased the prevalence of cybercrime, and the future success of e-commerce depends on enhanced online privacy and security. However, investigating security incidents is time- and cost-intensive as identifying telltale anomalies and the source of attacks requires the use of multiple forensic tools and technologies and security domain knowledge. Prompt responses to cyber-attacks are important to reduce damage and loss and to improve the security of cross-border e-commerce. This article proposes a digital forensic model for first incident responders to identify suspicious system behaviors. A prototype system is developed and evaluated by incident response handlers. The model and system are proven to help reduce time and effort in investigating cyberattacks. The proposed model is expected to enhance security incident handling efficiency for cross-border e-commerce.

Estimation of Position and Size of a Contaminant in Aluminum Casting Using a Thin-Film Magnetic Sensor

Advanced manufacturing processes require an in-line full inspection system. A nondestructive inspection system able to detect a contaminant such as tool chipping was utilized for the purpose of detecting a defective product as well as damaged machine tools used to fabricate the product. In a previous study, a system able to detect magnetic tool steel chipping in conductive material such as aluminum was developed and tested. In this study, a method of position and size estimation for magnetic chipping was investigated and is described. An experimental confirmation of the proposed method was also carried out using an actual prototype system.

Research on Efficacy Evaluation of Large-Scale Networked Intelligent Perception System

Under the smart engineering system (SES), there is a huge demand for evaluating the efficacy of a large-scale networked intelligent perception system (IPS). Considering the large-scale, distributed, and networked system characteristics and perception task demands, this paper proposes a conceptual system for IPS efficacy evaluation and, on this basis, designs the architecture of the efficacy evaluation system. A networked IPS model is constructed based on domain ontology, an index system is quickly established for efficacy evaluation, the evaluation methods are assembled automatically, and adaptive real-time organization strategies are generated for networked perception based on efficacy estimate. After exploring these key technologies, a prototype system is created for the service-oriented integrated efficacy evaluation platform and used to verify and integrate research results. The research provides support for the efficacy evaluation theories and methods of large-scale networked IPS.

A Novel Approach for Fire Safety

Liquids and solvents in industrial products produce flammable vapor which, when mixed with air, can ignite or explode. The ease by which those liquids produce flammable vapors depends on their flashpoints which allow them to be categorized according to the fire hazard they exhibit in their normal use. In this chapter, a novel approach for the classification of liquids is proposed. The proposed approach relies on the use of a vector network analyzer, a patch antenna, and a group of classifiers. In this study, random forest and REPTree algorithms are preferred as classifers. As proven in the study, random forest algorithm can provide higher accuracy than REPTree algorithm in the classification of hazardous liquids. A prototype system is currently under development in order to integrate the components of the proposed approach into a single unit. It is expected that the prototype system will quickly and reliable make a non-contact classification of liquids in different kinds of bottles.

Anonymization of Network Traces Data through Condensation-based Differential Privacy

Network traces are considered a primary source of information to researchers, who use them to investigate research problems such as identifying user behavior, analyzing network hierarchy, maintaining network security, classifying packet flows, and much more. However, most organizations are reluctant to share their data with a third party or the public due to privacy concerns. Therefore, data anonymization prior to sharing becomes a convenient solution to both organizations and researchers. Although several anonymization algorithms are available, few of them allow sufficient privacy (organization need), acceptable data utility (researcher need), and efficient data analysis at the same time. This article introduces a condensation-based differential privacy anonymization approach that achieves an improved tradeoff between privacy and utility compared to existing techniques and produces anonymized network trace data that can be shared publicly without lowering its utility value. Our solution also does not incur extra computation overhead for the data analyzer. A prototype system has been implemented, and experiments have shown that the proposed approach preserves privacy and allows data analysis without revealing the original data even when injection attacks are launched against it. When anonymized datasets are given as input to graph-based intrusion detection techniques, they yield almost identical intrusion detection rates as the original datasets with only a negligible impact.

Sistem Pendistribusian Air Bersih Metode Prabayar Terkendali Mikrokontroler Berbasis IoT

A number of problems sometimes often arise regarding the flow of clean water from Regional Drinking Water Companies (PDAMs) to customers, such as the flow of water stops suddenly or there is no water at all, so it is necessary to manufacture a prototype system for monitoring the distribution of clean water with a microcontroller-controlled prepaid method. IoT based. The distribution of PDAM water that is channeled to consumers can be monitored online through the Internet network. The objectives of this research are (a) to make a prototype (prototype) of a prepaid clean water distribution system controlled by a microcontroller based on IoT, (b) to program an Arduino IDE-assisted system, and (c) to measure system performance. The research method starts from making a prototype physical form of clean water distribution assisted by a microcontroller, programming the microcontroller and Wi-Fi module, and measuring system performance. The results of measuring system performance are indicated by an error in the ultrasonic sensor reading HC-SR04 that occurs when the water level is low and too high with a maximum measured water level of 95%. The error when measuring the waterflow sensor at the water level is lower than 49% which is influenced by the water speed from the low pressure pump when the water level is below that value. The accuracy level of the waterflow sensor is 96.96% which is based on the sensor measurement results which are compared to the measurement results with a measuring cup. The system can monitor data readings from the waterflow sensor by using the NodeMCU ESP8266 on a web server from Thinkspeak via the smartphone screen. Overall the tool can function well

Three-Phase Induction Motor Speed Estimation Using Recurrent Neural Network Structure

In induction motor speed control method, the development of the field-oriented control (FOC) algorithm which can control torque and flux separately enables the motor to replace many roles of DC motors. Induction motor speed control can be done by using a close loop system which requires a speed sensor. Referring to the speed sensor weaknesses such as less accurate of the measurement, this is due to the placement of the sensor system that is too far from the control system. Therefore, a speed sensorless method was developed which has various advantages. In this study, the speed sensorless method using an artificial neural network with recurrent neural network (RNN) as speed observer on three-phase induction motor has been discussed. The RNN can maintain steady-state conditions against a well-defined set point speed, so that the observer is able and will be suitable if applied as input control for the motor drives. In this work, the RNN has successfully estimated the rotor flux of the induction motor in MATLAB R2019a simulation as about 0.0004Wb. As based on speed estimation error, the estimator used has produced at about 26.77%, 8.7% and 6.1% for 150rad/s, 200rad/s and 250rad/s respectively. The future work can be developed and improved by creating a prototype system of the induction motor to get more accurate results in real-time of the proposed RNN observer.

Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

This paper presents an estimation method for a sound source of pre-recorded mallard calls from acoustic information using two microphone arrays combined with delay-and-sum beamforming. Rice farming using mallards saves labor because mallards work instead of farmers. Nevertheless, the number of mallards declines when they are preyed upon by natural enemies such as crows, kites, and weasels. We consider that efficient management can be achieved by locating and identifying the locations of mallards and their natural enemies using acoustic information that can be widely sensed in a paddy field. For this study, we developed a prototype system that comprises two sets of microphone arrays. We used 64 microphones in all installed on our originally designed and assembled sensor mounts. We obtained three acoustic datasets in an outdoor environment for our benchmark evaluation. The experimentally obtained results demonstrated that the proposed system provides adequate accuracy for application to rice–duck farming.

Design of a Step-Up DC–DC Converter for Standalone Photovoltaic Systems with Battery Energy Storages

This paper proposes a step-up DC–DC converter for a power electronic circuit for standalone photovoltaic systems with battery energy storages. The proposed DC–DC converter effectively converts low DC battery voltage into high DC-link voltage. It operates with soft-switching characteristics, which can reduce switching power losses. The proposed converter operates without output voltage feedback, which simplifies its control design. The operation principle of the proposed converter was described, along with the overall system configuration. The experimental results were discussed for the 500-W prototype system using a 12-V lead-acid battery.