Abdu Gumaei

Malicious websites detection is one of the cyber-security tasks that protects sensitive information such as credit card details and login credentials from attackers. Machine learning (ML)-based methods have been commonly used in several applications of cyber-security research. Although there are some methods and approaches proposed in the state-of-the-art studies, the advancement of the most effective solution is still of research interest and needs to be improved. Recently, decision fusion methods play an important role in improving the accuracy of ML methods. They are broadly classified based on the type of fusion into a voting decision fusion technique and a divide and conquer decision fusion technique. In this paper, a decision fusion ensemble learning (DFEL) model is proposed based on voting technique for detecting malicious websites. It combines the predictions of three effective ensemble classifiers, namely, gradient boosting (GB) classifier, extreme gradient boosting (XGB) classifier, and random forest (RF) classifier. We use these classifiers because their advantages to perform well for class imbalanced and data with statistical noises such as in the case of malicious websites detection. A weighted majority-voting rule is utilized for generating the final decisions of used classifiers. The experimental results are conducted on a publicly available large dataset of malicious and benign websites. The comparative study exposed that the DFEL model achieves high accuracies, which are 97.25% on average of 10-fold cross-validation test and 98.50% on a holdout of 30% test set. This confirms the ability of proposed approach to improve the detection rate of malicious websites.

A speech impairment limits a person’s capacity for oral and auditory communication. A great improvement in communication between the deaf and the general public would be represented by a real-time sign language detector. This work proposes a deep learning-based algorithm that can identify words from a person’s gestures and detect them. There have been many studies on this topic, but the development of static and dynamic sign language recognition models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers’ speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. For the accurate and effective recognition of signs, this study uses two different deep learning-based approaches. We create a real-time American Sign Language detector using the skeleton model, which reliably categorizes continuous signs in sign language in most cases using a deep learning approach. In the second deep learning approach, we create a sign language detector for static signs using YOLOv6. This application is very helpful for sign language users and learners to practice sign language in real time. After training both algorithms separately for static and continuous signs, we create a single algorithm using a hybrid approach. The proposed model, consisting of LSTM with MediaPipe holistic landmarks, achieves around 92% accuracy for different continuous signs, and the YOLOv6 model achieves 96% accuracy over different static signs. Throughout this study, we determine which approach is best for sequential movement detection and for the classification of different signs according to sign language and shows remarkable accuracy in real time.