Phishing websites continue to pose a serious cybersecurity threat by deceiving users into revealing sensitive information such as login credentials, banking details, and personal data. Traditional blacklist-based detection techniques are ineffective against newly created phishing websites, necessitating intelligent machine learning solutions. This paper presents PhishShield, a hybrid phishing website detection framework that integrates Support Vector Machine (SVM) and Light Gradient Boosting Machine (LightGBM) to accurately classify legitimate and phishing websites. The proposed approach utilizes URL-based feature extraction and text preprocessing to generate meaningful representations for classification. SVM provides robust decision boundaries, while LightGBM enhances predictive performance through efficient gradient boosting. Experimental evaluation demonstrates that the hybrid framework achieves higher accuracy, precision, recall, and F1-score compared to conventional machine learning models. The system is implemented as a web-based application capable of real-time URL analysis, enabling users to identify malicious websites before accessing them. The proposed framework offers an efficient, scalable, and reliable solution for strengthening web security against evolving phishing attacks.
Srija Pasupunuti, Sk.Mahammadunnisa· American Journal of AI Cyber...· 0 citations
Weapon-related threats in public places demand intelligent surveillance systems capable of detecting dangerous objects accurately and in real time. This paper presents DeepGuard, an intelligent deep learning framework for automated weapon detection in images and surveillance videos using Faster Region-Based Convolutional Neural Network (Faster R-CNN) and Single Shot Detector (SSD). The proposed framework employs annotated weapon datasets for model training and utilizes convolutional neural networks to identify and localize weapons with bounding boxes. A comparative evaluation of SSD and Faster RCNN is conducted to analyze their detection accuracy and inference speed. Experimental results demonstrate that Faster R-CNN achieves superior detection accuracy, whereas SSD provides faster processing suitable for real-time applications. The developed system effectively identifies weapons in diverse surveillance environments, enhancing public safety through early threat detection and continuous monitoring. The proposed framework offers a reliable, scalable, and intelligent solution for smart surveillance systems, making it suitable for deployment in airports, railway stations, educational institutions, commercial buildings, and other high-security environments.
Chengoli prashanth, Sk.Mahammadunnisa· American Journal of AI Cyber...· 0 citations
Recent advancements in deep learning have enabled the generation of realistic images directly from natural language descriptions. This paper presents a semantic-aware framework for text-to-face image synthesis using a joint Bidirectional Long Short-Term Memory (BiLSTM) network and Generative Adversarial Network (GAN). The proposed approach simultaneously trains the text encoder and image generator, allowing effective learning of semantic relationships between textual attributes and facial features. Initially, input descriptions are transformed into meaningful vector representations using Bi-LSTM, which are then utilized by the GAN to synthesize high-quality facial images. Unlike conventional methods that rely on separately trained text encoders, the proposed end-to-end architecture improves semantic consistency and visual realism. The model is trained on the CelebA dataset with corresponding facial descriptions and evaluated using similarity and image quality measures. Experimental results demonstrate improved face generation accuracy and better preservation of facial attributes, making the framework suitable for applications in forensic investigations, digital character creation, intelligent human-computer interaction, and public safety systems.
Heena Anjum, Sk.Mahammadunnisa· American Journal of AI Cyber...· 0 citations