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Abstract The Internet has become a vital part of our daily lives, serving as a hub for global connectivity and a facilitator for seamless communication and information exchange. However, the rise of malicious domains presents a serious challenge, undermining the reliability of the Internet and posing risks to user safety. These malicious activities exploit the Domain Name System (DNS) to deceive users, leading to harmful activities such as spreading drive-by-download malware, operating botnets, creating phishing sites, and sending spam. In response to this growing threat, the application of Machine Learning (ML) techniques has proven to be highly effective. These methods excel in quickly and accurately detecting, classifying, and analyzing such threats. This paper explores the latest developments in using transfer learning for the classification of malicious domains, with a focus on image visualization as a key methodological approach. Our proposed solution has achieved a remarkable testing accuracy rate of 98.67%, demonstrating its effectiveness in detecting and classifying malicious domains. 

Abstract OpenFlow-compliant commodity switches face challenges in efficiently managing flow rules due to the limited capacity of expensive high-speed memories used to store them. The accumulation of inactive flows can disrupt ongoing communication, necessitating an optimized approach to flow rule timeouts. This paper proposes Delayed Dynamic Timeout (DDT), a Reinforcement Learning-based approach to dynamically adjust flow rule timeouts and enhance the utilization of a switch’s flow table(s) for improved efficiency. Despite the dynamic nature of network traffic, our DDT algorithm leverages advancements in Reinforcement Learning algorithms to adapt and achieve flow-specific optimization objectives. The evaluation results demonstrate that DDT outperforms static timeout values in terms of both flow rule match rate and flow rule activity. By continuously adapting to changing network conditions, DDT showcases the potential of Reinforcement Learning algorithms to effectively optimize flow rule management. This research contributes to the advancement of flow rule optimization techniques and highlights the feasibility of applying Reinforcement Learning in the context of SDN. 

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.