Intelligent Phishing Detection Scheme Using Deep Learning Algorithms

Purpose: Phishing attacks have evolved in recent years due to high-tech-enabled economic growth worldwide. The rise in all types of fraud loss in 2019 has been attributed to the increase in deception scams and impersonation, as well as to sophisticated online attacks such as phishing. The global impact of phishing attacks will continue to intensify, and thus, a more efficient phishing detection method is required to protect online user activities. To address this need, this study focussed on the design and development of a deep learning-based phishing detection solution that leveraged the universal resource locator and website content such as images, text and frames. Design/methodology/approach: Deep learning techniques are efficient for natural language and image classification. In this study, the convolutional neural network (CNN) and the long short-term memory (LSTM) algorithm were used to build a hybrid classification model named the intelligent phishing detection system (IPDS). To build the proposed model, the CNN and LSTM classifier were trained by using 1m universal resource locators and over 10,000 images. Then, the sensitivity of the proposed model was determined by considering various factors such as the type of feature, number of misclassifications and split issues. Findings: An extensive experimental analysis was conducted to evaluate and compare the effectiveness of the IPDS in detecting phishing web pages and phishing attacks when applied to large data sets. The results showed that the model achieved an accuracy rate of 93.28% and an average detection time of 25 s. Originality/value: The hybrid approach using deep learning algorithm of both the CNN and LSTM methods was used in this research work. On the one hand, the combination of both CNN and LSTM was used to resolve the problem of a large data set and higher classifier prediction performance. Hence, combining the two methods leads to a better result with less training time for LSTM and CNN architecture, while using the image, frame and text features as a hybrid for our model detection. The hybrid features and IPDS classifier for phishing detection were the novelty of this study to the best of the authors' knowledge

Differential game model and coordination model for green supply chain based on green technology research and development

The purpose of this paper is to establish a green supply chain differential game model for green technology research and development based on a secondary green supply chain composed of a single manufacturer and a single retailer. It compares the differential game equilibrium solutions under centralized and decentralized decision-making. The green supply chain members are coordinated through the dynamic wholesale price mechanism, and numerical simulation is used as a methodology, to verify and explain the results. The study found that compared to decentralized decision-making, the level of green technology and the total profit of green channels are higher under centralized decision-making. When the coordination parameters are within a certain range, the dynamic wholesale price mechanism can coordinate the behavior of manufacturers and retailers. The result also discovers that under the dynamic wholesale price mechanism, with the increase of investment cost coefficient, or the increase of price sensitivity or the decrease of consumer's environmental awareness, the green technology level, product green degree, price, retailer's profit, and the total profit of green channel is decreased. In contrast, the wholesale price and manufacturer's profits are increased

Study of Fuzzy Controller to control vertical position of an air-cushion tracked vehicle

This paper presents the fuzzy logic control system of an air-cushion tracked vehicle (ACTV) operating on swamp peat terrain. Vehicle vertical position is maintained by using an inflated air-cushion system attached with the vehicle. It is desired that the vehicle vertical position be maintained at a desired position so that vehicle obtains sufficient traction control and to propel the driving system. To accomplish this task, it is required that the error between the actual position and the desired position equal to zero, and the differential position rate also be equal to zero. Therefore, the main purpose of this study is to develop an appropriate control strategy for an air-cushion system by using fuzzy logic expert system. Air-cushion system is controlled by the electronic proportional control valve and fuzzy logic controller (FLC) with associating the output signal of the distance (height) measuring sensor attached with the vehicle. In this control scheme the fundamental goal is to employ the fuzzy logic expert system to set the fuzzy rules and to actuate the electronic proportional valve in order to obtain appropriate valve control actions. Experimental values are taken in the laboratory for control system testing to investigate the relationship between vehicle vertical position and air-cushion system

Hybrid electrical air-cushion tracked vehicle for swamp peat in Malaysia

The aim of this paper is to present a hybrid electrical air-cushion tracked vehicle (HETAV) for the operation on swamp peat. Mathematical models are incorporated with accounting kinematics and dynamics behaviors of the vehicle. Sinkage of the HETAV is sensed by an ultrasonic displacement (UD) sensor, in order to operate the air-cushion system. The air-cushion of HETAV is protected with a novel-design auto-adjusting supporting (AAS) system. A propeller is equipped with the vehicle to develop additional thrust for overcoming the dragging motion resistance of the air-cushion system . The performance of the HETAV is defined by traction and motion resistance. The mean value of traction for the swamp terrain with propeller over without propeller increases 10.21% and 6.47% for the vehicle weight of 1.02 kN and 2.04 kN, respectively. Similarly, it was found that the mean values of vehicle’s motion resistance decrease 12.63% and 25.81% for the vehicle weight of 2.45 kN and 3.43 kN, respectively

Study on the development of a fuzzy logic control electromagnetic actuated CVT system

Electromagnetic actuated continuously variable transmission (EMA-CVT) system consists of two pairs of electromagnetic actuators (solenoid) attached with primary and secondary pulley in order to develop the attraction and repulsive forces. Kinematics of EMA is established for electromagnetic actuation and clamping force. This study also focused on fuzzy logic based controller (FLC) to precisely control for pushing and pulling the sheaves. The EMA-CVT performance with controller is 28% more than that of without controller. The solenoids of the EMA were activated by varying the current supply with the (FPID) to maintain the non-linearity of the EMA in response of the vehicle traction torque demand. Result shows that 12.5 amp and 14.00 amp current supply is needed for pulling and pushing respectively. The acceleration time of the 1/4 scale car has been recorded as 5.5 s with the response of drive wheels torque

Study on the development of a fuzzy logic control electromagnetic actuated CVT system

This paper conducts the preliminary research of an Electromagnetic Actuated Continuously Variable Transmission (EMA-CVT) system of quarter scale. An EMA-CVT system is consisted of two pairs of electromagnetic actuators (solenoid)attached with primary and secondary pulley in order to develop the attraction and repulsive forces. The relationships between the speed ratio and electromagnetic actuation and clamping force and output torque of the CVT are established based on the kinematics of the EMA-CVT system. This study also focused on fuzzy logic based controller (FLC) to precise control for pushing and pulling the sheaves based on the feedback of the RPM sensor and slope sensor. The EMA-CVT performance with controller is 28% more than that of the EMA-CVT without controller. The solenoids of the EMA were activated by varying the current supply with the Fuzzy-Proportional-Derivative-Integrator (FPID)to maintain the non-linearity of the CVT in response of the vehicle traction torque demand. Result shows that the solenoid able to pull the plunger in the desired distance with supply current of 12.5 amp while push the plunger to the desired distance with 14.00 amp current supply to the windings when the vehicle is considered in 10% grad. The acceleration time of the ¼ scale car has been recorded as 5.5 s with the response of drive wheels torque

Tractive performance prediction for intelligent air-cushion track vehicle: fuzzy logic approach

Fuzzy logic approach is used in this study to predict the tractive performance in terms of traction force, and motion resistance for an intelligent air cushion track vehicle while it operates in the swamp peat. The system is effective to control the intelligent air –cushion system with measuring the vehicle traction force (TF), motion resistance (MR), cushion clearance height (CH) and cushion pressure (CP). Sinkage measuring sensor, magnetic switch, pressure sensor, micro controller, control valves and battery are incorporated with the Fuzzy logic system (FLS) to investigate experimentally the TF, MR, CH, and CP. In this study, a comparison for tractive performance of an intelligent air cushion track vehicle has been performed with the results obtained from the predicted values of FLS and experimental actual values. The mean relative error of actual and predicted values from the FLS model on traction force, and total motion resistance are found as 5.58 %, and 6.78 % respectively. For all parameters, the relative error of predicted values are found to be less than the acceptable limits. The goodness of fit of the prediction values from the FLS model on TF, and MR are found as 0.90, and 0.98 respectively

Intelligent air-cushion tracked vehicle performance investigation: neural-networks

The Intelligent Air-Cushion Tracked Vehicle (IACTV) is given focus as an alternative to conventional off-road vehicles, which are driven by track and air-cushion systems. To make the IACTV as effi cient as possible, proper investigation of the vehicle’s performance is essential. The most relevant factors that affect the competitive effi ciency of the (ACTV) are the Tractive Effort (TE), Motion Resistance (MR) and Power Consumption (PC). Therefore, an Artifi cial Neural-Network (ANN) model is proposed to investigate the vehicle’s performance. Cushion Clearance Height (CH), and Air-Cushion Pressure (CP)are used at the input layers, while PC, TE and MR are used at the output layers. Experiments are carried out in the fi eld to investigate the vehicle’s performance, and the fi ndings are compared with the results obtained from ANN

Exploring knowledge and practices regarding menstrual hygiene management among Bihari women in the Geneva Camp in Bangladesh

Background: Research into menstrual hygiene management (MHM) has been mainly based on menstruation-related knowledge and practices of women and girls in the mainstream Bangladeshi society; socially disadvantaged groups, such as the Bihari refugee women, have largely been ignored. Purpose: This study aims to assess knowledge and practices about MHM among Bihari women in the Mohammadpur Geneva Camp in Dhaka, Bangladesh. Methods: In 2017, a cross-sectional survey was conducted among Bihari women and girls by the trained interviewers using a structured questionnaire. The purposive sampling was applied to select 160 Bihari women aged between 15 and 49. Data were entered, cleaned, and analysed using SPSS software. Both univariate and bivariate analyses were undertaken to examine knowledge and MHM-related practices with a significance level of p<0.01. Results: Overall, most women (59.4%) had low knowledge about menstruation. More than one-quarter (27.0%) used disposable sanitary napkins. The Bihari women who did not use sanitary pads (73%) reported that they used old disposable clothes (59.83%), reusable cloths (25.64%), cotton (9.40%), or toilet tissue paper (4.27%). Around two-thirds of the women (68.0%) performed special baths and 36.9% followed socio-cultural taboos during menstruation. The bivariate analyses revealed that higher menstruation knowledge was associated with higher use of disposable sanitary napkins (low knowledge: 18.9%, high knowledge: 38.5%; p<0.01). Conclusions: The findings suggest that it is imperative for Bihari women to have adequate and appropriate menstruation knowledge so that they can maintain good menstrual hygiene practices. The findings highlight challenges experienced by the refugee women in maintaining MHM and can be used to improve women’s reproductive health and well-being and reduce the risk of reproductive tract infections (RTI) among socially disadvantaged women