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Abstract In today’s interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software. 

Cyberbullying is a well-known social issue, and it is escalating day by day. Due to the vigorous development of the internet, social media provide many different ways for the user to express their opinions and exchange information. Cyberbullying occurs on social media using text messages, comments, sharing images and GIFs or stickers, and audio and video. Much research has been done to detect cyberbullying on textual data; some are available for images. Very few studies are available to detect cyberbullying on GIFs/stickers. We collect a GIF dataset from Twitter and Applied a deep learning model to detect cyberbullying from the dataset. Firstly, we extracted hashtags related to cyberbullying using Twitter. We used these hashtags to download GIF file using publicly available API GIPHY. We collected over 4100 GIFs including cyberbullying and non-cyberbullying. we applied deep learning pre-trained model VGG16 for the detection of the cyberbullying. The deep learning model achieved the accuracy of 97%. Our work provides the GIF dataset for researchers working in this area.