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,ar...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 intelli-gence(Al)and,specifically machine learning(ML),have shown great success in fast and accurate detection,classifica-tion,and even analysis of such threats.Accordingly,there is a growing body of literature addressing how subfields of Al/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.展开更多
基金supported in part by NSF Grants#2113945 and#2200538 and a generous financial and technical support from Palo Alto Networks,Inc.
文摘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 intelli-gence(Al)and,specifically machine learning(ML),have shown great success in fast and accurate detection,classifica-tion,and even analysis of such threats.Accordingly,there is a growing body of literature addressing how subfields of Al/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.
