IoT-based Dual Technology Motion Detector

Home security has become a prime concern in recent years. As technology is emerging every second, abundant IoT-based smart surveillance systems have been developed and implemented with many modern features to keep the home safe. The proposed project is an extended approach to the existing home security system. The project aims to reduce false alarm rates of the existing home security system by using a Dual Technology motion sensor. Dual-technology sensors are sensors that combine two different types of technology. The dual-technology sensor uses both PIR and ultrasonic sensors, activating the trigger only when both sensors detect any motion of an object. The proposed setup effectively reduces false alarms caused by inanimate objects. The trigger caused by the Dual Technology sensor sends a message alert to the specified phone number through the Twilio Programmable Messaging API. Ultrasonic technology is sensitive to motion toward and away from the detector; passive infrared technology (PIR) is sensitive to motion across the field of view. The future implications of the project are great as it can also be used in the Automation of lighting in homes, offices, and libraries

Viable Detection of URL Phishing using Machine Learning Approach

The objective of paper is to detect phishing URLs using machine learning algorithms. Phishing is a fraudulent activity that involves tricking users into giving away sensitive information, such as passwords and credit card numbers, by impersonating legitimate websites. The main objective of this work is to build a model that can accurately detect viable phishing URLs and classify them as either legitimate or fraudulent. This will help to prevent users from falling victim to phishing attacks and protect their personal information. The model will be trained on a large dataset of annotated URLs and will be optimised for high accuracy and low false positive rates. The paper consists of two datasets in which one of the dataset consists of phishing URLs and other datasets consist of features of URLs. The performance of the phishing detection model will be evaluated using various metrics, such as precision, recall, and F1 score. We will also conduct an in-depth analysis of the results and discuss the effectiveness of the approach. This work aims to build a robust model for phishing URL detection using machine learning algorithms. Future enhancements to this work could include incorporating more advanced feature extraction techniques, exploring the use of deep learning models, and expanding the dataset to include more diverse types of URLs