Nowadays, cyber hate speech is increasingly growing, which forms a serious problem worldwide by threatening the cohesion of civil societies. Hate speech relates to using expressions or phrases that are violent, offensive or insulting for a person or a minority of people. In particular, in the Arab region, the number of Arab social media users is growing rapidly, which is accompanied with high increasing rate of cyber hate speech. This drew our attention to aspire healthy online environments that are free of hatred and discrimination. Therefore, this article aims to detect cyber hate speech based on Arabic context over Twitter platform, by applying Natural Language Processing (NLP) techniques, and machine learning methods. The article considers a set of tweets related to racism, journalism, sports orientation, terrorism and Islam. Several types of features and emotions are extracted and arranged in 15 different combinations of data. The processed dataset is experimented using Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF), in which RF with the feature set of Term Frequency-Inverse Document Frequency (TF-IDF) and profile-related features achieves the best results. Furthermore, a feature importance analysis is conducted based on RF classifier in order to quantify the predictive ability of features in regard to the hate class.

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Detecting and classifying new malicious network traffic is a high priority concern for cybersecurity practitioners. New stealth or zero-day attack can make companies go out of businesses in the digital transformation era. Despite the plethora of studies that have explored different machine-learning (ML) techniques to address this issue, the most popular used approach remains traditional ML with legacy datasets and small campus network. The difficulty in data collection considers the biggest impediment of using ML. This paper examines the possibility of exposing zero-day malicious network traffic in large campus networks based on cloud environments by presenting a lightweight framework. An experiment was devised for the analysis. However, before that, the characteristics of the network were examined based on the flow level. The framework showed an outperformed accuracy rate of 100% for a specific type of attack and 97.97% as a comprehensive detection mechanism.