Addressing cyberbullying in Urdu tweets: a comprehensive dataset and detection system.

The prevalence of cyberbullying has reached an alarming rate, affecting approximately 54% of teenagers who experience various forms of cyberbullying, including offensive hate speech, threats, and racism. This research introduces a comprehensive dataset and system for cyberbullying detection in Urdu tweets, leveraging a spectrum of machine learning approaches including traditional models and advanced deep learning techniques. The objectives of this study are threefold.

Windows malware detection based on static analysis with multiple features.

Malware or malicious software is an intrusive software that infects or performs harmful activities on a computer under attack. Malware has been a threat to individuals and organizations since the dawn of computers and the research community has been struggling to develop efficient methods to detect malware. In this work, we present a static malware detection system to detect Portable Executable (PE) malware in Windows environment and classify them as benign or malware with high accuracy.