Abstract— Phishing website detection using machine learning focuses on the design and implementation of an intelligent system for detecting malicious URLs using machine learning techniques. The system aims to classify URLs as either legitimate or malicious by analyzing various structural and statistical features extracted from the URLs. A dataset containing both benign and malicious URLs is used to train and evaluate the model. The proposed approach utilizes a Gradient Boosting Classifier due to its high accuracy and ability to handle complex patterns in data. Feature extraction plays a crucial role in the project, where attributes such as URL length, presence of special characters, domain age, use of HTTPS, and abnormal patterns are considered. These features are fed into the model, which learns to differentiate between safe and harmful URLs. The project involves several stages, including data collection, preprocessing, feature extraction, model training, and performance evaluation. Multiple machine learning algorithms such as Support Vector Machine (SVM), Decision Tree, Random Forest, and XG Boost are also explored and compared to identify the most effective model .The system is designed to work in real time, allowing users to input URLs and receive instant predictions regarding their safety.
Keywords— Phishing Website Detection, Machine Learning, Gradient Boosting Classifier, URL Feature Extraction, Cybersecurity, Malicious URL Detection, Web Security, Classification, Feature Engineering, Real-Time Detection.
V. B, D. K. S. Rao, Dr. Gattu Prasad· International Scientific Jou...· 0 citations
Abstract— This research outlines the cloud data security when using machine learning techniques – Random Forest, Deep Neural Networks, and Q-Learning to prevent unauthorized data transfers and leaks. The major findings point to the fact that DNN showed a higher level of prediction capabilities in comparison with Random Forest – 95% of overall accuracy as opposed to 92%. It is particularly important to consider AUC-ROC of Random Forest, which is 0.96, making it the most reliable model. However, Q-Learning appears to be less accurate with 88% yet more effective when it comes to a cumulative reward and a policy optimization – features that are vital for a changing environment of cloud servers. The findings of the research make a significant contribution to the field of cloud data security, showing the effectiveness of advanced machine learning models in terms of identifying and mitigating security breaches. This, in turn, creates the opportunity of implementation of these techniques into security frameworks for enhancing the resilience and efficiency of the latter. The recommendations for further research lie in the area of hybrid models creation, in particular, the models that would be able to utilize the positive sides of all three techniques. Moreover, the results would be more generalized with a significantly larger dataset comprising diverse cloud environments and threat situations. Finally, the investigation of the models described in a real-time setting and large cloud-based
systems may be suggested for further research, given that these characteristics are essential for an effective practical deployment helping resist emerging threats.
Keywords- Cloud Data Security, Machine Learning, Random Forest, Deep Neural Networks, Q-Learning
Bharda Priya Dutt, M. Prasad, D. K. S. Rao· International Scientific Jou...· 0 citations
ABSTRACT
Firefighting is an inherently hazardous occupation, with numerous incidents each year resulting in injuries and fatalities among fire service personnel. To mitigate these risks and enhance operational efficiency, this paper presents the design and development of an IoT-Based Firefighting Robot that integrates autonomous fire detection and suppression capabilities with remote control via Bluetooth communication.
The system leverages Internet of Things (IoT) technologies for real-time monitoring and control, enabling the robot to navigate hazardous environments and extinguish fires autonomously or under manual operator control when required. Equipped with flame sensors for fire detection, a fire extinguishing mechanism, and Bluetooth-based communication, the robot can be operated from a safe distance using an Android application, allowing precise intervention in critical situations.
This hybrid approach combines the strengths of autonomous decisionmaking with human supervision, offering enhanced flexibility and safety in firefighting operations. The system is particularly suited for industrial settings and environments where the risk of accidental fires is high, enabling rapid response while minimizing human exposure to danger. The proposed solution demonstrates the effectiveness of IoT-enabled robotics in improving fire safety, offering a scalable and reliable approach to safeguarding lives, property, and critical infrastructure.
Keywords: DC motor, Flame sensors, Firefighting, Pump, Robot.
G. Sahithi, D. K. S. Rao· International Scientific Jou...· 0 citations