Parameter optimization for intelligent phishing detection using Adaptive Neuro-Fuzzy

Phishing attacks has been growing rapidly in the past few years. As a result, a number of approaches have been proposed to address the problem. Despite various approaches proposed such as feature-based and blacklist-based via machine learning techniques, there is still a lack of accuracy and real-time solution. Most approaches applying machine learning techniques requires that parameters are tuned to solve a problem, but parameters are difficult to tune to a desirable output. This study presents a parameter tuning framework, using adaptive Neuron-fuzzy inference system with comprehensive data to maximize systems performance. Extensive experiment was conducted. During ten-fold cross-validation, the data is split into training and testing pairs and parameters are set according to desirable output and have achieved 98.74% accuracy. Our results demonstrated higher performance compared to other results in the field. This paper contributes new comprehensive data, novel parameter tuning method and applied a new algorithm in a new field. The implication is that adaptive neuron-fuzzy system with effective data and proper parameter tuning can enhance system performance. The outcome will provide a new knowledge in the field

Intelligent phishing detection parameter framework for E-banking transactions based on Neuro-fuzzy

Phishing attacks have become more sophisticated in web-based transactions. As a result, various solutions have been developed to tackle the problem. Such solutions including feature-based and blacklist-based approaches applying machine learning algorithms. However, there is still a lack of accuracy and real-time solution. Most machine learning algorithms are parameter driven, but the parameters are difficult to tune to a desirable output. In line with Jiang and Ma’s findings, this study presents a parameter tuning framework, using Neuron-fuzzy system with comprehensive features in order to maximize systems performance. The neuron-fuzzy system was chosen because it has ability to generate fuzzy rules by given features and to learn new features. Extensive experiments were conducted, using different feature-sets, two cross-validation methods, a hybrid method and different parameters and achieved 98.4% accuracy. Our results demonstrated a high performance compared to other results in the field. As a contribution, we introduced a novel parameter tuning framework based on a neuron-fuzzy with six feature-sets and identified different numbers of membership functions different number of epochs, different sizes of feature-sets on a single platform. Parameter tuning based on neuron-fuzzy system with comprehensive features can enhance system performance in real-time. The outcome will provide guidance to the researchers who are using similar techniques in the field. It will decrease difficulties and increase confidence in the process of tuning parameters on a given problem

Automatic Field Monitoring and Detection of Plant Diseases Using IoT

This research presents a GSM-based system for automatic plant disease diagnosis and describes its use in the creation of ACPS. Traditional farming methods were largely ineffective against microbial diseases. In addition, farmers can't keep up with the ever-changing nature of infections, so a reliable disease forecasting system is essential. To circumvent this, we employ a Convolutional Neural Network (CNN) model that has been trained to examine the crop image recorded by a health maintenance system. The solar sensor node is in charge of taking pictures, sensing continuously, and automating smartly. An agricultural robot is sometimes known as an agribot or agbot. An autonomous robot with agricultural applications. It helps the farmer improve crop productivity while decreasing the need for manual labour. In the future, these agricultural robots could replace human labour in a variety of farming tasks, including tilling, planting, and harvesting. These agricultural robots will manage pests and diseases as well as perform tasks like weeding. In order to keep an eye on the crops and streamline the irrigation process, this system is equipped with disease prediction technology for plants and intelligent irrigation controls. The energy required to provide disease prediction and irrigation systems separately is reduced by combining them in this project